From 8d7d8d252b7dcb6d0e82bd388026445e863b3ae5 Mon Sep 17 00:00:00 2001 From: Rasool Saghaleyni Date: Tue, 8 Oct 2024 11:18:10 +0200 Subject: [PATCH] Update toplogy lab --- session_topology/Dockerfile | 2 - ...cal_Process_2018.human.enrichr.reports.png | Bin 302818 -> 0 bytes ...cal_Process_2018.human.enrichr.reports.txt | 1906 - .../KEGG_2019_Human.human.enrichr.reports.png | Bin 172077 -> 0 bytes .../KEGG_2019_Human.human.enrichr.reports.txt | 161 - .../OMIM_Disease.human.enrichr.reports.png | Bin 63641 -> 0 bytes .../OMIM_Disease.human.enrichr.reports.txt | 27 - .../lab/assests/Enrichr/gseapy.enrichr..log | 1959 - .../lab/{ => lectures}/1Introduction.key | Bin .../lab/{ => lectures}/1Introduction.pdf | Bin .../lab/{ => lectures}/2Network_inference.key | Bin .../lab/{ => lectures}/2Network_inference.pdf | Bin .../{ => lectures}/3Community_analysis.key | Bin .../{ => lectures}/3Community_analysis.pdf | Bin .../Application_net_analysis.pdf | Bin .../lab/{ => lectures}/lecture_short.key | Bin .../lab/{ => lectures}/lecture_short.pdf | Bin session_topology/lab/topology_lab.ipynb | 1702 - session_topology/lab/topology_lab_part1.ipynb | 2194 ++ session_topology/lab/topology_lab_part2.ipynb | 32681 ++++++++++++++++ session_topology/lab/topology_lab_part3.ipynb | 1007 + session_topology/topology_env.yml | 36 +- 22 files changed, 35912 insertions(+), 5763 deletions(-) delete mode 100644 session_topology/lab/assests/Enrichr/GO_Biological_Process_2018.human.enrichr.reports.png delete mode 100644 session_topology/lab/assests/Enrichr/GO_Biological_Process_2018.human.enrichr.reports.txt delete mode 100644 session_topology/lab/assests/Enrichr/KEGG_2019_Human.human.enrichr.reports.png delete mode 100644 session_topology/lab/assests/Enrichr/KEGG_2019_Human.human.enrichr.reports.txt delete mode 100644 session_topology/lab/assests/Enrichr/OMIM_Disease.human.enrichr.reports.png delete mode 100644 session_topology/lab/assests/Enrichr/OMIM_Disease.human.enrichr.reports.txt delete mode 100644 session_topology/lab/assests/Enrichr/gseapy.enrichr..log rename session_topology/lab/{ => lectures}/1Introduction.key (100%) rename session_topology/lab/{ => lectures}/1Introduction.pdf (100%) rename session_topology/lab/{ => lectures}/2Network_inference.key (100%) rename session_topology/lab/{ => lectures}/2Network_inference.pdf (100%) rename session_topology/lab/{ => lectures}/3Community_analysis.key (100%) rename session_topology/lab/{ => lectures}/3Community_analysis.pdf (100%) rename session_topology/lab/{ => lectures}/Application_net_analysis.pdf (100%) rename session_topology/lab/{ => lectures}/lecture_short.key (100%) mode change 100755 => 100644 rename session_topology/lab/{ => lectures}/lecture_short.pdf (100%) delete mode 100644 session_topology/lab/topology_lab.ipynb create mode 100644 session_topology/lab/topology_lab_part1.ipynb create mode 100644 session_topology/lab/topology_lab_part2.ipynb create mode 100644 session_topology/lab/topology_lab_part3.ipynb diff --git a/session_topology/Dockerfile b/session_topology/Dockerfile index 84970fc6..7dc64f25 100644 --- a/session_topology/Dockerfile +++ b/session_topology/Dockerfile @@ -33,8 +33,6 @@ RUN /opt/conda/envs/topology/bin/pip install ipykernel && \ # Fix permissions and make the script executable RUN chown -R jovyan:users /opt/conda /home/jovyan/.cache /home/jovyan/lab && \ chmod -R a+rwx /home/jovyan/lab && \ - find /home/jovyan/lab -type d -exec chmod a+rwx {} \; && \ - find /home/jovyan/lab -type f -exec chmod a+rw {} \; && \ chmod +x /usr/local/bin/start-script.sh # Switch back to the non-root user diff --git a/session_topology/lab/assests/Enrichr/GO_Biological_Process_2018.human.enrichr.reports.png b/session_topology/lab/assests/Enrichr/GO_Biological_Process_2018.human.enrichr.reports.png deleted file mode 100644 index f2e9bcb2014aa2b2d88fdede03118c643b9ac69b..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 302818 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z!~Wl~Z~mtbZy1y>e<%CXhFS3C*!2w&{>vec?>3D7mt#8)ew`V9ITU|#LvH-#*s)vx lcX3|^;=jNBFO8g7>8x_!?-9la!qzVZK4bk;`6-Wo{wJwRa((~+ diff --git a/session_topology/lab/assests/Enrichr/GO_Biological_Process_2018.human.enrichr.reports.txt b/session_topology/lab/assests/Enrichr/GO_Biological_Process_2018.human.enrichr.reports.txt deleted file mode 100644 index 7450045f..00000000 --- a/session_topology/lab/assests/Enrichr/GO_Biological_Process_2018.human.enrichr.reports.txt +++ /dev/null @@ -1,1906 +0,0 @@ -Gene_set Term Overlap P-value Adjusted P-value Old P-value Old Adjusted P-value Odds Ratio Combined Score Genes -GO_Biological_Process_2018 extracellular matrix organization (GO:0030198) 23/229 8.924360092948456e-14 4.554100955431598e-10 0 0 7.385050089904957 221.90160336728763 ITGB1;APP;SPARC;VWF;COL14A1;LAMB2;ELN;MMP2;ITGA1;BGN;HTRA1;NPNT;HSPG2;FBLN5;VCAN;COL4A2;ITGA11;COL6A1;TIMP2;ITGA8;COL8A1;A2M;GAS6 -GO_Biological_Process_2018 platelet aggregation (GO:0070527) 11/33 3.57063149387735e-13 9.110466256628057e-10 0 0 24.509803921568626 702.4721504836468 PDGFRA;CSRP1;ILK;HBB;HSPB1;FLNA;MYH9;TLN1;GAS6;MYL9;VCL -GO_Biological_Process_2018 homotypic cell-cell adhesion (GO:0034109) 11/38 2.090556641561889e-12 3.556036847296772e-09 0 0 21.284829721362236 572.4254997041618 PDGFRA;CSRP1;ILK;HBB;HSPB1;FLNA;MYH9;TLN1;GAS6;MYL9;VCL -GO_Biological_Process_2018 platelet degranulation (GO:0002576) 15/124 1.4531751219267392e-10 1.8538881617980367e-07 0 0 8.894686907020875 201.4833375376513 APP;SPARC;VWF;ACTN1;PCDH7;ACTN4;CLU;SOD1;ISLR;SERPING1;FLNA;TLN1;A2M;GAS6;VCL -GO_Biological_Process_2018 muscle contraction (GO:0006936) 15/137 6.040871332247157e-10 6.165313281691447e-07 0 0 8.050665521683127 170.893913708344 ROCK1;TPM2;TPM1;ITGA1;LMOD1;SORBS1;MYLK;ACTA2;GJA1;CALD1;MYH11;TLN1;MYL9;CRYAB;VCL -GO_Biological_Process_2018 regulated exocytosis (GO:0045055) 15/148 1.7901216054251938e-09 1.5224984254141275e-06 0 0 7.4523052464228945 150.09674793876366 APP;SPARC;VWF;ACTN1;PCDH7;ACTN4;CLU;SOD1;ISLR;SERPING1;FLNA;TLN1;A2M;GAS6;VCL -GO_Biological_Process_2018 cell-matrix adhesion (GO:0007160) 12/90 3.355568891066891e-09 2.4462097215877638e-06 0 0 9.803921568627453 191.3004364289284 ITGB1;ECM2;CTTN;ACTN1;ITGA1;ITGA11;ITGA8;ILK;SORBS1;NPNT;VCL;FBLN5 -GO_Biological_Process_2018 cellular protein metabolic process (GO:0044267) 25/484 1.282362434957013e-08 8.179869381982048e-06 0 0 3.7980068060281966 69.01729124192582 APP;FSTL1;PRSS23;LTBP1;CYR61;IGFBP7;IGFBP6;LMCD1;JAG1;GSN;IGFBP5;LAMB2;IGFBP3;MMP2;IGFBP2;RPL35A;HSPG2;KTN1;VCAN;SPARCL1;MSRB3;CPE;GAS6;MFGE8;RPS23 -GO_Biological_Process_2018 negative regulation of smooth muscle cell proliferation (GO:0048662) 7/27 5.6054511164544046e-08 3.1782907830296473e-05 0 0 19.06318082788671 318.2968094829242 CNN1;MEF2C;IGFBP5;IGFBP3;TPM1;OGN;PRKG1 -GO_Biological_Process_2018 neuron projection morphogenesis (GO:0048812) 13/163 3.7548751910686605e-07 0.00019161128100023378 0 0 5.864308913749549 86.76268849542835 MEF2A;APP;NCKAP1;ECM2;ROCK1;OMD;PRELP;ANK3;ALCAM;CTTN;MAP1B;OGN;FMOD -GO_Biological_Process_2018 negative regulation of smooth muscle cell migration (GO:0014912) 5/14 8.120632578675278e-07 0.0003767235277179995 0 0 26.260504201680675 368.26910703890053 MEF2C;IGFBP5;IGFBP3;TPM1;PRKG1 -GO_Biological_Process_2018 cell morphogenesis involved in neuron differentiation (GO:0048667) 10/97 8.155766328013625e-07 0.0003468239630987794 0 0 7.580351728320193 106.27175901283847 MEF2A;APP;MEF2C;ECM2;MAP1B;OMD;OGN;PRELP;ANK3;FMOD -GO_Biological_Process_2018 negative regulation of cell migration (GO:0030336) 11/121 8.191684737430889e-07 0.00032155513242392166 0 0 6.6844919786096275 93.68299510429169 CLIC4;JAG1;IGFBP5;IGFBP3;TPM1;PHLDB2;VCL;PFN2;PTPRG;RHOB;CLASP2 -GO_Biological_Process_2018 cardiac muscle cell development (GO:0055013) 6/27 1.3942015327695938e-06 0.0005081864586945169 0 0 16.33986928104575 220.31354059648163 PDGFRB;PDGFRA;NEXN;MYH11;SORBS2;PDLIM5 -GO_Biological_Process_2018 post-translational protein modification (GO:0043687) 18/357 2.1063844836056497e-06 0.000716592001322642 0 0 3.707365299060801 48.45725752014688 APP;IGFBP5;EPAS1;LAMB2;IGFBP3;LMO7;FBXO32;FSTL1;PRSS23;LTBP1;CYR61;KTN1;VCAN;SPARCL1;IGFBP7;GAS6;MFGE8;SKP1 -GO_Biological_Process_2018 regulation of signal transduction (GO:0009966) 14/233 4.0014468560807935e-06 0.001276211456661268 0 0 4.4180762433728855 54.911627011858435 PDGFRB;APP;PDGFRA;IGFBP5;IGFBP3;IGFBP2;ILK;NUCKS1;CLU;RGS5;IGFBP6;OGT;CD55;FGFR1 -GO_Biological_Process_2018 negative regulation of cellular process (GO:0048523) 22/534 4.336645475012399e-06 0.0013017589328816626 0 0 3.0293016082837627 37.40705657858833 APP;ACTN1;IGFBP3;CAV1;ITGA1;FHL1;PKD2;CLU;CYR61;RHOB;CTGF;SFRP4;GJA1;NOV;FRZB;ADAMTS1;CDH13;MYH9;APBB2;IGFBP7;IGFBP6;SKIL -GO_Biological_Process_2018 actin filament organization (GO:0007015) 10/120 5.723486389471453e-06 0.001622608391415157 0 0 6.127450980392156 73.96404674999181 EPS8;GSN;TPM2;ACTN1;TPM1;LMOD1;PLS3;DSTN;FLNA;RHOB -GO_Biological_Process_2018 sulfur compound catabolic process (GO:0044273) 6/35 6.978167460498888e-06 0.0018741888711013585 0 0 12.605042016806724 149.65618761095604 VCAN;OMD;OGN;BGN;PRELP;FMOD -GO_Biological_Process_2018 glycosaminoglycan catabolic process (GO:0006027) 7/56 1.0456201866391332e-05 0.0026678999062097484 0 0 9.191176470588236 105.40730951797458 VCAN;OMD;OGN;BGN;PRELP;FMOD;HSPG2 -GO_Biological_Process_2018 cell junction assembly (GO:0034329) 7/59 1.4856089741094738e-05 0.003610029807086023 0 0 8.723828514456631 96.9836800254364 TJP1;CDH11;ILK;CDH13;FLNA;TLN1;VCL -GO_Biological_Process_2018 negative regulation of vascular smooth muscle cell proliferation (GO:1904706) 4/12 1.520002663016593e-05 0.003525715267897125 0 0 24.509803921568626 271.91699456198467 CNN1;MEF2C;TPM1;PRKG1 -GO_Biological_Process_2018 keratan sulfate catabolic process (GO:0042340) 4/12 1.520002663016593e-05 0.003372423299727685 0 0 24.509803921568626 271.91699456198467 OMD;OGN;PRELP;FMOD -GO_Biological_Process_2018 positive regulation of supramolecular fiber organization (GO:1902905) 8/84 1.8841959345570453e-05 0.004006271605851918 0 0 7.002801120448179 76.18644464278363 APP;NCKAP1;CTTN;SYNPO2;LMOD1;SYNPO;CLU;PFN2 -GO_Biological_Process_2018 regulation of vascular smooth muscle cell proliferation (GO:1904705) 5/25 1.906502569746144e-05 0.003891553045365829 0 0 14.705882352941178 159.8184561289541 CNN1;MEF2C;MMP2;TPM1;PRKG1 -GO_Biological_Process_2018 regulation of muscle contraction (GO:0006937) 5/26 2.334320555337313e-05 0.004581552997648579 0 0 14.140271493212671 150.8088885593605 CNN1;CAV1;TPM1;PPP1R12B;MYL9 -GO_Biological_Process_2018 response to hydrogen peroxide (GO:0042542) 6/43 2.392675683091969e-05 0.004522157041043822 0 0 10.259917920656637 109.17079198934731 NET1;HBB;HBA1;AQP1;RHOB;SOD1 -GO_Biological_Process_2018 axon development (GO:0061564) 8/94 4.280071083582249e-05 0.00780042954982865 0 0 6.257822277847309 62.94715799249581 APP;ECM2;MAP1B;OMD;OGN;PRELP;ANK3;FMOD -GO_Biological_Process_2018 negative regulation of complement activation (GO:0045916) 3/6 4.827066830703694e-05 0.00849397311623481 0 0 36.76470588235294 365.39288467660316 SERPING1;A2M;CD55 -GO_Biological_Process_2018 negative regulation of endothelial cell proliferation (GO:0001937) 5/30 4.837154015176749e-05 0.00822799897981565 0 0 12.254901960784313 121.772045717924 MEF2C;SPARC;CAV2;CAV1;NR2F2 -GO_Biological_Process_2018 extracellular matrix assembly (GO:0085029) 4/16 5.3538135336371865e-05 0.00881306789101631 0 0 18.38235294117647 180.79257992582862 MFAP4;MYH11;GAS6;FBLN5 -GO_Biological_Process_2018 axonogenesis (GO:0007409) 12/223 5.790144989880397e-05 0.009233471838549894 0 0 3.9567396465312585 38.604991290960434 ENAH;APP;ECM2;MAP1B;OMD;OGN;PRELP;ANK3;FMOD;SPTAN1;SPTBN1;PDLIM7 -GO_Biological_Process_2018 regulation of nitric oxide biosynthetic process (GO:0045428) 5/32 6.685255843897642e-05 0.010337836536790808 0 0 11.488970588235295 110.44371534708395 PTGIS;CAV1;HBB;PKD2;CLU -GO_Biological_Process_2018 regulation of insulin-like growth factor receptor signaling pathway (GO:0043567) 4/17 6.926464657035053e-05 0.010395808572014671 0 0 17.301038062283734 165.70200575154757 IGFBP5;IGFBP3;IGFBP2;IGFBP6 -GO_Biological_Process_2018 elastic fiber assembly (GO:0048251) 3/7 8.362346603733128e-05 0.0121923013482429 0 0 31.512605042016805 295.87772215444613 MFAP4;MYH11;FBLN5 -GO_Biological_Process_2018 response to calcium ion (GO:0051592) 7/79 0.0001001902411705609 0.01420196668592701 0 0 6.515264333581535 59.995419185543184 MEF2A;CLIC4;MEF2C;CAV1;PKD2;ADD1;SEC31A -GO_Biological_Process_2018 actin filament bundle organization (GO:0061572) 5/35 0.00010424200388171221 0.014376944481307496 0 0 10.504201680672267 96.31087606724294 EPS8;PLS3;PAWR;ADD1;RHOB -GO_Biological_Process_2018 regulation of cell migration (GO:0030334) 14/316 0.00011651426827152029 0.015646639762883368 0 0 3.2576321667907675 29.505992985000816 PDGFRB;PDGFRA;CLIC4;JAG1;IGFBP5;TPM1;NEXN;ACTN4;CYR61;MYLK;RHOB;FLNA;CDH13;VCL -GO_Biological_Process_2018 regulation of vascular associated smooth muscle cell migration (GO:1904752) 3/8 0.00013245164511389152 0.017330788333748418 0 0 27.573529411764714 246.21212100227203 MEF2C;TPM1;PRKG1 -GO_Biological_Process_2018 aorta development (GO:0035904) 3/8 0.00013245164511389152 0.01689751862540471 0 0 27.573529411764714 246.21212100227203 PDGFRB;JAG1;PKD2 -GO_Biological_Process_2018 regulation of amyloid fibril formation (GO:1905906) 3/8 0.00013245164511389152 0.016485384024785082 0 0 27.573529411764714 246.21212100227203 APP;CLU;CRYAB -GO_Biological_Process_2018 integrin-mediated signaling pathway (GO:0007229) 6/58 0.0001339622950842621 0.016276418852737843 0 0 7.606490872210954 67.83432183754813 ITGB1;DST;ADAMTS1;ITGA11;ILK;MYH9 -GO_Biological_Process_2018 regulation of apoptotic process (GO:0042981) 25/815 0.00013607973823386246 0.016149183818776745 0 0 2.255503428365212 20.079099453035887 ITGB1;HSPB1;TNFRSF11B;KALRN;CLU;AQP1;FRZB;FLNA;TGM2;PDGFRB;PDGFRA;MEF2C;PTGIS;ACTN1;IGFBP3;PAWR;ACTN4;RHOB;SOD1;NET1;SFRP4;GAS6;IL6ST;CRYAB;FGFR1 -GO_Biological_Process_2018 actin filament bundle assembly (GO:0051017) 5/37 0.00013689994992371668 0.015877282828652868 0 0 9.936406995230524 88.39686199778669 EPS8;PLS3;PAWR;ADD1;RHOB -GO_Biological_Process_2018 regulation of angiogenesis (GO:0045765) 10/177 0.00016092225500221772 0.01824858371725149 0 0 4.1542040545031576 36.2852658598905 ITGB1;FOXC1;SPARC;PTGIS;COL4A2;ROCK1;HSPB1;HSPG2;AQP1;RHOB -GO_Biological_Process_2018 regulation of stress fiber assembly (GO:0051492) 6/60 0.00016198189179786668 0.0179694259531416 0 0 7.352941176470589 64.17666182356724 ROCK1;TPM1;PHLDB2;CTGF;PFN2;CLASP2 -GO_Biological_Process_2018 response to reactive oxygen species (GO:0000302) 6/60 0.00016198189179786668 0.017587097741372633 0 0 7.352941176470589 64.17666182356724 PDGFRA;TPM1;HBB;HBA1;PKD2;SOD1 -GO_Biological_Process_2018 muscle organ development (GO:0007517) 6/60 0.00016198189179786668 0.0172206998717607 0 0 7.352941176470589 64.17666182356724 MEF2A;MEF2C;TAGLN;FHL1;ITGA11;AEBP1 -GO_Biological_Process_2018 actin crosslink formation (GO:0051764) 3/9 0.00019668008036128668 0.020482825511911145 0 0 24.509803921568626 209.16500261842182 EPS8;FLNA;PLS3 -GO_Biological_Process_2018 regulation of muscle system process (GO:0090257) 3/9 0.00019668008036128668 0.020073169001672917 0 0 24.509803921568626 209.16500261842182 TPM1;PPP1R12B;MYL9 -GO_Biological_Process_2018 regulation of ATPase activity (GO:0043462) 5/40 0.00019990034909475758 0.020001793753540158 0 0 9.191176470588236 78.28760634294561 PLN;TPM2;TPM1;DNAJC10;PFN2 -GO_Biological_Process_2018 protein localization to cell periphery (GO:1990778) 8/117 0.00020048283646945675 0.01967430604814688 0 0 5.027652086475618 42.80936107855936 ROCK1;CAV1;SLMAP;FLNA;ANK3;GAS6;SPTBN1;CLASP2 -GO_Biological_Process_2018 cellular response to reactive oxygen species (GO:0034614) 6/65 0.00025252933598249577 0.02431428682110709 0 0 6.787330316742081 56.22612986802208 NET1;PDGFRA;TPM1;PKD2;AQP1;RHOB -GO_Biological_Process_2018 cellular response to calcium ion (GO:0071277) 5/42 0.00025276979669739525 0.023886745787903854 0 0 8.753501400560223 72.5055267674957 MEF2A;CLIC4;MEF2C;PKD2;ADD1 -GO_Biological_Process_2018 regulation of phospholipase C activity (GO:1900274) 3/10 0.0002781485119252793 0.025807124660994548 0 0 22.058823529411764 180.60342730935463 PDGFRB;PDGFRA;FGFR1 -GO_Biological_Process_2018 gas transport (GO:0015669) 3/10 0.0002781485119252793 0.025346283149191073 0 0 22.058823529411764 180.60342730935463 HBB;HBA1;AQP1 -GO_Biological_Process_2018 actin filament capping (GO:0051693) 3/10 0.0002781485119252793 0.024901611514994742 0 0 22.058823529411764 180.60342730935463 EPS8;GSN;ADD1 -GO_Biological_Process_2018 regulation of actin filament bundle assembly (GO:0032231) 4/24 0.0002869183577783132 0.025243868616254 0 0 12.254901960784313 99.95481434245882 ROCK1;SYNPO2;SYNPO;PFN2 -GO_Biological_Process_2018 regulation of smooth muscle cell proliferation (GO:0048660) 5/44 0.00031563617632345535 0.027299854369128684 0 0 8.355614973262032 67.35394676159639 PDGFRB;IGFBP5;IGFBP3;OGN;CDH13 -GO_Biological_Process_2018 positive regulation of actin filament bundle assembly (GO:0032233) 5/44 0.00031563617632345535 0.026844856796309872 0 0 8.355614973262032 67.35394676159639 SYNPO2;TPM1;SYNPO;CTGF;PFN2 -GO_Biological_Process_2018 regulation of cell death (GO:0010941) 7/96 0.00033872123195947723 0.028335974535888726 0 0 5.361519607843138 42.84032766112928 NOV;HBB;HBA1;CLU;CRYAB;CYR61;CTGF -GO_Biological_Process_2018 protein localization to membrane (GO:0072657) 9/160 0.0003519421613348585 0.02896711047244812 0 0 4.1360294117647065 32.88988666926096 ROCK1;CAV1;SLMAP;CPE;FLNA;ANK3;GAS6;SPTBN1;CLASP2 -GO_Biological_Process_2018 glycosaminoglycan biosynthetic process (GO:0006024) 7/99 0.0004087217791597093 0.03310646411193645 0 0 5.199049316696375 40.56545689218056 VCAN;OMD;OGN;BGN;PRELP;FMOD;HSPG2 -GO_Biological_Process_2018 actomyosin structure organization (GO:0031032) 6/71 0.00040912027983442404 0.0326209498124229 0 0 6.2137531068765535 48.476603329104606 EPB41L4A;TPM1;LMOD1;MYH9;SORBS1;MYH10 -GO_Biological_Process_2018 cellular response to metal ion (GO:0071248) 7/100 0.00043446492495278376 0.034108838646677776 0 0 5.147058823529412 39.84541719939721 MEF2A;MEF2C;CLIC4;ANK3;PKD2;ADD1;AQP1 -GO_Biological_Process_2018 blood circulation (GO:0008015) 5/50 0.0005762854861336556 0.04455734599606128 0 0 7.352941176470589 54.844907238282744 PLN;CYB5R3;ELN;SERPING1;SOD1 -GO_Biological_Process_2018 positive regulation of phosphorylation (GO:0042327) 10/208 0.0005825903397229125 0.044372514979194366 0 0 3.535067873303168 26.32927847460587 PDGFRB;APP;PDGFRA;FAM129A;NPTN;ITLN1;ILK;GAS6;CYR61;FGFR1 -GO_Biological_Process_2018 keratan sulfate biosynthetic process (GO:0018146) 4/29 0.0006079708929909007 0.04562463921959657 0 0 10.141987829614603 75.10530984294425 OMD;OGN;PRELP;FMOD -GO_Biological_Process_2018 positive regulation of actin filament polymerization (GO:0030838) 5/51 0.0006319107057762839 0.046733917848933014 0 0 7.208765859284892 53.10526573045139 NCKAP1;GSN;CTTN;LMOD1;PFN2 -GO_Biological_Process_2018 positive regulation of cytoskeleton organization (GO:0051495) 6/77 0.0006328859396630668 0.04613738500143756 0 0 5.729564553093965 42.19950539817815 NCKAP1;CTTN;SYNPO2;LMOD1;SYNPO;PFN2 -GO_Biological_Process_2018 extracellular matrix disassembly (GO:0022617) 6/78 0.0006779289751788687 0.048724951554053056 0 0 5.656108597285066 41.269615564957924 ELN;MMP2;TIMP2;HTRA1;A2M;HSPG2 -GO_Biological_Process_2018 cell-cell junction assembly (GO:0007043) 6/79 0.0007254145584216205 0.05141375682813235 0 0 5.58451228592703 40.3691395856747 TJP1;GJA1;CDH11;CDH13;TLN1;VCL -GO_Biological_Process_2018 circulatory system development (GO:0072359) 7/109 0.0007293402630894827 0.050983881678707266 0 0 4.722072315164597 34.109276356916425 MEF2A;GJA1;MEF2C;FOXC1;MICAL2;PKD2;HSPG2 -GO_Biological_Process_2018 smooth muscle contraction (GO:0006939) 3/14 0.0008103673564198415 0.0558824948623034 0 0 15.756302521008402 112.1537219537643 ROCK1;MYH11;MYLK -GO_Biological_Process_2018 cellular response to osmotic stress (GO:0071470) 3/14 0.0008103673564198415 0.05513739493080602 0 0 15.756302521008402 112.1537219537643 PKD2;AQP1;MYLK -GO_Biological_Process_2018 cell morphogenesis involved in differentiation (GO:0000904) 5/54 0.0008230378267403663 0.055262658287580126 0 0 6.8082788671023975 48.355857818123425 MEF2C;ACTN1;ITGA8;ILK;ANTXR1 -GO_Biological_Process_2018 skeletal system development (GO:0001501) 8/146 0.000884047699736451 0.058588252100715714 0 0 4.0290088638195005 28.32795945932505 VCAN;FRZB;CDH11;PLS3;TNFRSF11B;PRELP;AEBP1;FGFR1 -GO_Biological_Process_2018 endodermal cell differentiation (GO:0035987) 4/32 0.0008915226345500537 0.058326153898832375 0 0 9.191176470588236 64.54576959279048 COL4A2;MMP2;COL6A1;COL8A1 -GO_Biological_Process_2018 regulation of protein phosphorylation (GO:0001932) 11/261 0.0009366053982849783 0.060499966423395515 0 0 3.098940725715574 21.609683759523353 APP;IGFBP3;DNAJC10;FAM129A;NPTN;ITLN1;ILK;HSPB1;GAS6;CYR61;SOD1 -GO_Biological_Process_2018 positive regulation of phosphoprotein phosphatase activity (GO:0032516) 3/15 0.0010028136488024722 0.0639669756229877 0 0 14.705882352941178 101.54331736882686 PDGFRB;PPP1R12A;ITGA1 -GO_Biological_Process_2018 receptor-mediated endocytosis (GO:0006898) 9/188 0.0011204000024328958 0.07058520015327244 0 0 3.520025031289112 23.915294747990387 ITGB1;SPARC;CTTN;CAV2;CAV1;HBB;HBA1;HSPG2;PDLIM7 -GO_Biological_Process_2018 keratan sulfate metabolic process (GO:0042339) 4/34 0.001125531009623295 0.07004371636716676 0 0 8.650519031141869 58.73270195616532 OMD;OGN;PRELP;FMOD -GO_Biological_Process_2018 aorta morphogenesis (GO:0035909) 3/16 0.001221878444752376 0.0751234422117033 0 0 13.786764705882351 92.47287539834332 PDGFRB;JAG1;MYLK -GO_Biological_Process_2018 myoblast differentiation (GO:0045445) 3/16 0.001221878444752376 0.07422911551870684 0 0 13.786764705882351 92.47287539834332 MBNL1;JAG1;EPAS1 -GO_Biological_Process_2018 mitotic cytokinesis (GO:0000281) 5/59 0.0012330680773265735 0.07402760468938242 0 0 6.231306081754735 41.738844987346546 SEPT7;ANK3;MYH10;SPTBN1;RHOB -GO_Biological_Process_2018 cellular protein modification process (GO:0006464) 26/1001 0.0012380490116776911 0.0734623733324565 0 0 1.9098548510313216 12.784985706842964 APP;EPAS1;ILK;FSTL1;PRSS23;LTBP1;CYR61;PPP1CB;MAN2A1;STK38L;IGFBP7;SKP1;MGEA5;PPP1R12A;IGFBP5;LAMB2;IGFBP3;LMO7;FBXO32;KTN1;VCAN;SPARCL1;CPE;GAS6;MFGE8;FGFR1 -GO_Biological_Process_2018 positive regulation of osteoblast differentiation (GO:0045669) 4/35 0.0012573108546932244 0.07374778495976465 0 0 8.403361344537815 56.12420236190343 MEF2C;NPNT;IL6ST;CYR61 -GO_Biological_Process_2018 positive regulation of cell differentiation (GO:0045597) 9/194 0.0013942878897582002 0.08085285342541018 0 0 3.411158277744088 22.429632810046407 MEF2A;MEF2C;FOXC1;FRZB;ADIRF;NPNT;IL6ST;CYR61;CTGF -GO_Biological_Process_2018 muscle cell differentiation (GO:0042692) 4/36 0.0013995103571758866 0.08024383542324212 0 0 8.169934640522875 53.68981085302527 MBNL1;MEF2C;JAG1;SYNE1 -GO_Biological_Process_2018 endoderm formation (GO:0001706) 4/36 0.0013995103571758866 0.07935223725187276 0 0 8.169934640522875 53.68981085302527 COL4A2;MMP2;COL6A1;COL8A1 -GO_Biological_Process_2018 actin cytoskeleton reorganization (GO:0031532) 5/61 0.00143337004413062 0.08037898170547862 0 0 6.0270009643201545 39.463156539865984 GSN;CTTN;MYH9;FLNA;ANTXR1 -GO_Biological_Process_2018 cardiac myofibril assembly (GO:0055003) 3/17 0.0014688684212084148 0.08147429949376675 0 0 12.975778546712803 84.64441552053991 MEF2A;PDGFRB;PDGFRA -GO_Biological_Process_2018 ureteric bud development (GO:0001657) 3/17 0.0014688684212084148 0.08059823175727464 0 0 12.975778546712803 84.64441552053991 FOXC1;NPNT;PKD2 -GO_Biological_Process_2018 mesonephric tubule development (GO:0072164) 3/17 0.0014688684212084148 0.07974080375985683 0 0 12.975778546712803 84.64441552053991 FOXC1;NPNT;PKD2 -GO_Biological_Process_2018 negative regulation of cell proliferation (GO:0008285) 13/363 0.0014818091944442248 0.07959655072893558 0 0 2.6332847188462165 17.154510941927413 APP;IGFBP5;IGFBP3;ITGA1;PKD2;SFRP4;NOV;FRZB;ADAMTS1;OGN;CDH13;IGFBP7;IGFBP6 -GO_Biological_Process_2018 positive regulation of protein phosphorylation (GO:0001934) 14/412 0.0016162064005344856 0.08591147147841123 0 0 2.4985722444317533 16.060006863061385 PDGFRB;APP;PDGFRA;RBPMS;CAV2;CAV1;ITLN1;FAM129A;ILK;CYR61;NPTN;GAS6;PFN2;FGFR1 -GO_Biological_Process_2018 cytoskeleton organization (GO:0007010) 7/126 0.001701085283229 0.08949111546719163 0 0 4.084967320261438 26.04774848738709 DST;PALLD;TPM1;MICAL2;ANK3;TLN1;SYNE1 -GO_Biological_Process_2018 regulation of amyloid-beta formation (GO:1902003) 3/18 0.0017450224885294321 0.09086581386699688 0 0 12.254901960784313 77.83073328433055 APP;ROCK1;CLU -GO_Biological_Process_2018 cell-substrate adherens junction assembly (GO:0007045) 3/18 0.0017450224885294321 0.08994797736328983 0 0 12.254901960784313 77.83073328433055 CTTN;ACTN1;SORBS1 -GO_Biological_Process_2018 regulation of smooth muscle cell migration (GO:0014910) 3/18 0.0017450224885294321 0.08904849758965692 0 0 12.254901960784313 77.83073328433055 PDGFRB;IGFBP5;IGFBP3 -GO_Biological_Process_2018 focal adhesion assembly (GO:0048041) 3/18 0.0017450224885294321 0.08816682929669002 0 0 12.254901960784313 77.83073328433055 CTTN;ACTN1;SORBS1 -GO_Biological_Process_2018 skeletal muscle tissue development (GO:0007519) 4/39 0.0018928686399265105 0.09469910460338217 0 0 7.541478129713424 47.2825173582219 SVIL;MEF2C;CAV2;CAV1 -GO_Biological_Process_2018 inositol lipid-mediated signaling (GO:0048017) 4/39 0.0018928686399265105 0.09377969582082507 0 0 7.541478129713424 47.2825173582219 PDGFRB;PDGFRA;OGT;FGFR1 -GO_Biological_Process_2018 regulation of cytoskeleton organization (GO:0051493) 6/95 0.0018941067923399376 0.09293872078183367 0 0 4.643962848297214 29.113039789117426 PDGFRB;CLIC4;ROCK1;NEXN;PHLDB2;RHOB -GO_Biological_Process_2018 negative regulation of blood vessel morphogenesis (GO:2000181) 5/65 0.0019044895061039286 0.09255818999665093 0 0 5.656108597285066 35.42726969521417 FOXC1;SPARC;COL4A2;ROCK1;HSPG2 -GO_Biological_Process_2018 protein localization to plasma membrane (GO:0072659) 7/130 0.002033254342846421 0.09788393312778572 0 0 3.959276018099548 24.540058549232 ROCK1;SLMAP;FLNA;ANK3;GAS6;SPTBN1;CLASP2 -GO_Biological_Process_2018 negative regulation of epithelial cell proliferation (GO:0050680) 5/66 0.002038083015882913 0.09719941710327573 0 0 5.570409982174689 34.5128431986167 SPARC;CAV2;NFIB;CAV1;NR2F2 -GO_Biological_Process_2018 negative regulation of cell junction assembly (GO:1901889) 3/19 0.0020515137427385053 0.09693402434439438 0 0 11.609907120743037 71.85577415516104 ROCK1;PHLDB2;CLASP2 -GO_Biological_Process_2018 Rac protein signal transduction (GO:0016601) 3/19 0.0020515137427385053 0.09604472136875772 0 0 11.609907120743037 71.85577415516104 EPS8;NCKAP1;CDH13 -GO_Biological_Process_2018 positive regulation of phospholipase activity (GO:0010518) 3/19 0.0020515137427385053 0.09517158753813268 0 0 11.609907120743037 71.85577415516104 PDGFRB;PDGFRA;FGFR1 -GO_Biological_Process_2018 response to peptide hormone (GO:0043434) 4/40 0.002080977001839728 0.09566869946295614 0 0 7.352941176470589 45.40380722886994 IGFBP5;SORT1;CAV1;OGT -GO_Biological_Process_2018 negative regulation of cell motility (GO:2000146) 6/97 0.0021057277661403343 0.09594222134476896 0 0 4.548211036992117 28.031052787141604 CLIC4;JAG1;IGFBP5;TPM1;VCL;RHOB -GO_Biological_Process_2018 negative regulation of STAT cascade (GO:1904893) 4/41 0.0022816069925477482 0.10303575648647044 0 0 7.173601147776184 43.63612096838445 LEPROT;CAV1;BGN;ASPN -GO_Biological_Process_2018 regulation of actin filament polymerization (GO:0030833) 5/68 0.002325608095637259 0.10410156238628886 0 0 5.406574394463668 32.784243769623124 EPS8;NCKAP1;CTTN;LMOD1;PFN2 -GO_Biological_Process_2018 hydrogen peroxide metabolic process (GO:0042743) 3/20 0.0023894513760955856 0.10602930758448498 0 0 11.029411764705882 66.58115613022164 HBB;HBA1;SOD1 -GO_Biological_Process_2018 negative regulation of angiogenesis (GO:0016525) 5/69 0.0024799313298591343 0.10909559979544106 0 0 5.3282182438192685 31.96677540900224 FOXC1;SPARC;COL4A2;ROCK1;HSPG2 -GO_Biological_Process_2018 regulation of canonical Wnt signaling pathway (GO:0060828) 9/213 0.002634113980884112 0.1148878943970224 0 0 3.1068765534382767 18.45238733535343 SFRP4;FRZB;TBL1XR1;CAV1;IGFBP2;USP34;ILK;IGFBP6;DKK3 -GO_Biological_Process_2018 cytoskeleton-dependent cytokinesis (GO:0061640) 5/70 0.0026415568584343967 0.11423614108975193 0 0 5.2521008403361344 31.178502187688828 SEPT7;ANK3;MYH10;SPTBN1;RHOB -GO_Biological_Process_2018 regulation of mesenchymal stem cell differentiation (GO:2000739) 2/6 0.002666261101669795 0.11433554959513414 0 0 24.509803921568626 145.27152265712303 PDGFRA;LTBP2 -GO_Biological_Process_2018 regulation of wound healing, spreading of epidermal cells (GO:1903689) 2/6 0.002666261101669795 0.11338275334850804 0 0 24.509803921568626 145.27152265712303 PHLDB2;CLASP2 -GO_Biological_Process_2018 regulation of keratinocyte apoptotic process (GO:1902172) 2/6 0.002666261101669795 0.11244570580017328 0 0 24.509803921568626 145.27152265712303 SFRP4;GSN -GO_Biological_Process_2018 cardiac ventricle morphogenesis (GO:0003208) 4/43 0.002722071913385513 0.11385846700005142 0 0 6.839945280437758 40.399192584016035 MEF2C;JAG1;TPM1;CPE -GO_Biological_Process_2018 negative regulation of protein metabolic process (GO:0051248) 4/43 0.002722071913385513 0.11293278840655505 0 0 6.839945280437758 40.399192584016035 ROCK1;FLNA;CLU;CRYAB -GO_Biological_Process_2018 regulation of ruffle assembly (GO:1900027) 3/21 0.0027598825412571233 0.11357806941963793 0 0 10.504201680672267 61.896713841115655 EPS8;CAV1;PFN2 -GO_Biological_Process_2018 regulation of cardiac muscle hypertrophy (GO:0010611) 3/21 0.0027598825412571233 0.1126694448642808 0 0 10.504201680672267 61.896713841115655 MEF2A;IL6ST;LMCD1 -GO_Biological_Process_2018 positive regulation of programmed cell death (GO:0043068) 10/257 0.0028310020910471047 0.11465558468740772 0 0 2.8610666056305782 16.78623407595486 NET1;ITGB1;SFRP4;FRZB;IGFBP3;PAWR;KALRN;RHOB;TGM2;SOD1 -GO_Biological_Process_2018 transmembrane receptor protein tyrosine kinase signaling pathway (GO:0007169) 13/396 0.003155215219170048 0.1267800256962579 0 0 2.413844325609032 13.900601866202418 PDGFRB;PDGFRA;NCKAP1;FOXC1;ROCK1;SORT1;CAV2;MMP2;HSPB1;SORBS1;KALRN;PTPRG;FGFR1 -GO_Biological_Process_2018 regulation of calcium ion import (GO:0090279) 3/22 0.0031637941723242644 0.12613157547945875 0 0 10.026737967914439 57.71373613824493 PDGFRB;PLN;PKD2 -GO_Biological_Process_2018 protein localization to cell surface (GO:0034394) 3/22 0.0031637941723242644 0.12515381132845518 0 0 10.026737967914439 57.71373613824493 FLNA;FBLN5;VCL -GO_Biological_Process_2018 positive regulation of phosphatase activity (GO:0010922) 3/22 0.0031637941723242644 0.12419108970285167 0 0 10.026737967914439 57.71373613824493 PDGFRB;MEF2C;ITGA1 -GO_Biological_Process_2018 ER to Golgi vesicle-mediated transport (GO:0006888) 8/180 0.0033057192962958853 0.12877164556486947 0 0 3.2679738562091507 18.666997354704755 USO1;BCAP29;ANK3;GAS6;SPTAN1;SPTBN1;CD55;SEC31A -GO_Biological_Process_2018 endocytosis (GO:0006897) 10/263 0.0033370461924090945 0.12900717212017884 0 0 2.795795124133304 15.943494846826 APP;SPARC;CTTN;SORT1;CAV1;HBB;HBA1;GAS6;HSPG2;PDLIM7 -GO_Biological_Process_2018 positive regulation of apoptotic process (GO:0043065) 11/307 0.003347720883168022 0.12844676441207833 0 0 2.634604330331481 15.015862827806039 NET1;ITGB1;SFRP4;PTGIS;FRZB;IGFBP3;PAWR;KALRN;RHOB;TGM2;SOD1 -GO_Biological_Process_2018 cellular response to ionizing radiation (GO:0071479) 4/46 0.003486818109626795 0.13278531950317565 0 0 6.3938618925831205 36.181366208908784 NET1;NUCKS1;CRYAB;RHOB -GO_Biological_Process_2018 regulation of actin cytoskeleton organization (GO:0032956) 5/75 0.0035661072390925507 0.13479885363769845 0 0 4.901960784313727 27.628826898710837 EPS8;PDGFRB;PDGFRA;ROCK1;RHOB -GO_Biological_Process_2018 regulation of smooth muscle contraction (GO:0006940) 3/23 0.0036021147651529835 0.1351587621071741 0 0 9.59079283887468 53.960046399241826 CNN1;CTTN;CAV1 -GO_Biological_Process_2018 smooth muscle cell migration (GO:0014909) 2/7 0.0036993216448215132 0.13779298068265824 0 0 21.008403361344534 117.63877763399329 PDGFRB;NOV -GO_Biological_Process_2018 regulation of myosin-light-chain-phosphatase activity (GO:0035507) 2/7 0.0036993216448215132 0.13679448082263898 0 0 21.008403361344534 117.63877763399329 PPP1R12A;ROCK1 -GO_Biological_Process_2018 oxygen transport (GO:0015671) 2/7 0.0036993216448215132 0.13581034786708046 0 0 21.008403361344534 117.63877763399329 HBB;HBA1 -GO_Biological_Process_2018 retrograde axonal transport (GO:0008090) 2/7 0.0036993216448215132 0.13484027395374415 0 0 21.008403361344534 117.63877763399329 DST;SOD1 -GO_Biological_Process_2018 negative regulation of platelet-derived growth factor receptor-beta signaling pathway (GO:2000587) 2/7 0.0036993216448215132 0.1338839599540722 0 0 21.008403361344534 117.63877763399329 PDGFRB;PDGFRA -GO_Biological_Process_2018 negative regulation of membrane protein ectodomain proteolysis (GO:0051045) 2/7 0.0036993216448215132 0.13294111516566326 0 0 21.008403361344534 117.63877763399329 ROCK1;TIMP2 -GO_Biological_Process_2018 negative regulation of nitric oxide metabolic process (GO:1904406) 2/7 0.0036993216448215132 0.1320114570176516 0 0 21.008403361344534 117.63877763399329 PTGIS;CAV1 -GO_Biological_Process_2018 negative regulation of nitric oxide biosynthetic process (GO:0045019) 2/7 0.0036993216448215132 0.13109471078836235 0 0 21.008403361344534 117.63877763399329 PTGIS;CAV1 -GO_Biological_Process_2018 regulation of cellular response to insulin stimulus (GO:1900076) 2/7 0.0036993216448215132 0.13019060933464952 0 0 21.008403361344534 117.63877763399329 NUCKS1;OGT -GO_Biological_Process_2018 myofibril assembly (GO:0030239) 4/47 0.003771055495236679 0.13180613830269022 0 0 6.257822277847309 34.92115359509263 PDGFRB;PDGFRA;TPM1;LMOD1 -GO_Biological_Process_2018 negative regulation of JAK-STAT cascade (GO:0046426) 4/47 0.003771055495236679 0.13090949790607329 0 0 6.257822277847309 34.92115359509263 LEPROT;CAV1;BGN;ASPN -GO_Biological_Process_2018 regulation of cardiac muscle cell action potential (GO:0098901) 3/24 0.004075716115408387 0.14052959011438512 0 0 9.191176470588236 50.576367774688826 CAV1;SLMAP;FLNA -GO_Biological_Process_2018 protein heterooligomerization (GO:0051291) 4/49 0.004385811667519623 0.15020669086813848 0 0 6.0024009603841515 32.58931914387981 SEPT7;COL6A1;HBB;HBA1 -GO_Biological_Process_2018 regulation of endothelial cell proliferation (GO:0001936) 5/79 0.004457245629719468 0.15163549632305628 0 0 4.6537602382725245 25.191847894350172 SPARC;CAV2;CAV1;CDH13;NR2F2 -GO_Biological_Process_2018 regulation of transforming growth factor beta receptor signaling pathway (GO:0017015) 5/79 0.004457245629719468 0.1506312877381354 0 0 4.6537602382725245 25.191847894350172 CAV1;LTBP4;SKIL;ASPN;DKK3 -GO_Biological_Process_2018 lamellipodium organization (GO:0097581) 3/25 0.0045854150117760895 0.15394324213877228 0 0 8.823529411764708 47.513599961123525 CTTN;CDH13;VCL -GO_Biological_Process_2018 positive regulation of nitric oxide biosynthetic process (GO:0045429) 3/25 0.0045854150117760895 0.1529370771574731 0 0 8.823529411764708 47.513599961123525 HBB;PKD2;CLU -GO_Biological_Process_2018 positive regulation of nitric oxide metabolic process (GO:1904407) 3/25 0.0045854150117760895 0.15194397925385314 0 0 8.823529411764708 47.513599961123525 HBB;PKD2;CLU -GO_Biological_Process_2018 positive regulation of extracellular matrix assembly (GO:1901203) 2/8 0.0048882840152581755 0.16093492470879012 0 0 18.38235294117647 97.81091827585104 PHLDB2;CLASP2 -GO_Biological_Process_2018 barbed-end actin filament capping (GO:0051016) 2/8 0.0048882840152581755 0.15990329057604147 0 0 18.38235294117647 97.81091827585104 EPS8;ADD1 -GO_Biological_Process_2018 myelin maintenance (GO:0043217) 2/8 0.0048882840152581755 0.1588847982793788 0 0 18.38235294117647 97.81091827585104 CLU;SOD1 -GO_Biological_Process_2018 negative regulation of protein homooligomerization (GO:0032463) 2/8 0.0048882840152581755 0.1578791982902688 0 0 18.38235294117647 97.81091827585104 CLU;CRYAB -GO_Biological_Process_2018 amyloid fibril formation (GO:1990000) 2/8 0.0048882840152581755 0.1568862473576256 0 0 18.38235294117647 97.81091827585104 APP;GSN -GO_Biological_Process_2018 positive regulation of behavior (GO:0048520) 2/8 0.0048882840152581755 0.15590570831164044 0 0 18.38235294117647 97.81091827585104 MEF2C;SGIP1 -GO_Biological_Process_2018 negative regulation of voltage-gated potassium channel activity (GO:1903817) 2/8 0.0048882840152581755 0.15493734987492214 0 0 18.38235294117647 97.81091827585104 CAV1;ANK3 -GO_Biological_Process_2018 actin filament network formation (GO:0051639) 2/8 0.0048882840152581755 0.15398094648063254 0 0 18.38235294117647 97.81091827585104 ACTN1;PLS3 -GO_Biological_Process_2018 endochondral ossification (GO:0001958) 2/8 0.0048882840152581755 0.15303627809731574 0 0 18.38235294117647 97.81091827585104 MEF2C;FOXC1 -GO_Biological_Process_2018 peptidyl-tyrosine modification (GO:0018212) 4/51 0.005064750615188733 0.15759403895919574 0 0 5.767012687427912 30.48125939663462 PDGFRB;PDGFRA;EFEMP1;FGFR1 -GO_Biological_Process_2018 positive regulation of ossification (GO:0045778) 4/52 0.005429192444300774 0.16791011541373851 0 0 5.656108597285066 29.502063785178862 MEF2C;NPNT;IL6ST;CYR61 -GO_Biological_Process_2018 regulation of calcium ion transport (GO:0051924) 4/52 0.005429192444300774 0.16689860869437864 0 0 5.656108597285066 29.502063785178862 PLN;CACNA2D1;PKD2;MYLK -GO_Biological_Process_2018 negative regulation of cell-matrix adhesion (GO:0001953) 3/27 0.005716107496075754 0.17466644642200346 0 0 8.169934640522875 42.19335958267928 JAG1;PHLDB2;CLASP2 -GO_Biological_Process_2018 muscle fiber development (GO:0048747) 3/27 0.005716107496075754 0.17362676519330106 0 0 8.169934640522875 42.19335958267928 CAV2;NEXN;MYH11 -GO_Biological_Process_2018 regulation of neuron differentiation (GO:0045664) 5/84 0.005780992831828229 0.1745586178746713 0 0 4.3767507002801125 22.554183477049055 MEF2C;ZEB1;ROCK1;ID4;FGFR1 -GO_Biological_Process_2018 positive regulation of transcription from RNA polymerase II promoter (GO:0045944) 21/848 0.006165776907977383 0.18508211506710934 0 0 1.820893451720311 9.26605540324532 MEF2A;APP;IL33;FOXC1;MEF2C;JAG1;PPP1R12A;PCGF5;EPAS1;ATRX;EBF1;MICAL2;ADIRF;PKD2;CYR61;NFIA;NFIB;TBL1XR1;SUB1;CDH13;OGT -GO_Biological_Process_2018 positive regulation of cellular biosynthetic process (GO:0031328) 7/159 0.006168132378991225 0.18407005573094867 0 0 3.2371439141694416 16.47175095500333 PDGFRB;PDGFRA;FAM129A;HBB;SORBS1;PKD2;CLU -GO_Biological_Process_2018 positive regulation of intracellular signal transduction (GO:1902533) 14/479 0.006172864143789438 0.18314026584742732 0 0 2.1490851037701093 10.933668920430193 PDGFRB;ITGB1;APP;PDGFRA;CAV2;CAV1;ILK;ACTN4;GJA1;FLNA;CDH13;S100A4;GAS6;FGFR1 -GO_Biological_Process_2018 negative regulation of supramolecular fiber organization (GO:1902904) 4/54 0.0062097693614337115 0.18317024885200134 0 0 5.446623093681917 27.677731609979297 CLU;CRYAB;PFN2;CLASP2 -GO_Biological_Process_2018 ionotropic glutamate receptor signaling pathway (GO:0035235) 2/9 0.006228748155641678 0.1826741484956292 0 0 16.33986928104575 82.9833317724312 GRIA2;APP -GO_Biological_Process_2018 regulation of platelet-derived growth factor receptor-beta signaling pathway (GO:2000586) 2/9 0.006228748155641678 0.1816302962185113 0 0 16.33986928104575 82.9833317724312 PDGFRB;PDGFRA -GO_Biological_Process_2018 negative regulation of amyloid-beta formation (GO:1902430) 2/9 0.006228748155641678 0.18059830589908796 0 0 16.33986928104575 82.9833317724312 ROCK1;CLU -GO_Biological_Process_2018 mesodermal cell differentiation (GO:0048333) 2/9 0.006228748155641678 0.17957797648722867 0 0 16.33986928104575 82.9833317724312 ITGB1;ITGA8 -GO_Biological_Process_2018 cytoskeletal anchoring at plasma membrane (GO:0007016) 2/9 0.006228748155641678 0.17856911145078358 0 0 16.33986928104575 82.9833317724312 ANK3;TLN1 -GO_Biological_Process_2018 regulation of cell differentiation (GO:0045595) 6/121 0.006259547043950885 0.1784495450574378 0 0 3.6460865337870687 18.49895765806168 LTBP4;PHLDB2;SKIL;CTGF;FGFR1;CLASP2 -GO_Biological_Process_2018 cellular response to hydrogen peroxide (GO:0070301) 3/28 0.006338474368399805 0.1796957483441345 0 0 7.878151260504201 39.87224665485465 NET1;AQP1;RHOB -GO_Biological_Process_2018 chondrocyte differentiation (GO:0002062) 3/28 0.006338474368399805 0.1787029541543879 0 0 7.878151260504201 39.87224665485465 MEF2C;NOV;NFIB -GO_Biological_Process_2018 regulation of transmembrane receptor protein serine/threonine kinase signaling pathway (GO:0090092) 3/28 0.006338474368399805 0.17772106979090224 0 0 7.878151260504201 39.87224665485465 SFRP4;LTBP4;DKK3 -GO_Biological_Process_2018 regulation of NIK/NF-kappaB signaling (GO:1901222) 3/28 0.006338474368399805 0.1767499164040667 0 0 7.878151260504201 39.87224665485465 APP;ILK;ACTN4 -GO_Biological_Process_2018 positive regulation of calcium-mediated signaling (GO:0050850) 3/28 0.006338474368399805 0.17578931903230546 0 0 7.878151260504201 39.87224665485465 CDH13;PKD2;LMCD1 -GO_Biological_Process_2018 positive regulation of fibroblast proliferation (GO:0048146) 3/28 0.006338474368399805 0.17483910649699572 0 0 7.878151260504201 39.87224665485465 PDGFRA;GAS6;AQP1 -GO_Biological_Process_2018 sulfur compound biosynthetic process (GO:0044272) 6/122 0.006510245401882423 0.1786117327193871 0 0 3.616200578592093 18.205321096903738 VCAN;OMD;OGN;BGN;PRELP;FMOD -GO_Biological_Process_2018 negative regulation of transforming growth factor beta receptor signaling pathway (GO:0030512) 4/55 0.006626582676837161 0.18083129090855635 0 0 5.3475935828877015 26.827091128468968 CAV1;LTBP1;SKIL;ASPN -GO_Biological_Process_2018 regulation of embryonic development (GO:0045995) 3/29 0.006999688477968685 0.18999686331422447 0 0 7.606490872210954 37.74256821042938 SEPT7;PHLDB2;CLASP2 -GO_Biological_Process_2018 skeletal muscle organ development (GO:0060538) 3/29 0.006999688477968685 0.1889915889051545 0 0 7.606490872210954 37.74256821042938 SVIL;MEF2C;CAV1 -GO_Biological_Process_2018 striated muscle tissue development (GO:0014706) 3/29 0.006999688477968685 0.18799689633196948 0 0 7.606490872210954 37.74256821042938 SVIL;MEF2C;CAV1 -GO_Biological_Process_2018 positive regulation of protein modification process (GO:0031401) 7/163 0.007037588581303722 0.1880252069654078 0 0 3.157704799711296 15.651131310844073 APP;NPTN;FAM129A;ITLN1;ILK;GAS6;CYR61 -GO_Biological_Process_2018 phosphatidylinositol-mediated signaling (GO:0048015) 4/56 0.007061519200856742 0.1876819400102706 0 0 5.2521008403361344 26.014154760338602 PDGFRB;PDGFRA;OGT;FGFR1 -GO_Biological_Process_2018 cellular response to cAMP (GO:0071320) 3/30 0.00770031569209585 0.2035995387397156 0 0 7.352941176470589 35.78304376462829 IGFBP5;PKD2;AQP1 -GO_Biological_Process_2018 regulation of developmental growth (GO:0048638) 3/30 0.00770031569209585 0.2025500565812635 0 0 7.352941176470589 35.78304376462829 APP;CTTN;SOD1 -GO_Biological_Process_2018 positive regulation of phospholipase C activity (GO:0010863) 3/30 0.00770031569209585 0.2015113383423852 0 0 7.352941176470589 35.78304376462829 PDGFRB;PDGFRA;FGFR1 -GO_Biological_Process_2018 aminoglycan metabolic process (GO:0006022) 3/30 0.00770031569209585 0.20048321926920976 0 0 7.352941176470589 35.78304376462829 VCAN;BGN;HSPG2 -GO_Biological_Process_2018 artery morphogenesis (GO:0048844) 3/30 0.00770031569209585 0.1994655379531224 0 0 7.352941176470589 35.78304376462829 PDGFRB;JAG1;PKD2 -GO_Biological_Process_2018 positive regulation of reactive oxygen species biosynthetic process (GO:1903428) 3/30 0.00770031569209585 0.19845813624628847 0 0 7.352941176470589 35.78304376462829 HBB;PKD2;CLU -GO_Biological_Process_2018 protein deglycosylation (GO:0006517) 2/10 0.007716404071885449 0.1978734169790525 0 0 14.705882352941178 71.53539437144566 MGEA5;MAN2A1 -GO_Biological_Process_2018 positive regulation of amyloid-beta formation (GO:1902004) 2/10 0.007716404071885449 0.19688404989415725 0 0 14.705882352941178 71.53539437144566 APP;CLU -GO_Biological_Process_2018 muscle cell migration (GO:0014812) 2/10 0.007716404071885449 0.1959045272578679 0 0 14.705882352941178 71.53539437144566 ROCK1;NOV -GO_Biological_Process_2018 regulation of amyloid-beta clearance (GO:1900221) 2/10 0.007716404071885449 0.1949347028655022 0 0 14.705882352941178 71.53539437144566 ROCK1;CLU -GO_Biological_Process_2018 actin filament depolymerization (GO:0030042) 2/10 0.007716404071885449 0.19397443339325834 0 0 14.705882352941178 71.53539437144566 MICAL2;DSTN -GO_Biological_Process_2018 regulation of protein tyrosine kinase activity (GO:0061097) 3/31 0.00844087624322023 0.2111460366134943 0 0 7.1157495256167 33.97534977805836 APP;CAV1;GAS6 -GO_Biological_Process_2018 skeletal system morphogenesis (GO:0048705) 3/31 0.00844087624322023 0.21011605594708704 0 0 7.1157495256167 33.97534977805836 PDGFRA;SFRP4;FGFR1 -GO_Biological_Process_2018 cell morphogenesis (GO:0000902) 4/59 0.008478235660075654 0.2100215367639129 0 0 4.985044865403789 23.779924774570965 NCKAP1;CDH11;CDH13;CLU -GO_Biological_Process_2018 positive regulation of protein binding (GO:0032092) 4/60 0.00898879381553508 0.2215933084090605 0 0 4.901960784313727 23.096944162125283 APP;MEF2C;CAV1;ADD1 -GO_Biological_Process_2018 regulation of potassium ion transmembrane transport (GO:1901379) 3/32 0.009221846177048418 0.2262455819301831 0 0 6.893382352941178 32.303630689355394 CAV1;FHL1;FLNA -GO_Biological_Process_2018 substrate adhesion-dependent cell spreading (GO:0034446) 3/32 0.009221846177048418 0.22516306718410564 0 0 6.893382352941178 32.303630689355394 ITGA8;ILK;ANTXR1 -GO_Biological_Process_2018 negative regulation of morphogenesis of an epithelium (GO:1905331) 2/11 0.009347030213740986 0.22713283419390595 0 0 13.368983957219253 62.4692060205029 PHLDB2;CLASP2 -GO_Biological_Process_2018 drug transmembrane transport (GO:0006855) 2/11 0.009347030213740986 0.226056375264077 0 0 13.368983957219253 62.4692060205029 TMEM30A;ATP8B1 -GO_Biological_Process_2018 positive regulation by host of viral process (GO:0044794) 2/11 0.009347030213740986 0.22499007160717105 0 0 13.368983957219253 62.4692060205029 CAV2;NUCKS1 -GO_Biological_Process_2018 regulation of long-term neuronal synaptic plasticity (GO:0048169) 2/11 0.009347030213740986 0.22393378019117488 0 0 13.368983957219253 62.4692060205029 APP;NPTN -GO_Biological_Process_2018 negative regulation of amyloid precursor protein catabolic process (GO:1902992) 2/11 0.009347030213740986 0.22288736065757128 0 0 13.368983957219253 62.4692060205029 ROCK1;CLU -GO_Biological_Process_2018 positive regulation of sodium ion transmembrane transporter activity (GO:2000651) 2/11 0.009347030213740986 0.221850675259164 0 0 13.368983957219253 62.4692060205029 ACTN4;ANK3 -GO_Biological_Process_2018 epithelial cell-cell adhesion (GO:0090136) 2/11 0.009347030213740986 0.2208235887996308 0 0 13.368983957219253 62.4692060205029 NOV;VCL -GO_Biological_Process_2018 positive regulation of calcium ion transport (GO:0051928) 3/33 0.010043658740843656 0.236187974905646 0 0 6.6844919786096275 30.75410303932754 PDGFRB;CAV1;MYLK -GO_Biological_Process_2018 cellular glucose homeostasis (GO:0001678) 3/33 0.010043658740843656 0.23510454382809715 0 0 6.6844919786096275 30.75410303932754 NUCKS1;GAS6;OGT -GO_Biological_Process_2018 negative regulation of protein modification process (GO:0031400) 4/63 0.010639474681410535 0.24791433469971674 0 0 4.6685340802987865 21.21001012294753 CRTAP;IGFBP3;DNAJC10;FAM129A -GO_Biological_Process_2018 regulation of transcription from RNA polymerase II promoter (GO:0006357) 31/1478 0.01074027930182324 0.2491256603509273 0 0 1.5422271750378096 6.992078908251387 APP;FOXC1;EPAS1;ADIRF;NUCKS1;AEBP1;PKD2;FSTL1;CYR61;SKIL;MEF2A;IL33;MEF2C;JAG1;PPP1R12A;PCGF5;CAV1;ATRX;EBF1;SMARCA5;NR2F2;EID1;SFRP4;ZEB1;NFIA;NFIB;TBL1XR1;SUB1;ID4;CDH13;OGT -GO_Biological_Process_2018 epithelial tube morphogenesis (GO:0060562) 3/34 0.010906705761053796 0.25184126469980783 0 0 6.4878892733564015 29.31473273014767 MEF2C;PKD2;RHOB -GO_Biological_Process_2018 positive regulation of muscle cell differentiation (GO:0051149) 3/34 0.010906705761053796 0.2507068445885474 0 0 6.4878892733564015 29.31473273014767 MEF2A;MEF2C;IGFBP3 -GO_Biological_Process_2018 bone development (GO:0060348) 3/34 0.010906705761053796 0.2495825986486885 0 0 6.4878892733564015 29.31473273014767 SFRP4;SBDS;PLS3 -GO_Biological_Process_2018 endoplasmic reticulum organization (GO:0007029) 3/34 0.010906705761053796 0.24846839061900686 0 0 6.4878892733564015 29.31473273014767 ATL3;CAV2;SEC31A -GO_Biological_Process_2018 negative regulation of protein oligomerization (GO:0032460) 2/12 0.01111649188708034 0.2521220359989821 0 0 12.254901960784313 55.1387931092381 CLU;CRYAB -GO_Biological_Process_2018 endochondral bone morphogenesis (GO:0060350) 2/12 0.01111649188708034 0.2510064517688981 0 0 12.254901960784313 55.1387931092381 MEF2C;FOXC1 -GO_Biological_Process_2018 positive regulation of amyloid precursor protein catabolic process (GO:1902993) 2/12 0.01111649188708034 0.249900696474762 0 0 12.254901960784313 55.1387931092381 APP;CLU -GO_Biological_Process_2018 exit from mitosis (GO:0010458) 2/12 0.01111649188708034 0.2488046407884692 0 0 12.254901960784313 55.1387931092381 EPS8;CLASP2 -GO_Biological_Process_2018 regulation of cell communication by electrical coupling (GO:0010649) 2/12 0.01111649188708034 0.2477181576409213 0 0 12.254901960784313 55.1387931092381 CAV1;ANK3 -GO_Biological_Process_2018 regulation of establishment of endothelial barrier (GO:1903140) 2/12 0.01111649188708034 0.2466411221729173 0 0 12.254901960784313 55.1387931092381 ROCK1;ADD1 -GO_Biological_Process_2018 cellular response to fluid shear stress (GO:0071498) 2/12 0.01111649188708034 0.24557341168732025 0 0 12.254901960784313 55.1387931092381 MEF2C;PKD2 -GO_Biological_Process_2018 negative regulation of epithelial cell differentiation (GO:0030857) 2/12 0.01111649188708034 0.2445149056024611 0 0 12.254901960784313 55.1387931092381 ZEB1;CAV1 -GO_Biological_Process_2018 cardiac muscle fiber development (GO:0048739) 2/12 0.01111649188708034 0.2434654854067424 0 0 12.254901960784313 55.1387931092381 NEXN;MYH11 -GO_Biological_Process_2018 regulation of vascular endothelial cell proliferation (GO:1905562) 2/12 0.01111649188708034 0.2424250346144059 0 0 12.254901960784313 55.1387931092381 MEF2C;FGFR1 -GO_Biological_Process_2018 endosome transport via multivesicular body sorting pathway (GO:0032509) 2/12 0.01111649188708034 0.2413934387224297 0 0 12.254901960784313 55.1387931092381 LEPROT;SORT1 -GO_Biological_Process_2018 positive regulation of stem cell differentiation (GO:2000738) 2/12 0.01111649188708034 0.2403705851685211 0 0 12.254901960784313 55.1387931092381 FOXC1;LTBP2 -GO_Biological_Process_2018 dermatan sulfate biosynthetic process (GO:0030208) 2/12 0.01111649188708034 0.2393563632901729 0 0 12.254901960784313 55.1387931092381 VCAN;BGN -GO_Biological_Process_2018 negative regulation of protein phosphorylation (GO:0001933) 7/178 0.011120836757166306 0.23844382341100695 0 0 2.8916060806345016 13.009147065147278 IGFBP3;CAV1;DNAJC10;BGN;FAM129A;HSPB1;ASPN -GO_Biological_Process_2018 positive regulation of protein polymerization (GO:0032273) 4/64 0.011230280048919116 0.2397829250612312 0 0 4.595588235294118 20.63024619931989 NCKAP1;CTTN;LMOD1;PFN2 -GO_Biological_Process_2018 homophilic cell adhesion via plasma membrane adhesion molecules (GO:0007156) 4/64 0.011230280048919116 0.2387838295401427 0 0 4.595588235294118 20.63024619931989 ITGB1;CDH11;NPTN;CDH13 -GO_Biological_Process_2018 positive regulation of cell motility (GO:2000147) 7/179 0.011443382244266723 0.24230530951241944 0 0 2.8754518567203418 12.854258052098064 PDGFRB;PDGFRA;CAVIN1;CDH13;ACTN4;CYR61;MYLK -GO_Biological_Process_2018 positive regulation of NIK/NF-kappaB signaling (GO:1901224) 3/35 0.011811338972199631 0.2490630693187385 0 0 6.302521008403362 27.97497024618039 APP;ILK;ACTN4 -GO_Biological_Process_2018 endothelial cell migration (GO:0043542) 3/35 0.011811338972199631 0.2480381184161923 0 0 6.302521008403362 27.97497024618039 NOV;CDH13;MYH9 -GO_Biological_Process_2018 negative regulation of epithelial cell migration (GO:0010633) 3/35 0.011811338972199631 0.2470215687505521 0 0 6.302521008403362 27.97497024618039 NR2F2;PTPRG;PFN2 -GO_Biological_Process_2018 positive regulation of ATPase activity (GO:0032781) 3/35 0.011811338972199631 0.2460133174495295 0 0 6.302521008403362 27.97497024618039 TPM1;DNAJC10;PFN2 -GO_Biological_Process_2018 regulation of phosphoprotein phosphatase activity (GO:0043666) 3/35 0.011811338972199631 0.2450132633135558 0 0 6.302521008403362 27.97497024618039 PDGFRB;PPP1R12A;ITGA1 -GO_Biological_Process_2018 regulation of JAK-STAT cascade (GO:0046425) 4/65 0.011841808719011663 0.24465080928387256 0 0 4.524886877828054 20.072936188974584 LEPROT;CAV1;BGN;ASPN -GO_Biological_Process_2018 Golgi vesicle transport (GO:0048193) 9/271 0.012180755063404195 0.2506386818086758 0 0 2.4419361840677234 10.763805686719275 USO1;BCAP29;ANK3;GAS6;KLC1;SPTAN1;SPTBN1;CD55;SEC31A -GO_Biological_Process_2018 regulation of cellular component organization (GO:0051128) 6/140 0.0123666735760019 0.2534423102744486 0 0 3.151260504201681 13.842699703773091 NET1;SPARC;GSN;LTBP4;STK38L;CDH13 -GO_Biological_Process_2018 actin polymerization or depolymerization (GO:0008154) 3/36 0.01275787131856461 0.2604136693545408 0 0 6.127450980392156 26.72553210506226 GSN;DSTN;MICAL2 -GO_Biological_Process_2018 heart looping (GO:0001947) 3/36 0.01275787131856461 0.2593761646957577 0 0 6.127450980392156 26.72553210506226 MEF2C;MICAL2;PKD2 -GO_Biological_Process_2018 dermatan sulfate metabolic process (GO:0030205) 2/13 0.013020739690873395 0.2636699787401862 0 0 11.312217194570133 49.10873113032139 VCAN;BGN -GO_Biological_Process_2018 regulation of NMDA receptor activity (GO:2000310) 2/13 0.013020739690873395 0.2626278049111736 0 0 11.312217194570133 49.10873113032139 APP;MEF2C -GO_Biological_Process_2018 renal absorption (GO:0070293) 2/13 0.013020739690873395 0.2615938371753029 0 0 11.312217194570133 49.10873113032139 GSN;HBB -GO_Biological_Process_2018 positive regulation of actin nucleation (GO:0051127) 2/13 0.013020739690873395 0.26056797899030165 0 0 11.312217194570133 49.10873113032139 NCKAP1;GSN -GO_Biological_Process_2018 positive regulation of endothelial cell chemotaxis (GO:2001028) 2/13 0.013020739690873395 0.2595501353223708 0 0 11.312217194570133 49.10873113032139 HSPB1;FGFR1 -GO_Biological_Process_2018 negative regulation of reactive oxygen species biosynthetic process (GO:1903427) 2/13 0.013020739690873395 0.2585402126168363 0 0 11.312217194570133 49.10873113032139 PTGIS;CAV1 -GO_Biological_Process_2018 cellular response to low-density lipoprotein particle stimulus (GO:0071404) 2/13 0.013020739690873395 0.25753811876948424 0 0 11.312217194570133 49.10873113032139 ITGB1;CDH13 -GO_Biological_Process_2018 chondroitin sulfate catabolic process (GO:0030207) 2/13 0.013020739690873395 0.2565437630985596 0 0 11.312217194570133 49.10873113032139 VCAN;BGN -GO_Biological_Process_2018 positive regulation of angiogenesis (GO:0045766) 5/103 0.01333187651390771 0.261663714809504 0 0 3.5693889206167904 15.411184255292332 ITGB1;PTGIS;HSPB1;AQP1;RHOB -GO_Biological_Process_2018 positive regulation of macromolecule metabolic process (GO:0010604) 9/276 0.013586938085016173 0.2656480653173852 0 0 2.39769820971867 10.306856735962024 ACTA2;SFRP4;APP;MEF2C;GSN;ROCK1;ANK3;GAS6;CLU -GO_Biological_Process_2018 positive regulation of smooth muscle cell proliferation (GO:0048661) 3/37 0.01374657822559041 0.2677434682640758 0 0 5.9618441971383165 25.558219448490423 PDGFRB;MMP2;CDH13 -GO_Biological_Process_2018 positive regulation of stress fiber assembly (GO:0051496) 3/37 0.01374657822559041 0.2667254322630717 0 0 5.9618441971383165 25.558219448490423 TPM1;CTGF;PFN2 -GO_Biological_Process_2018 regulation of metal ion transport (GO:0010959) 3/37 0.01374657822559041 0.26571510865601466 0 0 5.9618441971383165 25.558219448490423 PLN;CACNA2D1;ANK3 -GO_Biological_Process_2018 positive regulation of vasculature development (GO:1904018) 5/104 0.013854240232207576 0.26678561473568024 0 0 3.535067873303168 15.12713496625545 ITGB1;PTGIS;HSPB1;AQP1;RHOB -GO_Biological_Process_2018 MAPK cascade (GO:0000165) 9/278 0.014181878679741487 0.2720681462508301 0 0 2.3804485823106223 10.130689935449576 MEF2A;PDGFRB;PDGFRA;MEF2C;ILK;SPTAN1;SPTBN1;SKP1;FGFR1 -GO_Biological_Process_2018 positive regulation of neurogenesis (GO:0050769) 4/69 0.01449998753635465 0.2771289752734748 0 0 4.262574595055414 18.04606772854956 MEF2C;ZEB1;NPTN;FGFR1 -GO_Biological_Process_2018 regulation of osteoblast differentiation (GO:0045667) 4/69 0.01449998753635465 0.2760949119329021 0 0 4.262574595055414 18.04606772854956 MEF2C;NPNT;IL6ST;CYR61 -GO_Biological_Process_2018 positive regulation of myoblast differentiation (GO:0045663) 2/14 0.015055807974103668 0.28561259513699266 0 0 10.504201680672267 44.07554044465884 MEF2C;IGFBP3 -GO_Biological_Process_2018 establishment or maintenance of actin cytoskeleton polarity (GO:0030950) 2/14 0.015055807974103668 0.2845547707105593 0 0 10.504201680672267 44.07554044465884 AQP1;RHOB -GO_Biological_Process_2018 regulation of calcineurin-NFAT signaling cascade (GO:0070884) 2/14 0.015055807974103668 0.2835047531064613 0 0 10.504201680672267 44.07554044465884 RCAN2;LMCD1 -GO_Biological_Process_2018 negative regulation of focal adhesion assembly (GO:0051895) 2/14 0.015055807974103668 0.28246245622004057 0 0 10.504201680672267 44.07554044465884 PHLDB2;CLASP2 -GO_Biological_Process_2018 hydrogen peroxide catabolic process (GO:0042744) 2/14 0.015055807974103668 0.2814277952082455 0 0 10.504201680672267 44.07554044465884 HBB;HBA1 -GO_Biological_Process_2018 negative regulation of biomineral tissue development (GO:0070168) 2/14 0.015055807974103668 0.2804006864666096 0 0 10.504201680672267 44.07554044465884 GAS6;ASPN -GO_Biological_Process_2018 epiboly involved in wound healing (GO:0090505) 2/14 0.015055807974103668 0.279381047606731 0 0 10.504201680672267 44.07554044465884 FLNA;CYR61 -GO_Biological_Process_2018 regulation of gastrulation (GO:0010470) 2/14 0.015055807974103668 0.2783687974342429 0 0 10.504201680672267 44.07554044465884 PHLDB2;CLASP2 -GO_Biological_Process_2018 regulation of synapse organization (GO:0050807) 2/14 0.015055807974103668 0.27736385592726004 0 0 10.504201680672267 44.07554044465884 MEF2C;PDLIM5 -GO_Biological_Process_2018 positive regulation of muscle hypertrophy (GO:0014742) 2/14 0.015055807974103668 0.2763661442152915 0 0 10.504201680672267 44.07554044465884 MEF2A;IL6ST -GO_Biological_Process_2018 positive regulation of endothelial cell migration (GO:0010595) 4/70 0.015218681808230572 0.2783545995247333 0 0 4.201680672268908 17.58500646841455 SPARC;HSPB1;RHOB;FGFR1 -GO_Biological_Process_2018 regulation of protein kinase activity (GO:0045859) 6/147 0.015415058888629225 0.2809394482452676 0 0 3.0012004801920766 12.522240088341375 BGN;HSPB1;GAS6;CYR61;ASPN;SOD1 -GO_Biological_Process_2018 protein homooligomerization (GO:0051260) 7/190 0.015451567757890784 0.2806026699947212 0 0 2.7089783281733744 11.296561011798165 VWF;ATL3;CAV1;ITLN1;PKD2;CRYAB;GLS -GO_Biological_Process_2018 negative regulation of signal transduction (GO:0009968) 9/283 0.015753399962683937 0.2850695035800572 0 0 2.3383911868634377 9.705958116099954 PDGFRB;SFRP4;PDGFRA;LEPROT;IGFBP5;FRZB;IGFBP3;DKK3;FGFR1 -GO_Biological_Process_2018 negative regulation of protein catabolic process (GO:0042177) 3/39 0.0158514372505976 0.2858299798226133 0 0 5.656108597285066 23.441714391961156 ROCK1;TIMP2;FLNA -GO_Biological_Process_2018 negative regulation of apoptotic process (GO:0043066) 13/485 0.015877427415491755 0.2852905355677973 0 0 1.9708914493632503 8.165121117615529 PDGFRB;ITGB1;PDGFRA;MEF2C;CAV1;HSPB1;AQP1;SOD1;FLNA;GAS6;IL6ST;CRYAB;FGFR1 -GO_Biological_Process_2018 negative regulation of canonical Wnt signaling pathway (GO:0090090) 6/148 0.015889400172083998 0.284503891502262 0 0 2.9809220985691582 12.347286510076142 SFRP4;FRZB;CAV1;IGFBP2;IGFBP6;DKK3 -GO_Biological_Process_2018 regulation of cation channel activity (GO:2001257) 4/71 0.01595945946919226 0.2847591666828255 0 0 4.142502071251036 17.140445548380693 APP;MEF2C;SLMAP;ANK3 -GO_Biological_Process_2018 heart development (GO:0007507) 6/149 0.016373748860606482 0.29113324193614937 0 0 2.9609159099881563 12.175510972677696 MEF2A;GJA1;MEF2C;FOXC1;MICAL2;PKD2 -GO_Biological_Process_2018 regulation of cell motility (GO:2000145) 5/109 0.016672970001820518 0.2954241872197574 0 0 3.3729087965461417 13.80857539669625 ROCK1;CAVIN1;NEXN;FLNA;RHOB -GO_Biological_Process_2018 Wnt signaling pathway (GO:0016055) 5/109 0.016672970001820518 0.2944019581982357 0 0 3.3729087965461417 13.80857539669625 SFRP4;FRZB;USP34;CPE;SKP1 -GO_Biological_Process_2018 cell-cell junction organization (GO:0045216) 4/72 0.0167225173311121 0.29425864117470696 0 0 4.084967320261438 16.71159773111701 TJP1;CDH11;CDH13;TLN1 -GO_Biological_Process_2018 Rho protein signal transduction (GO:0007266) 4/72 0.0167225173311121 0.2932474430950689 0 0 4.084967320261438 16.71159773111701 EPS8;ROCK1;CDH13;RHOB -GO_Biological_Process_2018 embryonic heart tube morphogenesis (GO:0003143) 3/40 0.016967963634697728 0.29653259735569354 0 0 5.514705882352941 22.480302600505595 MEF2C;MICAL2;PKD2 -GO_Biological_Process_2018 response to cAMP (GO:0051591) 3/40 0.016967963634697728 0.29552054070942835 0 0 5.514705882352941 22.480302600505595 IGFBP5;PKD2;AQP1 -GO_Biological_Process_2018 determination of heart left/right asymmetry (GO:0061371) 3/40 0.016967963634697728 0.29451536880225343 0 0 5.514705882352941 22.480302600505595 MEF2C;MICAL2;PKD2 -GO_Biological_Process_2018 myotube differentiation (GO:0014902) 2/15 0.01721781332755131 0.2978389878321842 0 0 9.803921568627453 39.821674240279606 MEF2C;SORT1 -GO_Biological_Process_2018 maintenance of protein location in nucleus (GO:0051457) 2/15 0.01721781332755131 0.29683277503545386 0 0 9.803921568627453 39.821674240279606 SYNE1;SKP1 -GO_Biological_Process_2018 negative regulation of adherens junction organization (GO:1903392) 2/15 0.01721781332755131 0.2958333380824725 0 0 9.803921568627453 39.821674240279606 PHLDB2;CLASP2 -GO_Biological_Process_2018 regulation of protein homooligomerization (GO:0032462) 2/15 0.01721781332755131 0.2948406087600481 0 0 9.803921568627453 39.821674240279606 CLU;CRYAB -GO_Biological_Process_2018 negative regulation of platelet-derived growth factor receptor signaling pathway (GO:0010642) 2/15 0.01721781332755131 0.2938545197675396 0 0 9.803921568627453 39.821674240279606 PDGFRB;PDGFRA -GO_Biological_Process_2018 regulation of establishment of cell polarity (GO:2000114) 2/15 0.01721781332755131 0.29287500470164785 0 0 9.803921568627453 39.821674240279606 GSN;ROCK1 -GO_Biological_Process_2018 dermatan sulfate proteoglycan biosynthetic process (GO:0050651) 2/15 0.01721781332755131 0.29190199804150946 0 0 9.803921568627453 39.821674240279606 VCAN;BGN -GO_Biological_Process_2018 vasculature development (GO:0001944) 2/15 0.01721781332755131 0.2909354351340873 0 0 9.803921568627453 39.821674240279606 PDGFRB;PDGFRA -GO_Biological_Process_2018 negative regulation of platelet activation (GO:0010544) 2/15 0.01721781332755131 0.2899752521798493 0 0 9.803921568627453 39.821674240279606 PDGFRA;PRKG1 -GO_Biological_Process_2018 positive regulation of potassium ion transport (GO:0043268) 2/15 0.01721781332755131 0.2890213862187314 0 0 9.803921568627453 39.821674240279606 FHL1;FLNA -GO_Biological_Process_2018 cytoplasmic sequestering of protein (GO:0051220) 2/15 0.01721781332755131 0.2880737751163749 0 0 9.803921568627453 39.821674240279606 FLNA;PKD2 -GO_Biological_Process_2018 negative regulation of anoikis (GO:2000811) 2/15 0.01721781332755131 0.2871323575506351 0 0 9.803921568627453 39.821674240279606 ITGB1;CAV1 -GO_Biological_Process_2018 bicarbonate transport (GO:0015701) 3/41 0.018127415441422053 0.3013166156272858 0 0 5.3802008608321366 21.57637995724636 HBB;HBA1;AQP1 -GO_Biological_Process_2018 mesenchymal cell differentiation (GO:0048762) 3/41 0.018127415441422053 0.3003383149271972 0 0 5.3802008608321366 21.57637995724636 FOXC1;MEF2C;S100A4 -GO_Biological_Process_2018 positive regulation of receptor-mediated endocytosis (GO:0048260) 3/41 0.018127415441422053 0.2993663462704749 0 0 5.3802008608321366 21.57637995724636 SFRP4;SGIP1;CLU -GO_Biological_Process_2018 cellular response to peptide (GO:1901653) 3/41 0.018127415441422053 0.29840064837927976 0 0 5.3802008608321366 21.57637995724636 APP;CACNA2D1;CAV1 -GO_Biological_Process_2018 nitrogen compound transport (GO:0071705) 4/74 0.01831621808321531 0.3005391025036904 0 0 3.97456279809221 15.898125506966677 TMEM30A;HBB;SLC38A2;AQP1 -GO_Biological_Process_2018 gland development (GO:0048732) 4/75 0.019147211812481427 0.31316737781760484 0 0 3.9215686274509807 15.512149691040188 CAV1;PKD2;DKK3;SOD1 -GO_Biological_Process_2018 positive regulation of kinase activity (GO:0033674) 5/113 0.01918444750152617 0.3127739156558724 0 0 3.2535137948984905 12.863272235030196 PDGFRB;PDGFRA;GAS6;CYR61;FGFR1 -GO_Biological_Process_2018 positive regulation of canonical Wnt signaling pathway (GO:0090263) 5/113 0.01918444750152617 0.31177782038308305 0 0 3.2535137948984905 12.863272235030196 SFRP4;TBL1XR1;CAV1;USP34;ILK -GO_Biological_Process_2018 cellular response to radiation (GO:0071478) 3/42 0.019329898502524718 0.31314435574090044 0 0 5.2521008403361344 20.725326872638654 NET1;ADIRF;RHOB -GO_Biological_Process_2018 positive regulation of epithelial cell apoptotic process (GO:1904037) 2/16 0.019502953090670832 0.31494800513194066 0 0 9.191176470588236 36.18740242951449 SFRP4;GSN -GO_Biological_Process_2018 leukocyte tethering or rolling (GO:0050901) 2/16 0.019502953090670832 0.31395447830187145 0 0 9.191176470588236 36.18740242951449 ITGB1;ROCK1 -GO_Biological_Process_2018 leukocyte adhesion to vascular endothelial cell (GO:0061756) 2/16 0.019502953090670832 0.31296720006821777 0 0 9.191176470588236 36.18740242951449 ITGB1;ROCK1 -GO_Biological_Process_2018 regulation of bicellular tight junction assembly (GO:2000810) 2/16 0.019502953090670832 0.31198611166675 0 0 9.191176470588236 36.18740242951449 TJP1;ROCK1 -GO_Biological_Process_2018 positive regulation of cardiac muscle hypertrophy (GO:0010613) 2/16 0.019502953090670832 0.3110111550677914 0 0 9.191176470588236 36.18740242951449 MEF2A;IL6ST -GO_Biological_Process_2018 regulation of SMAD protein import into nucleus (GO:0060390) 2/16 0.019502953090670832 0.3100422729647765 0 0 9.191176470588236 36.18740242951449 RBPMS;NOV -GO_Biological_Process_2018 regulation of membrane repolarization (GO:0060306) 2/16 0.019502953090670832 0.3090794087630225 0 0 9.191176470588236 36.18740242951449 CACNA2D1;CAV1 -GO_Biological_Process_2018 regulation of ERK1 and ERK2 cascade (GO:0070372) 8/247 0.02013184942215945 0.3180582897872436 0 0 2.381519409383187 9.300910144375193 PDGFRB;APP;PDGFRA;GAS6;NPNT;CYR61;CTGF;FGFR1 -GO_Biological_Process_2018 regulation of cell growth (GO:0001558) 7/201 0.020370142543652558 0.3208297450625279 0 0 2.560725782850454 9.97065970050567 NET1;GJA1;NOV;FRZB;FHL1;LTBP4;APBB2 -GO_Biological_Process_2018 positive regulation of ERK1 and ERK2 cascade (GO:0070374) 7/201 0.020370142543652558 0.3198425766161816 0 0 2.560725782850454 9.97065970050567 PDGFRB;APP;PDGFRA;GAS6;NPNT;CTGF;FGFR1 -GO_Biological_Process_2018 negative regulation of cell growth (GO:0030308) 5/115 0.02052849644119313 0.32134023723744953 0 0 3.1969309462915603 12.423085958404679 GJA1;NOV;FRZB;FHL1;APBB2 -GO_Biological_Process_2018 positive regulation of protein localization to plasma membrane (GO:1903078) 3/43 0.02057548812938112 0.3210908743860301 0 0 5.1299589603283176 19.922989782700032 ITGB1;SORBS1;SPTBN1 -GO_Biological_Process_2018 positive regulation of multicellular organismal process (GO:0051240) 7/202 0.02086558894414136 0.32462530604254064 0 0 2.548048922539313 9.860067549671086 IL33;FOXC1;ROCK1;SGIP1;GAS6;CLASP2;SOD1 -GO_Biological_Process_2018 negative regulation of transmembrane receptor protein serine/threonine kinase signaling pathway (GO:0090101) 4/77 0.02087829906941304 0.3238357451404703 0 0 3.819709702062644 14.778628653471301 NOV;CAV1;SKIL;ASPN -GO_Biological_Process_2018 axon guidance (GO:0007411) 6/158 0.021198441068994676 0.32780498416690856 0 0 2.792256142963515 10.760873886325431 ENAH;ALCAM;NFIB;SPTAN1;SPTBN1;PDLIM7 -GO_Biological_Process_2018 negative regulation of cell communication (GO:0010648) 4/78 0.021778692340429825 0.33576032330275946 0 0 3.770739064856712 14.429951744893652 PDGFRB;PDGFRA;IGFBP3;FGFR1 -GO_Biological_Process_2018 regulation of focal adhesion assembly (GO:0051893) 3/44 0.02186423016951756 0.3360637546838799 0 0 5.01336898395722 19.165624848343672 ROCK1;PHLDB2;CLASP2 -GO_Biological_Process_2018 adherens junction organization (GO:0034332) 3/44 0.02186423016951756 0.3350545542193637 0 0 5.01336898395722 19.165624848343672 CDH11;CDH13;VCL -GO_Biological_Process_2018 positive regulation of protein processing (GO:0010954) 2/17 0.021907503884829843 0.33471255186912185 0 0 8.650519031141869 33.0529935787675 GSN;MYH9 -GO_Biological_Process_2018 regulation of multicellular organism growth (GO:0040014) 2/17 0.021907503884829843 0.33371340992324383 0 0 8.650519031141869 33.0529935787675 APP;SOD1 -GO_Biological_Process_2018 positive regulation of epidermal cell differentiation (GO:0045606) 2/17 0.021907503884829843 0.3327202152508533 0 0 8.650519031141869 33.0529935787675 SFRP4;FOXC1 -GO_Biological_Process_2018 histone H2A monoubiquitination (GO:0035518) 2/17 0.021907503884829843 0.3317329149088626 0 0 8.650519031141869 33.0529935787675 PCGF5;SKP1 -GO_Biological_Process_2018 negative regulation of tumor necrosis factor-mediated signaling pathway (GO:0010804) 2/17 0.021907503884829843 0.33075145658072985 0 0 8.650519031141869 33.0529935787675 CCDC3;GAS6 -GO_Biological_Process_2018 negative regulation of intracellular transport (GO:0032387) 2/17 0.021907503884829843 0.3297757885672174 0 0 8.650519031141869 33.0529935787675 PLN;CRYAB -GO_Biological_Process_2018 negative regulation of stress fiber assembly (GO:0051497) 2/17 0.021907503884829843 0.3288058597773138 0 0 8.650519031141869 33.0529935787675 PHLDB2;CLASP2 -GO_Biological_Process_2018 regulation of protein localization to membrane (GO:1905475) 2/17 0.021907503884829843 0.3278416197193158 0 0 8.650519031141869 33.0529935787675 GSN;SPTBN1 -GO_Biological_Process_2018 positive regulation of macrophage activation (GO:0043032) 2/17 0.021907503884829843 0.3268830184920664 0 0 8.650519031141869 33.0529935787675 APP;IL33 -GO_Biological_Process_2018 cellular response to inorganic substance (GO:0071241) 2/17 0.021907503884829843 0.32593000677634604 0 0 8.650519031141869 33.0529935787675 ATRX;AQP1 -GO_Biological_Process_2018 positive regulation of cation transmembrane transport (GO:1904064) 2/17 0.021907503884829843 0.3249825358264148 0 0 8.650519031141869 33.0529935787675 FLNA;ANK3 -GO_Biological_Process_2018 platelet-derived growth factor receptor signaling pathway (GO:0048008) 2/17 0.021907503884829843 0.3240405574617006 0 0 8.650519031141869 33.0529935787675 PDGFRB;PDGFRA -GO_Biological_Process_2018 plasma membrane bounded cell projection organization (GO:0120036) 5/118 0.022657593307485742 0.3341667590985541 0 0 3.115653040877368 11.799788876718766 APP;CTTN;ROCK1;TPM1;CDH11 -GO_Biological_Process_2018 peptidyl-tyrosine phosphorylation (GO:0018108) 4/79 0.022702504439231996 0.3338642079348728 0 0 3.72300819061802 14.092628566124215 PDGFRB;PDGFRA;EFEMP1;FGFR1 -GO_Biological_Process_2018 positive regulation of signal transduction (GO:0009967) 7/206 0.022931317294358197 0.33626009239399385 0 0 2.4985722444317533 9.432739201441922 JAG1;PTGIS;NOV;NPTN;FLNA;ILK;GAS6 -GO_Biological_Process_2018 regulation of hydrolase activity (GO:0051336) 4/80 0.023649864149326627 0.3458030279484636 0 0 3.6764705882352944 13.766168780423575 TPM2;CAV1;PRKG1;SOD1 -GO_Biological_Process_2018 negative regulation of growth (GO:0045926) 5/120 0.024153605781122172 0.3521595722887613 0 0 3.063725490196078 11.40723530123137 GJA1;NOV;FRZB;FHL1;APBB2 -GO_Biological_Process_2018 regulation of adherens junction organization (GO:1903391) 2/18 0.024427820184259687 0.3551429242173139 0 0 8.169934640522875 30.32706392727768 ROCK1;ADD1 -GO_Biological_Process_2018 regulation of cellular response to transforming growth factor beta stimulus (GO:1903844) 2/18 0.024427820184259687 0.3541339954553329 0 0 8.169934640522875 30.32706392727768 LTBP4;DKK3 -GO_Biological_Process_2018 regulation of oxidative stress-induced intrinsic apoptotic signaling pathway (GO:1902175) 2/18 0.024427820184259687 0.35313078300361805 0 0 8.169934640522875 30.32706392727768 HSPB1;SOD1 -GO_Biological_Process_2018 regulation of cardiac muscle cell membrane repolarization (GO:0099623) 2/18 0.024427820184259687 0.3521332384188621 0 0 8.169934640522875 30.32706392727768 CACNA2D1;FLNA -GO_Biological_Process_2018 regulation of microtubule polymerization or depolymerization (GO:0031110) 2/18 0.024427820184259687 0.3511413138035977 0 0 8.169934640522875 30.32706392727768 MAP1B;CLASP2 -GO_Biological_Process_2018 positive regulation of protein localization to cell periphery (GO:1904377) 3/46 0.02457121383181665 0.3522104050105628 0 0 4.79539641943734 17.772600818406318 ITGB1;SORBS1;SPTBN1 -GO_Biological_Process_2018 cartilage development (GO:0051216) 3/46 0.02457121383181665 0.3512238212430262 0 0 4.79539641943734 17.772600818406318 MEF2C;NOV;NFIB -GO_Biological_Process_2018 positive regulation of ion transport (GO:0043270) 3/46 0.02457121383181665 0.35024274911664904 0 0 4.79539641943734 17.772600818406318 FHL1;ANK3;MYLK -GO_Biological_Process_2018 regulation of fibroblast proliferation (GO:0048145) 3/46 0.02457121383181665 0.3492671425731486 0 0 4.79539641943734 17.772600818406318 PDGFRA;GAS6;AQP1 -GO_Biological_Process_2018 regulation of cellular component movement (GO:0051270) 3/46 0.02457121383181665 0.3482969560660011 0 0 4.79539641943734 17.772600818406318 ROCK1;ACTN1;ACTN4 -GO_Biological_Process_2018 negative regulation of cell death (GO:0060548) 4/81 0.024620891899179938 0.3480343805028123 0 0 3.6310820624546114 13.450108686529784 NOV;CLU;CYR61;CTGF -GO_Biological_Process_2018 positive regulation of binding (GO:0051099) 4/81 0.024620891899179938 0.34707295956219675 0 0 3.6310820624546114 13.450108686529784 APP;FOXC1;CAV1;ADD1 -GO_Biological_Process_2018 regulation of anatomical structure morphogenesis (GO:0022603) 4/82 0.025615699801897405 0.3601016972151032 0 0 3.586800573888092 13.144009476184912 MEF2C;SPARC;PHLDB2;CLASP2 -GO_Biological_Process_2018 negative regulation of binding (GO:0051100) 4/82 0.025615699801897405 0.3591124068381386 0 0 3.586800573888092 13.144009476184912 PLN;ROCK1;CAV1;ID4 -GO_Biological_Process_2018 negative regulation of signaling (GO:0023057) 4/83 0.02663439171802707 0.3723706874988825 0 0 3.5435861091424528 12.847455622139282 PDGFRB;PDGFRA;IGFBP3;FGFR1 -GO_Biological_Process_2018 positive regulation of peptidyl-serine phosphorylation (GO:0033138) 4/83 0.02663439171802707 0.3713532812488856 0 0 3.5435861091424528 12.847455622139282 APP;CAV1;GAS6;PFN2 -GO_Biological_Process_2018 positive regulation of homeostatic process (GO:0032846) 4/83 0.02663439171802707 0.3703414194471175 0 0 3.5435861091424528 12.847455622139282 ANO1;SGIP1;CAV1;ATRX -GO_Biological_Process_2018 striated muscle cell differentiation (GO:0051146) 2/19 0.0270603328909748 0.3752415183224033 0 0 7.739938080495357 27.93874887273595 MEF2C;SORT1 -GO_Biological_Process_2018 drug transport (GO:0015893) 2/19 0.0270603328909748 0.3742246036386027 0 0 7.739938080495357 27.93874887273595 TMEM30A;ATP8B1 -GO_Biological_Process_2018 positive regulation of adherens junction organization (GO:1903393) 2/19 0.0270603328909748 0.3732131857909308 0 0 7.739938080495357 27.93874887273595 ROCK1;ADD1 -GO_Biological_Process_2018 regulation of epithelial cell migration (GO:0010632) 3/48 0.02745066607642364 0.3775761428247704 0 0 4.595588235294118 16.522816388978878 PTPRG;CLASP2;PFN2 -GO_Biological_Process_2018 positive regulation of chemotaxis (GO:0050921) 3/48 0.02745066607642364 0.37656115319352107 0 0 4.595588235294118 16.522816388978878 PDGFRB;HSPB1;CDH13 -GO_Biological_Process_2018 negative regulation of multicellular organismal process (GO:0051241) 5/125 0.028167551300316857 0.3853592876287317 0 0 2.9411764705882355 10.498778313730796 MEF2C;NFIB;GAS6;PFN2;PTPRG -GO_Biological_Process_2018 protein oligomerization (GO:0051259) 7/217 0.02933075217989865 0.4002000758663712 0 0 2.3719165085389 8.370775025879553 SEPT7;ATL3;VWF;CAV1;HBB;HBA1;CRYAB -GO_Biological_Process_2018 negative regulation of innate immune response (GO:0045824) 2/20 0.029801547943478963 0.4055394644148617 0 0 7.352941176470589 25.83231575299851 SERPING1;A2M -GO_Biological_Process_2018 Golgi to endosome transport (GO:0006895) 2/20 0.029801547943478963 0.40446090200950297 0 0 7.352941176470589 25.83231575299851 SORT1;VPS13A -GO_Biological_Process_2018 mesoderm formation (GO:0001707) 2/20 0.029801547943478963 0.4033880614206184 0 0 7.352941176470589 25.83231575299851 ITGB1;ITGA8 -GO_Biological_Process_2018 regulation of epidermal cell differentiation (GO:0045604) 2/20 0.029801547943478963 0.4023208972369659 0 0 7.352941176470589 25.83231575299851 SFRP4;ROCK1 -GO_Biological_Process_2018 ureteric bud morphogenesis (GO:0060675) 2/20 0.029801547943478963 0.4012593645265781 0 0 7.352941176470589 25.83231575299851 NPNT;PKD2 -GO_Biological_Process_2018 negative regulation of actin filament bundle assembly (GO:0032232) 2/20 0.029801547943478963 0.4002034188304556 0 0 7.352941176470589 25.83231575299851 PHLDB2;CLASP2 -GO_Biological_Process_2018 wound healing, spreading of cells (GO:0044319) 2/20 0.029801547943478963 0.3991530161563599 0 0 7.352941176470589 25.83231575299851 FLNA;CYR61 -GO_Biological_Process_2018 regulation of anoikis (GO:2000209) 2/20 0.029801547943478963 0.3981081129727045 0 0 7.352941176470589 25.83231575299851 ITGB1;CAV1 -GO_Biological_Process_2018 branching involved in ureteric bud morphogenesis (GO:0001658) 2/20 0.029801547943478963 0.3970686662025408 0 0 7.352941176470589 25.83231575299851 NPNT;PKD2 -GO_Biological_Process_2018 chondroitin sulfate biosynthetic process (GO:0030206) 2/20 0.029801547943478963 0.3960346332176383 0 0 7.352941176470589 25.83231575299851 VCAN;BGN -GO_Biological_Process_2018 regulation of peptidyl-serine phosphorylation (GO:0033135) 4/86 0.029834687460171982 0.3954452210630069 0 0 3.4199726402188784 12.011229663724642 APP;CAV1;GAS6;PFN2 -GO_Biological_Process_2018 positive regulation of protein kinase B signaling (GO:0051897) 5/127 0.029884563560266763 0.39508012395865616 0 0 2.8948587308939326 10.162150303339404 PDGFRB;ITGB1;PDGFRA;GAS6;FGFR1 -GO_Biological_Process_2018 negative regulation of response to stimulus (GO:0048585) 5/127 0.029884563560266763 0.394059245085378 0 0 2.8948587308939326 10.162150303339404 PDGFRB;PDGFRA;IGFBP3;CLU;FGFR1 -GO_Biological_Process_2018 positive regulation of hydrolase activity (GO:0051345) 6/172 0.030477028545596464 0.4008357646087081 0 0 2.5649794801641588 8.953784306371976 NET1;ITGB1;CAV2;TPM1;DNAJC10;PFN2 -GO_Biological_Process_2018 regulation of blood vessel endothelial cell migration (GO:0043535) 3/50 0.03050199610581913 0.4001328692236377 0 0 4.411764705882354 15.396896256255191 MEF2C;HSPB1;FGFR1 -GO_Biological_Process_2018 positive regulation of cell migration (GO:0030335) 7/221 0.0319296695891473 0.4177874459318428 0 0 2.328985892999734 8.021538908035103 PDGFRB;PDGFRA;CDH13;ACTN4;CYR61;MYLK;CLASP2 -GO_Biological_Process_2018 negative regulation of Wnt signaling pathway (GO:0030178) 6/174 0.031988679210138225 0.4174890793077631 0 0 2.5354969574036508 8.728126808972155 SFRP4;FRZB;CAV1;IGFBP2;IGFBP6;DKK3 -GO_Biological_Process_2018 positive regulation of cellular component movement (GO:0051272) 2/21 0.03264804495619472 0.4250075852333205 0 0 7.002801120448179 23.963377478170052 CAVIN1;ACTN4 -GO_Biological_Process_2018 cortical actin cytoskeleton organization (GO:0030866) 2/21 0.03264804495619472 0.4239261409960856 0 0 7.002801120448179 23.963377478170052 NCKAP1;ROCK1 -GO_Biological_Process_2018 phospholipid translocation (GO:0045332) 2/21 0.03264804495619472 0.4228501863235066 0 0 7.002801120448179 23.963377478170052 TMEM30A;ATP8B1 -GO_Biological_Process_2018 vesicle docking (GO:0048278) 2/21 0.03264804495619472 0.4217796795226876 0 0 7.002801120448179 23.963377478170052 CAV2;USO1 -GO_Biological_Process_2018 regulation of cell-matrix adhesion (GO:0001952) 3/52 0.033724235452394134 0.434582761397897 0 0 4.242081447963801 14.378698588236634 JAG1;ROCK1;CDH13 -GO_Biological_Process_2018 positive regulation of cell-substrate adhesion (GO:0010811) 3/52 0.033724235452394134 0.4334880944926127 0 0 4.242081447963801 14.378698588236634 CDH13;FLNA;NPNT -GO_Biological_Process_2018 positive regulation of phosphatidylinositol 3-kinase signaling (GO:0014068) 3/53 0.0353990471703743 0.4538727078151258 0 0 4.16204217536071 13.90567581282925 PDGFRB;PDGFRA;FGFR1 -GO_Biological_Process_2018 negative regulation of cellular response to transforming growth factor beta stimulus (GO:1903845) 3/53 0.0353990471703743 0.4527351822316292 0 0 4.16204217536071 13.90567581282925 CAV1;SKIL;ASPN -GO_Biological_Process_2018 positive regulation of cytosolic calcium ion concentration (GO:0007204) 5/133 0.03542561656163188 0.4519423032850187 0 0 2.7642636001769127 9.233525234556224 PDGFRA;CACNA2D1;CAV1;NPTN;CD55 -GO_Biological_Process_2018 lipid translocation (GO:0034204) 2/22 0.03559647586046295 0.4529895668726744 0 0 6.6844919786096275 22.296180740025918 TMEM30A;ATP8B1 -GO_Biological_Process_2018 negative regulation of cell cycle G1/S phase transition (GO:1902807) 2/22 0.03559647586046295 0.4518627271540857 0 0 6.6844919786096275 22.296180740025918 FHL1;PKD2 -GO_Biological_Process_2018 negative regulation of actin filament polymerization (GO:0030837) 2/22 0.03559647586046295 0.4507414796921649 0 0 6.6844919786096275 22.296180740025918 GSN;PFN2 -GO_Biological_Process_2018 regulation of microtubule depolymerization (GO:0031114) 2/22 0.03559647586046295 0.4496257829602536 0 0 6.6844919786096275 22.296180740025918 MAP1B;CLASP2 -GO_Biological_Process_2018 regulation of membrane protein ectodomain proteolysis (GO:0051043) 2/22 0.03559647586046295 0.4485155958418332 0 0 6.6844919786096275 22.296180740025918 ROCK1;TIMP2 -GO_Biological_Process_2018 negative regulation of protein import into nucleus (GO:0042308) 2/22 0.03559647586046295 0.44741087762547405 0 0 6.6844919786096275 22.296180740025918 NOV;GAS6 -GO_Biological_Process_2018 viral genome replication (GO:0019079) 2/22 0.03559647586046295 0.4463115879998586 0 0 6.6844919786096275 22.296180740025918 NFIA;GAS6 -GO_Biological_Process_2018 phagocytosis (GO:0006909) 5/135 0.037404344976808335 0.4678293441584631 0 0 2.7233115468409586 8.948715701141207 GSN;MYH9;GAS6;TGM2;GULP1 -GO_Biological_Process_2018 cellular transition metal ion homeostasis (GO:0046916) 4/93 0.03815200156257482 0.4760138483467465 0 0 3.162555344718533 10.329465708506172 APP;CYBRD1;SKP1;SOD1 -GO_Biological_Process_2018 cell-cell adhesion mediated by cadherin (GO:0044331) 2/23 0.03864356360448186 0.4809709879845634 0 0 6.3938618925831205 20.801630742336886 CDH11;CDH13 -GO_Biological_Process_2018 lamellipodium assembly (GO:0030032) 2/23 0.03864356360448186 0.4798007422717056 0 0 6.3938618925831205 20.801630742336886 CDH13;VCL -GO_Biological_Process_2018 membrane assembly (GO:0071709) 2/23 0.03864356360448186 0.4786361773632791 0 0 6.3938618925831205 20.801630742336886 ANK3;SPTBN1 -GO_Biological_Process_2018 cellular response to interleukin-6 (GO:0071354) 2/23 0.03864356360448186 0.4774772519943607 0 0 6.3938618925831205 20.801630742336886 PTGIS;IL6ST -GO_Biological_Process_2018 divalent inorganic cation homeostasis (GO:0072507) 2/23 0.03864356360448186 0.4763239252987221 0 0 6.3938618925831205 20.801630742336886 CAV1;ANK3 -GO_Biological_Process_2018 negative regulation of transcription factor import into nucleus (GO:0042992) 2/23 0.03864356360448186 0.4751761568040264 0 0 6.3938618925831205 20.801630742336886 NOV;PKD2 -GO_Biological_Process_2018 cellular response to amyloid-beta (GO:1904646) 2/23 0.03864356360448186 0.4740339064270937 0 0 6.3938618925831205 20.801630742336886 APP;CACNA2D1 -GO_Biological_Process_2018 regulation of microtubule cytoskeleton organization (GO:0070507) 2/23 0.03864356360448186 0.4728971344692349 0 0 6.3938618925831205 20.801630742336886 PHLDB2;CLASP2 -GO_Biological_Process_2018 positive regulation of cell junction assembly (GO:1901890) 2/23 0.03864356360448186 0.471765801611653 0 0 6.3938618925831205 20.801630742336886 ROCK1;CAV1 -GO_Biological_Process_2018 regulation of cytosolic calcium ion concentration (GO:0051480) 5/137 0.039449647163154085 0.4804571586481511 0 0 2.683555173894375 8.675209788616414 PDGFRA;PLN;CAV1;NPTN;CD55 -GO_Biological_Process_2018 chordate embryonic development (GO:0043009) 3/56 0.0406758475329435 0.4942115475252635 0 0 3.9390756302521006 12.613395966609426 MBNL1;PKD2;FGFR1 -GO_Biological_Process_2018 regulation of BMP signaling pathway (GO:0030510) 3/56 0.0406758475329435 0.4930376483624957 0 0 3.9390756302521006 12.613395966609426 SFRP4;SKIL;CYR61 -GO_Biological_Process_2018 cellular iron ion homeostasis (GO:0006879) 3/56 0.0406758475329435 0.4918693127028689 0 0 3.9390756302521006 12.613395966609426 CYBRD1;SKP1;SOD1 -GO_Biological_Process_2018 positive regulation of protein localization to membrane (GO:1905477) 4/95 0.040748459341949664 0.4915824775933077 0 0 3.095975232198142 9.908164853156368 ITGB1;ANK3;SORBS1;SPTBN1 -GO_Biological_Process_2018 Golgi to plasma membrane protein transport (GO:0043001) 2/24 0.041786100829854456 0.5029114918272342 0 0 6.127450980392156 19.455830335657662 ANK3;SPTBN1 -GO_Biological_Process_2018 negative regulation of G1/S transition of mitotic cell cycle (GO:2000134) 2/24 0.041786100829854456 0.5017281706699936 0 0 6.127450980392156 19.455830335657662 FHL1;PKD2 -GO_Biological_Process_2018 regulation of chemotaxis (GO:0050920) 2/24 0.041786100829854456 0.5005504050111439 0 0 6.127450980392156 19.455830335657662 PDGFRB;PDGFRA -GO_Biological_Process_2018 IRE1-mediated unfolded protein response (GO:0036498) 3/57 0.04251812503144064 0.5081264450478725 0 0 3.8699690402476783 12.220684298130033 TLN1;ADD1;SEC31A -GO_Biological_Process_2018 positive regulation of cell death (GO:0010942) 3/57 0.04251812503144064 0.5069392337276672 0 0 3.8699690402476783 12.220684298130033 HBB;HBA1;CLU -GO_Biological_Process_2018 positive regulation of Wnt signaling pathway (GO:0030177) 5/140 0.04264317863161767 0.5072450828837879 0 0 2.6260504201680672 8.284894842787029 SFRP4;TBL1XR1;CAV1;USP34;ILK -GO_Biological_Process_2018 cellular response to organonitrogen compound (GO:0071417) 4/97 0.04344296444589789 0.5155568548079463 0 0 3.032140691328078 9.509722144863476 MEF2C;IGFBP5;PKD2;AQP1 -GO_Biological_Process_2018 regulation of epidermal growth factor receptor signaling pathway (GO:0042058) 3/58 0.04440164153984588 0.5257113150297761 0 0 3.803245436105477 11.845127428787684 APP;ITGA1;CDH13 -GO_Biological_Process_2018 positive regulation of MAPK cascade (GO:0043410) 8/289 0.04466980892172881 0.5276621178879216 0 0 2.035416242621616 6.327004724567028 PDGFRB;APP;PDGFRA;CAV2;GAS6;NPNT;CTGF;FGFR1 -GO_Biological_Process_2018 positive regulation of neuron differentiation (GO:0045666) 4/98 0.04482696991992201 0.5282956755227759 0 0 3.0012004801920766 9.318563366007467 MEF2C;ZEB1;NPTN;FGFR1 -GO_Biological_Process_2018 cell-cell adhesion via plasma-membrane adhesion molecules (GO:0098742) 5/142 0.044856332492398635 0.5274236514025582 0 0 2.589063794531897 8.03720616132058 ITGB1;ALCAM;CDH11;NPTN;CDH13 -GO_Biological_Process_2018 negative regulation of wound healing (GO:0061045) 2/25 0.04502094860414535 0.5281423005217327 0 0 5.882352941176472 18.238984547048144 PHLDB2;CLASP2 -GO_Biological_Process_2018 positive regulation of phosphatidylinositol 3-kinase activity (GO:0043552) 2/25 0.04502094860414535 0.5269309649700774 0 0 5.882352941176472 18.238984547048144 PDGFRB;PDGFRA -GO_Biological_Process_2018 response to amyloid-beta (GO:1904645) 2/25 0.04502094860414535 0.5257251732882237 0 0 5.882352941176472 18.238984547048144 APP;CACNA2D1 -GO_Biological_Process_2018 chondroitin sulfate proteoglycan biosynthetic process (GO:0050650) 2/25 0.04502094860414535 0.5245248875044606 0 0 5.882352941176472 18.238984547048144 VCAN;BGN -GO_Biological_Process_2018 positive regulation of glucose transport (GO:0010828) 2/25 0.04502094860414535 0.5233300699930609 0 0 5.882352941176472 18.238984547048144 ITLN1;SORBS1 -GO_Biological_Process_2018 cellular response to vascular endothelial growth factor stimulus (GO:0035924) 2/25 0.04502094860414535 0.5221406834703494 0 0 5.882352941176472 18.238984547048144 FOXC1;HSPB1 -GO_Biological_Process_2018 protein depolymerization (GO:0051261) 2/25 0.04502094860414535 0.5209566909908248 0 0 5.882352941176472 18.238984547048144 DSTN;MICAL2 -GO_Biological_Process_2018 regulation of nervous system development (GO:0051960) 2/25 0.04502094860414535 0.5197780559433343 0 0 5.882352941176472 18.238984547048144 MEF2C;PDLIM5 -GO_Biological_Process_2018 positive regulation of embryonic development (GO:0040019) 2/25 0.04502094860414535 0.5186047420472997 0 0 5.882352941176472 18.238984547048144 PHLDB2;CLASP2 -GO_Biological_Process_2018 negative regulation of transcription from RNA polymerase II promoter (GO:0000122) 13/565 0.046102694729354896 0.5298694846934641 0 0 1.691827173347215 5.205555751816171 MEF2A;FOXC1;MEF2C;CAV1;ATRX;NR2F2;AEBP1;EID1;ZEB1;NFIA;NFIB;TBL1XR1;SKIL -GO_Biological_Process_2018 regulation of cell proliferation (GO:0042127) 16/740 0.046526981726366455 0.5335442421340405 0 0 1.5898251192368842 4.877142897440549 PDGFRB;APP;PDGFRA;JAG1;IGFBP3;ITGA1;TNFRSF11B;PKD2;SFRP4;FRZB;ADAMTS1;CDH13;IGFBP7;IGFBP6;IL6ST;FGFR1 -GO_Biological_Process_2018 neuron development (GO:0048666) 5/144 0.04713705439656566 0.5393282255284183 0 0 2.553104575163399 7.798958000690974 APP;MEF2C;ROCK1;CDH11;SKIL -GO_Biological_Process_2018 cell chemotaxis (GO:0060326) 3/60 0.04829123688356071 0.5512979458989045 0 0 3.6764705882352944 11.141563109435227 PDGFRB;PDGFRA;NOV -GO_Biological_Process_2018 positive regulation of glucose import (GO:0046326) 2/26 0.04834503516990993 0.5506801662322552 0 0 5.656108597285066 17.134568707970473 ITLN1;SORBS1 -GO_Biological_Process_2018 apoptotic cell clearance (GO:0043277) 2/26 0.04834503516990993 0.5494537070647002 0 0 5.656108597285066 17.134568707970473 GAS6;TGM2 -GO_Biological_Process_2018 regulation of NF-kappaB import into nucleus (GO:0042345) 2/26 0.04834503516990993 0.5482326988267786 0 0 5.656108597285066 17.134568707970473 APP;NOV -GO_Biological_Process_2018 negative regulation of catalytic activity (GO:0043086) 2/26 0.04834503516990993 0.5470171052595352 0 0 5.656108597285066 17.134568707970473 PLN;PPP1R12A -GO_Biological_Process_2018 regulation of cardiac muscle cell contraction (GO:0086004) 2/26 0.04834503516990993 0.5458068904248902 0 0 5.656108597285066 17.134568707970473 PLN;CAV1 -GO_Biological_Process_2018 regulation of microtubule-based process (GO:0032886) 2/26 0.04834503516990993 0.5446020187020979 0 0 5.656108597285066 17.134568707970473 PHLDB2;CLASP2 -GO_Biological_Process_2018 protein processing (GO:0016485) 4/101 0.04912575930863068 0.5521778628897408 0 0 2.9120559114735003 8.775107024600839 CPE;AEBP1;GAS6;OGT -GO_Biological_Process_2018 regulation of kinase activity (GO:0043549) 4/101 0.04912575930863068 0.550964285169104 0 0 2.9120559114735003 8.775107024600839 PDGFRB;PDGFRA;FGFR1;SOD1 -GO_Biological_Process_2018 glycosaminoglycan metabolic process (GO:0030203) 3/61 0.0502966999391162 0.5628597802397147 0 0 3.616200578592093 10.811773667795167 VCAN;BGN;HSPG2 -GO_Biological_Process_2018 positive regulation of cellular amide metabolic process (GO:0034250) 3/61 0.0502966999391162 0.561628139582735 0 0 3.616200578592093 10.811773667795167 APP;FAM129A;CLU -GO_Biological_Process_2018 positive regulation of lipid kinase activity (GO:0090218) 2/27 0.05175535469239772 0.576654093876213 0 0 5.446623093681917 16.128689432857758 PDGFRB;PDGFRA -GO_Biological_Process_2018 leukocyte cell-cell adhesion (GO:0007159) 2/27 0.05175535469239772 0.5753977668743041 0 0 5.446623093681917 16.128689432857758 ITGB1;ROCK1 -GO_Biological_Process_2018 regulation of actin filament-based process (GO:0032970) 3/62 0.05234217179812843 0.5806567449692378 0 0 3.5578747628083494 10.495562934177757 PDGFRB;ROCK1;RHOB -GO_Biological_Process_2018 negative regulation of transcription, DNA-templated (GO:0045892) 17/813 0.052976123316546025 0.5864146578835886 0 0 1.5375153751537518 4.517087900366826 MEF2A;FOXC1;MEF2C;ATP8B1;CAV1;NR2F2;AEBP1;DKK3;EID1;ZEB1;NFIA;NFIB;TBL1XR1;ID4;FLNA;GAS6;SKIL -GO_Biological_Process_2018 peptide metabolic process (GO:0006518) 4/104 0.05364397094118035 0.5925220426684921 0 0 2.828054298642533 8.273151000653481 CPE;AEBP1;GAS6;SOD1 -GO_Biological_Process_2018 iron ion homeostasis (GO:0055072) 3/63 0.054427309159066295 0.5998759365846983 0 0 3.5014005602240896 10.192189231382756 CYBRD1;SKP1;SOD1 -GO_Biological_Process_2018 regulation of proteolysis (GO:0030162) 3/63 0.054427309159066295 0.5985831005144726 0 0 3.5014005602240896 10.192189231382756 CAV1;LTBP4;OGT -GO_Biological_Process_2018 negative regulation of dephosphorylation (GO:0035305) 3/63 0.054427309159066295 0.5972958250294953 0 0 3.5014005602240896 10.192189231382756 MYO1D;PPP1R12A;PPP1R12B -GO_Biological_Process_2018 positive regulation of phosphate metabolic process (GO:0045937) 2/28 0.05524896606215785 0.605011746384531 0 0 5.2521008403361344 15.209588511378561 APP;ILK -GO_Biological_Process_2018 positive regulation of phospholipid metabolic process (GO:1903727) 2/28 0.05524896606215785 0.603716218019682 0 0 5.2521008403361344 15.209588511378561 PDGFRB;PDGFRA -GO_Biological_Process_2018 positive regulation of cell communication (GO:0010647) 2/28 0.05524896606215785 0.6024262261008365 0 0 5.2521008403361344 15.209588511378561 ILK;ANK3 -GO_Biological_Process_2018 cardiac muscle cell action potential involved in contraction (GO:0086002) 2/28 0.05524896606215785 0.6011417352136279 0 0 5.2521008403361344 15.209588511378561 GJA1;CACNA2D1 -GO_Biological_Process_2018 cGMP metabolic process (GO:0046068) 2/28 0.05524896606215785 0.5998627102450882 0 0 5.2521008403361344 15.209588511378561 PDE5A;AQP1 -GO_Biological_Process_2018 regulation of actin polymerization or depolymerization (GO:0008064) 2/28 0.05524896606215785 0.598589116380449 0 0 5.2521008403361344 15.209588511378561 EPS8;PFN2 -GO_Biological_Process_2018 positive regulation of endocytosis (GO:0045807) 3/64 0.05655175569290019 0.6114059519086222 0 0 3.4466911764705883 9.900961730812195 SGIP1;GAS6;CLU -GO_Biological_Process_2018 cellular response to decreased oxygen levels (GO:0036294) 3/64 0.05655175569290019 0.6101133389024729 0 0 3.4466911764705883 9.900961730812195 PTGIS;EPAS1;AQP1 -GO_Biological_Process_2018 positive regulation of cell adhesion (GO:0045785) 3/64 0.05655175569290019 0.6088261799596407 0 0 3.4466911764705883 9.900961730812195 TPM1;NPNT;TGM2 -GO_Biological_Process_2018 positive regulation of intracellular protein transport (GO:0090316) 5/152 0.056938149016757984 0.6116955251210863 0 0 2.418730650154799 6.9315733970331275 LEPROT;TMEM30A;TOMM7;ANK3;GAS6 -GO_Biological_Process_2018 protein stabilization (GO:0050821) 5/152 0.056938149016757984 0.610410450488479 0 0 2.418730650154799 6.9315733970331275 CRTAP;FLNA;CLU;CRYAB;PFN2 -GO_Biological_Process_2018 positive regulation of cell cycle process (GO:0090068) 4/107 0.05838021751944545 0.624558176104256 0 0 2.7487630566245187 7.80862613424703 PDGFRB;SVIL;ATRX;PKD2 -GO_Biological_Process_2018 cortical cytoskeleton organization (GO:0030865) 2/29 0.05882299169862056 0.6279785076110057 0 0 5.0709939148073016 14.367253981853178 NCKAP1;ROCK1 -GO_Biological_Process_2018 regulation of keratinocyte differentiation (GO:0045616) 2/29 0.05882299169862056 0.6266674877621309 0 0 5.0709939148073016 14.367253981853178 FOXC1;ROCK1 -GO_Biological_Process_2018 regulation of myoblast differentiation (GO:0045661) 2/29 0.05882299169862056 0.6253619304959598 0 0 5.0709939148073016 14.367253981853178 MEF2C;IGFBP3 -GO_Biological_Process_2018 regulation of MAPK cascade (GO:0043408) 6/203 0.05948783575583633 0.6311152304823966 0 0 2.1732831063459868 6.132968911351478 PDGFRB;PDGFRA;CAV2;CAV1;CYR61;FGFR1 -GO_Biological_Process_2018 negative regulation of hydrolase activity (GO:0051346) 4/108 0.0600071056022745 0.6353034437518813 0 0 2.7233115468409586 7.661471397242237 MYO1D;PPP1R12A;PLN;PPP1R12B -GO_Biological_Process_2018 negative regulation of protein kinase activity (GO:0006469) 4/109 0.061657931369124376 0.6514294488129229 0 0 2.698327037236913 7.517953065513713 CAV1;BGN;HSPB1;ASPN -GO_Biological_Process_2018 replication fork processing (GO:0031297) 2/30 0.062474616370412475 0.658694147393006 0 0 4.901960784313727 13.593112464752874 ATRX;NUCKS1 -GO_Biological_Process_2018 cellular response to drug (GO:0035690) 2/30 0.062474616370412475 0.6573360151303399 0 0 4.901960784313727 13.593112464752874 MEF2C;GAS6 -GO_Biological_Process_2018 positive regulation of cation channel activity (GO:2001259) 2/30 0.062474616370412475 0.655983471889331 0 0 4.901960784313727 13.593112464752874 ANK3;PKD2 -GO_Biological_Process_2018 regulation of protein polymerization (GO:0032271) 2/30 0.062474616370412475 0.6546364832406877 0 0 4.901960784313727 13.593112464752874 EPS8;PFN2 -GO_Biological_Process_2018 chondroitin sulfate metabolic process (GO:0030204) 2/30 0.062474616370412475 0.6532950150373256 0 0 4.901960784313727 13.593112464752874 VCAN;BGN -GO_Biological_Process_2018 regulation of protein localization to plasma membrane (GO:1903076) 3/67 0.06315720466731561 0.6590822401172016 0 0 3.2923617208077256 9.093925642667953 ITGB1;SORBS1;SPTBN1 -GO_Biological_Process_2018 regulation of protein binding (GO:0043393) 4/111 0.065031038663524 0.6772518169386998 0 0 2.649708532061473 7.241363553687125 APP;ROCK1;CAV1;ADD1 -GO_Biological_Process_2018 regulation of epithelial to mesenchymal transition (GO:0010717) 3/68 0.0654350855056677 0.6800717746138947 0 0 3.2439446366782008 8.84525309931473 FOXC1;PHLDB2;CLASP2 -GO_Biological_Process_2018 negative regulation of phosphatase activity (GO:0010923) 3/68 0.0654350855056677 0.6786895149093949 0 0 3.2439446366782008 8.84525309931473 MYO1D;PPP1R12A;PPP1R12B -GO_Biological_Process_2018 regulation of dendritic spine development (GO:0060998) 2/31 0.06620108602845774 0.6852416673493303 0 0 4.743833017077799 12.87978373296977 MEF2C;PDLIM5 -GO_Biological_Process_2018 regulation of glucose import (GO:0046324) 2/31 0.06620108602845774 0.6838545384680563 0 0 4.743833017077799 12.87978373296977 ITLN1;SORBS1 -GO_Biological_Process_2018 positive regulation of protein dephosphorylation (GO:0035307) 2/31 0.06620108602845774 0.6824730141479188 0 0 4.743833017077799 12.87978373296977 PDGFRB;ITGA1 -GO_Biological_Process_2018 anterograde axonal transport (GO:0008089) 2/31 0.06620108602845774 0.6810970604903626 0 0 4.743833017077799 12.87978373296977 HSPB1;SOD1 -GO_Biological_Process_2018 regulation of endothelial cell migration (GO:0010594) 3/69 0.06775031957630176 0.6956335629735773 0 0 3.1969309462915603 8.605901861381293 SPARC;NR2F2;RHOB -GO_Biological_Process_2018 negative regulation of calcium ion transmembrane transporter activity (GO:1901020) 2/32 0.06999970672933856 0.7172861514855716 0 0 4.595588235294118 12.220883393945002 PLN;PKD2 -GO_Biological_Process_2018 protein deacetylation (GO:0006476) 2/32 0.06999970672933856 0.7158487042882057 0 0 4.595588235294118 12.220883393945002 TBL1XR1;MORF4L2 -GO_Biological_Process_2018 regulation of glutamate receptor signaling pathway (GO:1900449) 2/32 0.06999970672933856 0.7144170068796293 0 0 4.595588235294118 12.220883393945002 APP;MEF2C -GO_Biological_Process_2018 protein trimerization (GO:0070206) 2/32 0.06999970672933856 0.7129910248299693 0 0 4.595588235294118 12.220883393945002 COL6A1;ITLN1 -GO_Biological_Process_2018 negative regulation of receptor activity (GO:2000272) 2/32 0.06999970672933856 0.7115707239836945 0 0 4.595588235294118 12.220883393945002 CAV1;PKD2 -GO_Biological_Process_2018 positive regulation of transcription factor import into nucleus (GO:0042993) 2/32 0.06999970672933856 0.7101560704568879 0 0 4.595588235294118 12.220883393945002 APP;FLNA -GO_Biological_Process_2018 vasculogenesis (GO:0001570) 2/32 0.06999970672933856 0.7087470306345529 0 0 4.595588235294118 12.220883393945002 PDGFRB;CAV1 -GO_Biological_Process_2018 vascular endothelial growth factor receptor signaling pathway (GO:0048010) 3/70 0.07010248478631834 0.7083821383457078 0 0 3.151260504201681 8.375410838004138 NCKAP1;ROCK1;HSPB1 -GO_Biological_Process_2018 negative regulation of developmental process (GO:0051093) 3/70 0.07010248478631834 0.7069821736454199 0 0 3.151260504201681 8.375410838004138 NFIB;GAS6;SKIL -GO_Biological_Process_2018 epithelium development (GO:0060429) 4/115 0.07206026280193661 0.7252929409828058 0 0 2.557544757033248 6.726988559286699 TAGLN;PKD2;VCL;CNN3 -GO_Biological_Process_2018 cellular response to oxidative stress (GO:0034599) 4/115 0.07206026280193661 0.7238651989729972 0 0 2.557544757033248 6.726988559286699 PDGFRA;TPM1;PKD2;SOD1 -GO_Biological_Process_2018 regulation of protein kinase B signaling (GO:0051896) 5/163 0.07218466930387776 0.7236903093471282 0 0 2.255503428365212 5.92865299562733 PDGFRB;ITGB1;PDGFRA;GAS6;FGFR1 -GO_Biological_Process_2018 response to insulin (GO:0032868) 3/71 0.07249114995254738 0.7253379180546065 0 0 3.1068765534382767 8.153347537881048 SORT1;SORBS1;OGT -GO_Biological_Process_2018 positive regulation of cell-matrix adhesion (GO:0001954) 2/33 0.07386784343837091 0.7376665461174301 0 0 4.456327985739751 11.610863109978338 ROCK1;CDH13 -GO_Biological_Process_2018 positive regulation of intrinsic apoptotic signaling pathway (GO:2001244) 2/33 0.07386784343837091 0.7362257911445445 0 0 4.456327985739751 11.610863109978338 CAV1;SOD1 -GO_Biological_Process_2018 embryonic organ morphogenesis (GO:0048562) 2/33 0.07386784343837091 0.7347906531501106 0 0 4.456327985739751 11.610863109978338 PDGFRA;EFEMP1 -GO_Biological_Process_2018 positive regulation of catalytic activity (GO:0043085) 2/33 0.07386784343837091 0.7333610993502077 0 0 4.456327985739751 11.610863109978338 CAV1;SOD1 -GO_Biological_Process_2018 carboxylic acid metabolic process (GO:0019752) 2/33 0.07386784343837091 0.7319370972155471 0 0 4.456327985739751 11.610863109978338 CYB5R3;PTGIS -GO_Biological_Process_2018 regulation of phosphatidylinositol 3-kinase activity (GO:0043551) 2/33 0.07386784343837091 0.7305186144690053 0 0 4.456327985739751 11.610863109978338 PDGFRB;PDGFRA -GO_Biological_Process_2018 positive regulation of Notch signaling pathway (GO:0045747) 2/33 0.07386784343837091 0.7291056190831853 0 0 4.456327985739751 11.610863109978338 JAG1;NOV -GO_Biological_Process_2018 positive regulation of locomotion (GO:0040017) 2/33 0.07386784343837091 0.7276980792780053 0 0 4.456327985739751 11.610863109978338 PDGFRB;CAVIN1 -GO_Biological_Process_2018 regulation of neurotransmitter receptor activity (GO:0099601) 2/33 0.07386784343837091 0.7262959635183175 0 0 4.456327985739751 11.610863109978338 APP;MEF2C -GO_Biological_Process_2018 regulation of phosphatase activity (GO:0010921) 3/72 0.07491587537354344 0.7351840615984465 0 0 3.063725490196078 7.9393059334104645 MYO1D;PPP1R12A;PPP1R12B -GO_Biological_Process_2018 negative regulation of cell cycle (GO:0045786) 3/72 0.07491587537354344 0.7337729597527681 0 0 3.063725490196078 7.9393059334104645 FOXC1;NR2F2;RHOB -GO_Biological_Process_2018 canonical Wnt signaling pathway (GO:0060070) 3/73 0.07737621324136428 0.7564191880664405 0 0 3.0217566478646254 7.732904516768762 SFRP4;FRZB;CAV1 -GO_Biological_Process_2018 calcium ion transport (GO:0006816) 4/118 0.07757622931594135 0.7569244707442614 0 0 2.4925224327018944 6.372119197327247 CACNA2D1;CAV1;PKD2;GAS6 -GO_Biological_Process_2018 regulation of wound healing (GO:0061041) 2/34 0.07780291900524121 0.7576875871827212 0 0 4.3252595155709335 11.044880316410254 CAV1;MYLK -GO_Biological_Process_2018 regulation of insulin receptor signaling pathway (GO:0046626) 2/34 0.07780291900524121 0.7562443727309446 0 0 4.3252595155709335 11.044880316410254 NUCKS1;OGT -GO_Biological_Process_2018 negative regulation of endothelial cell migration (GO:0010596) 2/34 0.07780291900524121 0.7548066457865892 0 0 4.3252595155709335 11.044880316410254 MEF2C;NR2F2 -GO_Biological_Process_2018 neuron projection development (GO:0031175) 5/167 0.07823204043823175 0.7575296059891776 0 0 2.2014793941528707 5.609536789078194 APP;NCKAP1;CTTN;ROCK1;CDH11 -GO_Biological_Process_2018 regulation of glomerular filtration (GO:0003093) 1/6 0.07888463821035145 0.7624020999761808 0 0 12.254901960784313 31.124617274206983 GAS6 -GO_Biological_Process_2018 protein repair (GO:0030091) 1/6 0.07888463821035145 0.7609608861766041 0 0 12.254901960784313 31.124617274206983 MSRB3 -GO_Biological_Process_2018 smooth muscle tissue development (GO:0048745) 1/6 0.07888463821035145 0.7595251109196669 0 0 12.254901960784313 31.124617274206983 MYLK -GO_Biological_Process_2018 response to chemokine (GO:1990868) 1/6 0.07888463821035145 0.7580947434791403 0 0 12.254901960784313 31.124617274206983 FOXC1 -GO_Biological_Process_2018 positive regulation of adaptive immune response (GO:0002821) 1/6 0.07888463821035145 0.7566697533598186 0 0 12.254901960784313 31.124617274206983 IL6ST -GO_Biological_Process_2018 regulation of macrophage apoptotic process (GO:2000109) 1/6 0.07888463821035145 0.7552501102953536 0 0 12.254901960784313 31.124617274206983 MEF2C -GO_Biological_Process_2018 neuron projection maintenance (GO:1990535) 1/6 0.07888463821035145 0.7538357842461114 0 0 12.254901960784313 31.124617274206983 APP -GO_Biological_Process_2018 regulation of histone H3-K27 methylation (GO:0061085) 1/6 0.07888463821035145 0.7524267453970532 0 0 12.254901960784313 31.124617274206983 OGT -GO_Biological_Process_2018 regulation of endothelial cell development (GO:1901550) 1/6 0.07888463821035145 0.7510229641556408 0 0 12.254901960784313 31.124617274206983 ROCK1 -GO_Biological_Process_2018 cellular response to chemokine (GO:1990869) 1/6 0.07888463821035145 0.7496244111497644 0 0 12.254901960784313 31.124617274206983 FOXC1 -GO_Biological_Process_2018 regulation of protein depolymerization (GO:1901879) 1/6 0.07888463821035145 0.7482310572256942 0 0 12.254901960784313 31.124617274206983 MAP1B -GO_Biological_Process_2018 regulation of non-motile cilium assembly (GO:1902855) 1/6 0.07888463821035145 0.7468428734460547 0 0 12.254901960784313 31.124617274206983 SEPT7 -GO_Biological_Process_2018 B cell chemotaxis (GO:0035754) 1/6 0.07888463821035145 0.7454598310878212 0 0 12.254901960784313 31.124617274206983 GAS6 -GO_Biological_Process_2018 SCF complex assembly (GO:0010265) 1/6 0.07888463821035145 0.7440819016403392 0 0 12.254901960784313 31.124617274206983 SKP1 -GO_Biological_Process_2018 regulation of extent of cell growth (GO:0061387) 1/6 0.07888463821035145 0.7427090568033644 0 0 12.254901960784313 31.124617274206983 CTTN -GO_Biological_Process_2018 determination of liver left/right asymmetry (GO:0071910) 1/6 0.07888463821035145 0.7413412684851262 0 0 12.254901960784313 31.124617274206983 PKD2 -GO_Biological_Process_2018 cellular hyperosmotic response (GO:0071474) 1/6 0.07888463821035145 0.7399785088004108 0 0 12.254901960784313 31.124617274206983 AQP1 -GO_Biological_Process_2018 regulation of superoxide metabolic process (GO:0090322) 1/6 0.07888463821035145 0.7386207500686669 0 0 12.254901960784313 31.124617274206983 FBLN5 -GO_Biological_Process_2018 regulation of nuclear cell cycle DNA replication (GO:0033262) 1/6 0.07888463821035145 0.7372679648121309 0 0 12.254901960784313 31.124617274206983 ATRX -GO_Biological_Process_2018 positive regulation of amyloid-beta clearance (GO:1900223) 1/6 0.07888463821035145 0.7359201257539735 0 0 12.254901960784313 31.124617274206983 ROCK1 -GO_Biological_Process_2018 ventricular cardiac muscle cell development (GO:0055015) 1/6 0.07888463821035145 0.7345772058164662 0 0 12.254901960784313 31.124617274206983 MEF2A -GO_Biological_Process_2018 establishment of endothelial intestinal barrier (GO:0090557) 1/6 0.07888463821035145 0.7332391781191685 0 0 12.254901960784313 31.124617274206983 TJP1 -GO_Biological_Process_2018 cardiac ventricle formation (GO:0003211) 1/6 0.07888463821035145 0.7319060159771336 0 0 12.254901960784313 31.124617274206983 MEF2C -GO_Biological_Process_2018 negative regulation of endoplasmic reticulum calcium ion concentration (GO:0032471) 1/6 0.07888463821035145 0.7305776928991352 0 0 12.254901960784313 31.124617274206983 TGM2 -GO_Biological_Process_2018 positive regulation of cardiac muscle tissue development (GO:0055025) 1/6 0.07888463821035145 0.7292541825859121 0 0 12.254901960784313 31.124617274206983 MEF2C -GO_Biological_Process_2018 protein localization to endoplasmic reticulum exit site (GO:0070973) 1/6 0.07888463821035145 0.7279354589284329 0 0 12.254901960784313 31.124617274206983 BCAP29 -GO_Biological_Process_2018 negative regulation of neurological system process (GO:0031645) 1/6 0.07888463821035145 0.7266214960061795 0 0 12.254901960784313 31.124617274206983 NOV -GO_Biological_Process_2018 retinal ganglion cell axon guidance (GO:0031290) 1/6 0.07888463821035145 0.7253122680854477 0 0 12.254901960784313 31.124617274206983 ALCAM -GO_Biological_Process_2018 regulation of saliva secretion (GO:0046877) 1/6 0.07888463821035145 0.7240077496176681 0 0 12.254901960784313 31.124617274206983 AQP1 -GO_Biological_Process_2018 positive regulation of osteoblast proliferation (GO:0033690) 1/6 0.07888463821035145 0.7227079152377441 0 0 12.254901960784313 31.124617274206983 CYR61 -GO_Biological_Process_2018 gap junction assembly (GO:0016264) 1/6 0.07888463821035145 0.7214127397624077 0 0 12.254901960784313 31.124617274206983 GJA1 -GO_Biological_Process_2018 regulation of relaxation of cardiac muscle (GO:1901897) 1/6 0.07888463821035145 0.7201221981885931 0 0 12.254901960784313 31.124617274206983 PLN -GO_Biological_Process_2018 regulation of cellular response to oxidative stress (GO:1900407) 1/6 0.07888463821035145 0.7188362656918277 0 0 12.254901960784313 31.124617274206983 FBLN5 -GO_Biological_Process_2018 negative regulation of vasculature development (GO:1901343) 1/6 0.07888463821035145 0.7175549176246407 0 0 12.254901960784313 31.124617274206983 FOXC1 -GO_Biological_Process_2018 carbohydrate transport (GO:0008643) 1/6 0.07888463821035145 0.7162781295149884 0 0 12.254901960784313 31.124617274206983 AQP1 -GO_Biological_Process_2018 growth hormone secretion (GO:0030252) 1/6 0.07888463821035145 0.7150058770646953 0 0 12.254901960784313 31.124617274206983 LTBP4 -GO_Biological_Process_2018 positive regulation of action potential (GO:0045760) 1/6 0.07888463821035145 0.7137381361479139 0 0 12.254901960784313 31.124617274206983 ANK3 -GO_Biological_Process_2018 positive regulation of mitophagy in response to mitochondrial depolarization (GO:0098779) 1/6 0.07888463821035145 0.7124748828095991 0 0 12.254901960784313 31.124617274206983 TOMM7 -GO_Biological_Process_2018 positive regulation of toll-like receptor 3 signaling pathway (GO:0034141) 1/6 0.07888463821035145 0.711216093263999 0 0 12.254901960784313 31.124617274206983 CAV1 -GO_Biological_Process_2018 regulation of tooth mineralization (GO:0070170) 1/6 0.07888463821035145 0.7099617438931631 0 0 12.254901960784313 31.124617274206983 ASPN -GO_Biological_Process_2018 dosage compensation by inactivation of X chromosome (GO:0009048) 1/6 0.07888463821035145 0.7087118112454639 0 0 12.254901960784313 31.124617274206983 PCGF5 -GO_Biological_Process_2018 positive regulation of microglial cell activation (GO:1903980) 1/6 0.07888463821035145 0.7074662720341361 0 0 12.254901960784313 31.124617274206983 APP -GO_Biological_Process_2018 regulation of aldosterone biosynthetic process (GO:0032347) 1/6 0.07888463821035145 0.7062251031358306 0 0 12.254901960784313 31.124617274206983 DKK3 -GO_Biological_Process_2018 regulation of cortisol biosynthetic process (GO:2000064) 1/6 0.07888463821035145 0.7049882815891829 0 0 12.254901960784313 31.124617274206983 DKK3 -GO_Biological_Process_2018 polyphosphate metabolic process (GO:0006797) 1/6 0.07888463821035145 0.7037557845933977 0 0 12.254901960784313 31.124617274206983 PRUNE2 -GO_Biological_Process_2018 positive regulation of sodium ion transmembrane transport (GO:1902307) 1/6 0.07888463821035145 0.7025275895068472 0 0 12.254901960784313 31.124617274206983 ANK3 -GO_Biological_Process_2018 cellular response to magnesium ion (GO:0071286) 1/6 0.07888463821035145 0.7013036738456855 0 0 12.254901960784313 31.124617274206983 ANK3 -GO_Biological_Process_2018 regulation of dendritic cell chemotaxis (GO:2000508) 1/6 0.07888463821035145 0.7000840152824757 0 0 12.254901960784313 31.124617274206983 GAS6 -GO_Biological_Process_2018 negative regulation of delayed rectifier potassium channel activity (GO:1902260) 1/6 0.07888463821035145 0.6988685916448324 0 0 12.254901960784313 31.124617274206983 ANK3 -GO_Biological_Process_2018 morphogenesis of an endothelium (GO:0003159) 1/6 0.07888463821035145 0.6976573809140788 0 0 12.254901960784313 31.124617274206983 RHOB -GO_Biological_Process_2018 maintenance of protein location in extracellular region (GO:0071694) 1/6 0.07888463821035145 0.696450361223916 0 0 12.254901960784313 31.124617274206983 LTBP1 -GO_Biological_Process_2018 positive regulation of oxidative stress-induced cell death (GO:1903209) 1/6 0.07888463821035145 0.695247510859108 0 0 12.254901960784313 31.124617274206983 SOD1 -GO_Biological_Process_2018 negative regulation of mononuclear cell migration (GO:0071676) 1/6 0.07888463821035145 0.6940488082541784 0 0 12.254901960784313 31.124617274206983 NOV -GO_Biological_Process_2018 negative regulation of interleukin-6 secretion (GO:1900165) 1/6 0.07888463821035145 0.6928542319921229 0 0 12.254901960784313 31.124617274206983 GAS6 -GO_Biological_Process_2018 astrocyte activation (GO:0048143) 1/6 0.07888463821035145 0.6916637608031331 0 0 12.254901960784313 31.124617274206983 APP -GO_Biological_Process_2018 neuron remodeling (GO:0016322) 1/6 0.07888463821035145 0.6904773735633336 0 0 12.254901960784313 31.124617274206983 APP -GO_Biological_Process_2018 cellular protein localization (GO:0034613) 8/327 0.07922379128738448 0.6922585735265805 0 0 1.7988846914912755 4.561033693521463 SEPT7;CTTN;USO1;FLNA;CPE;FBLN5;VCL;SEC31A -GO_Biological_Process_2018 regulation of phosphorylation (GO:0042325) 4/119 0.079460695453414 0.6931417588013191 0 0 2.4715768660405337 6.259250559536959 APP;ILK;HSPB1;OGT -GO_Biological_Process_2018 positive regulation of epithelial cell migration (GO:0010634) 3/74 0.07987170818608033 0.6955381004668394 0 0 2.9809220985691582 7.533784516697727 SPARC;RHOB;CLASP2 -GO_Biological_Process_2018 mitotic spindle organization (GO:0007052) 3/74 0.07987170818608033 0.6943531973996047 0 0 2.9809220985691582 7.533784516697727 SBDS;FLNA;CLASP2 -GO_Biological_Process_2018 mitotic nuclear division (GO:0140014) 3/74 0.07987170818608033 0.6931723246149114 0 0 2.9809220985691582 7.533784516697727 EPS8;FLNA;CLASP2 -GO_Biological_Process_2018 regulation of heart rate by cardiac conduction (GO:0086091) 2/35 0.0818024130295892 0.7087227736672218 0 0 4.201680672268908 10.518691330543254 CACNA2D1;CAV1 -GO_Biological_Process_2018 negative regulation of cell-substrate adhesion (GO:0010812) 2/35 0.0818024130295892 0.7075215486271079 0 0 4.201680672268908 10.518691330543254 JAG1;ACTN4 -GO_Biological_Process_2018 regulation of sodium ion transport (GO:0002028) 2/35 0.0818024130295892 0.7063243886463514 0 0 4.201680672268908 10.518691330543254 SLMAP;ANK3 -GO_Biological_Process_2018 regulation of synapse assembly (GO:0051963) 2/35 0.0818024130295892 0.7051312731249892 0 0 4.201680672268908 10.518691330543254 MEF2C;PDLIM5 -GO_Biological_Process_2018 DNA-dependent DNA replication maintenance of fidelity (GO:0045005) 2/35 0.0818024130295892 0.7039421816020129 0 0 4.201680672268908 10.518691330543254 ATRX;NUCKS1 -GO_Biological_Process_2018 mitotic cell cycle phase transition (GO:0044772) 6/221 0.08197679301881715 0.7042551763889291 0 0 1.9962736225712008 4.993317308502075 EPS8;PPP1CB;PPP1R12A;PPP1R12B;SKP1;CLASP2 -GO_Biological_Process_2018 negative regulation of phosphorylation (GO:0042326) 3/75 0.08240189769982018 0.7067174520372813 0 0 2.9411764705882355 7.341608270571418 IGFBP3;DNAJC10;FAM129A -GO_Biological_Process_2018 negative regulation of cellular amide metabolic process (GO:0034249) 3/75 0.08240189769982018 0.7055316845003059 0 0 2.9411764705882355 7.341608270571418 IGFBP5;ROCK1;CLU -GO_Biological_Process_2018 regulation of acute inflammatory response (GO:0002673) 4/121 0.08329751230193955 0.7120053689728602 0 0 2.430724355858046 6.041168192945412 SERPING1;IL6ST;CLU;CD55 -GO_Biological_Process_2018 Golgi organization (GO:0007030) 4/121 0.08329751230193955 0.7108147245431397 0 0 2.430724355858046 6.041168192945412 ATL3;ATP8B1;SYNE1;CLASP2 -GO_Biological_Process_2018 positive regulation of transcription, DNA-templated (GO:0045893) 21/1120 0.08542731007344075 0.7277722258844209 0 0 1.3786764705882353 3.3916674253985795 MEF2A;APP;IL33;FOXC1;MEF2C;JAG1;PPP1R12A;PCGF5;EPAS1;ATRX;EBF1;ADIRF;ILK;NR2F2;PKD2;CYR61;NFIA;NFIB;TBL1XR1;CDH13;OGT -GO_Biological_Process_2018 regulation of potassium ion transport (GO:0043266) 2/36 0.08586386087529987 0.7302721367444254 0 0 4.084967320261438 10.028563113135698 FHL1;ANK3 -GO_Biological_Process_2018 regulation of receptor-mediated endocytosis (GO:0048259) 2/36 0.08586386087529987 0.7290570416749671 0 0 4.084967320261438 10.028563113135698 SGIP1;CLU -GO_Biological_Process_2018 regulation of muscle cell differentiation (GO:0051147) 2/36 0.08586386087529987 0.7278459834662048 0 0 4.084967320261438 10.028563113135698 MEF2A;MEF2C -GO_Biological_Process_2018 calcium ion transmembrane import into cytosol (GO:0097553) 2/36 0.08586386087529987 0.7266389420342542 0 0 4.084967320261438 10.028563113135698 CACNA2D1;PKD2 -GO_Biological_Process_2018 histone deacetylation (GO:0016575) 2/36 0.08586386087529987 0.7254358974282371 0 0 4.084967320261438 10.028563113135698 TBL1XR1;MORF4L2 -GO_Biological_Process_2018 regulation of blood circulation (GO:1903522) 2/36 0.08586386087529987 0.7242368298291822 0 0 4.084967320261438 10.028563113135698 PLN;TPM1 -GO_Biological_Process_2018 plasma membrane organization (GO:0007009) 2/36 0.08586386087529987 0.7230417195489359 0 0 4.084967320261438 10.028563113135698 ANK3;SPTBN1 -GO_Biological_Process_2018 axonal transport (GO:0098930) 2/36 0.08586386087529987 0.7218505470290859 0 0 4.084967320261438 10.028563113135698 DST;SOD1 -GO_Biological_Process_2018 glutamate receptor signaling pathway (GO:0007215) 2/36 0.08586386087529987 0.7206632928398935 0 0 4.084967320261438 10.028563113135698 GRIA2;APP -GO_Biological_Process_2018 positive regulation of receptor activity (GO:2000273) 2/37 0.08998485252119941 0.7540110056086708 0 0 3.97456279809221 9.571200032340258 ITGB1;PKD2 -GO_Biological_Process_2018 regulation of sodium ion transmembrane transporter activity (GO:2000649) 2/37 0.08998485252119941 0.752774921992919 0 0 3.97456279809221 9.571200032340258 SLMAP;ANK3 -GO_Biological_Process_2018 angiotensin-activated signaling pathway (GO:0038166) 1/7 0.09141554395380712 0.7634918507304055 0 0 10.504201680672267 25.12961922102256 CAV1 -GO_Biological_Process_2018 skeletal muscle myosin thick filament assembly (GO:0030241) 1/7 0.09141554395380712 0.7622443150265976 0 0 10.504201680672267 25.12961922102256 MYH11 -GO_Biological_Process_2018 regulation of cyclic nucleotide metabolic process (GO:0030799) 1/7 0.09141554395380712 0.7610008495860974 0 0 10.504201680672267 25.12961922102256 PKD2 -GO_Biological_Process_2018 adherens junction assembly (GO:0034333) 1/7 0.09141554395380712 0.7597614345216249 0 0 10.504201680672267 25.12961922102256 VCL -GO_Biological_Process_2018 protein localization to basolateral plasma membrane (GO:1903361) 1/7 0.09141554395380712 0.7585260500752483 0 0 10.504201680672267 25.12961922102256 CAV1 -GO_Biological_Process_2018 inner ear receptor stereocilium organization (GO:0060122) 1/7 0.09141554395380712 0.7572946766173341 0 0 10.504201680672267 25.12961922102256 SOD1 -GO_Biological_Process_2018 regulation of entry of bacterium into host cell (GO:2000535) 1/7 0.09141554395380712 0.7560672946455068 0 0 10.504201680672267 25.12961922102256 CAV1 -GO_Biological_Process_2018 bleb assembly (GO:0032060) 1/7 0.09141554395380712 0.7548438847836209 0 0 10.504201680672267 25.12961922102256 MYLK -GO_Biological_Process_2018 cellular response to interferon-alpha (GO:0035457) 1/7 0.09141554395380712 0.7536244277807395 0 0 10.504201680672267 25.12961922102256 GAS6 -GO_Biological_Process_2018 response to misfolded protein (GO:0051788) 1/7 0.09141554395380712 0.7524089045101253 0 0 10.504201680672267 25.12961922102256 CLU -GO_Biological_Process_2018 positive regulation of calcineurin-NFAT signaling cascade (GO:0070886) 1/7 0.09141554395380712 0.7511972959682411 0 0 10.504201680672267 25.12961922102256 LMCD1 -GO_Biological_Process_2018 negative regulation of glial cell apoptotic process (GO:0034351) 1/7 0.09141554395380712 0.7499895832737584 0 0 10.504201680672267 25.12961922102256 GAS6 -GO_Biological_Process_2018 positive regulation of dendritic cell chemotaxis (GO:2000510) 1/7 0.09141554395380712 0.7487857476665774 0 0 10.504201680672267 25.12961922102256 GAS6 -GO_Biological_Process_2018 hemostasis (GO:0007599) 1/7 0.09141554395380712 0.7475857705068554 0 0 10.504201680672267 25.12961922102256 VWF -GO_Biological_Process_2018 regulation of cardiac muscle cell membrane potential (GO:0086036) 1/7 0.09141554395380712 0.7463896332740444 0 0 10.504201680672267 25.12961922102256 PLN -GO_Biological_Process_2018 regulation of endothelial cell chemotaxis to fibroblast growth factor (GO:2000544) 1/7 0.09141554395380712 0.7451973175659389 0 0 10.504201680672267 25.12961922102256 FGFR1 -GO_Biological_Process_2018 positive regulation of extracellular matrix disassembly (GO:0090091) 1/7 0.09141554395380712 0.7440088050977316 0 0 10.504201680672267 25.12961922102256 CLASP2 -GO_Biological_Process_2018 melanocyte differentiation (GO:0030318) 1/7 0.09141554395380712 0.7428240777010792 0 0 10.504201680672267 25.12961922102256 MEF2C -GO_Biological_Process_2018 vesicle transport along actin filament (GO:0030050) 1/7 0.09141554395380712 0.741643117323176 0 0 10.504201680672267 25.12961922102256 ACTN4 -GO_Biological_Process_2018 sequestering of extracellular ligand from receptor (GO:0035581) 1/7 0.09141554395380712 0.7404659060258376 0 0 10.504201680672267 25.12961922102256 LTBP1 -GO_Biological_Process_2018 gas homeostasis (GO:0033483) 1/7 0.09141554395380712 0.7392924259845922 0 0 10.504201680672267 25.12961922102256 CAV1 -GO_Biological_Process_2018 positive regulation of cyclin-dependent protein serine/threonine kinase activity involved in G1/S transition of mitotic cell cycle (GO:0031659) 1/7 0.09141554395380712 0.7381226594877812 0 0 10.504201680672267 25.12961922102256 PKD2 -GO_Biological_Process_2018 negative regulation of calcium ion import (GO:0090281) 1/7 0.09141554395380712 0.7369565889356678 0 0 10.504201680672267 25.12961922102256 PLN -GO_Biological_Process_2018 retinoic acid receptor signaling pathway (GO:0048384) 1/7 0.09141554395380712 0.7357941968395547 0 0 10.504201680672267 25.12961922102256 ACTN4 -GO_Biological_Process_2018 regulation of response to biotic stimulus (GO:0002831) 1/7 0.09141554395380712 0.7346354658209098 0 0 10.504201680672267 25.12961922102256 CD55 -GO_Biological_Process_2018 positive regulation of ER-associated ubiquitin-dependent protein catabolic process (GO:1903071) 1/7 0.09141554395380712 0.7334803786104995 0 0 10.504201680672267 25.12961922102256 CAV1 -GO_Biological_Process_2018 vascular endothelial growth factor signaling pathway (GO:0038084) 1/7 0.09141554395380712 0.7323289180475318 0 0 10.504201680672267 25.12961922102256 FOXC1 -GO_Biological_Process_2018 cellular response to hydroxyurea (GO:0072711) 1/7 0.09141554395380712 0.7311810670788053 0 0 10.504201680672267 25.12961922102256 ATRX -GO_Biological_Process_2018 regulation of atrial cardiac muscle cell membrane repolarization (GO:0060372) 1/7 0.09141554395380712 0.7300368087578681 0 0 10.504201680672267 25.12961922102256 FLNA -GO_Biological_Process_2018 membrane fusion involved in viral entry into host cell (GO:0039663) 1/7 0.09141554395380712 0.7288961262441841 0 0 10.504201680672267 25.12961922102256 GAS6 -GO_Biological_Process_2018 regulation of high voltage-gated calcium channel activity (GO:1901841) 1/7 0.09141554395380712 0.7277590028023053 0 0 10.504201680672267 25.12961922102256 CACNA2D1 -GO_Biological_Process_2018 peroxisome proliferator activated receptor signaling pathway (GO:0035357) 1/7 0.09141554395380712 0.7266254218010556 0 0 10.504201680672267 25.12961922102256 ACTN4 -GO_Biological_Process_2018 positive regulation of hemopoiesis (GO:1903708) 1/7 0.09141554395380712 0.7254953667127181 0 0 10.504201680672267 25.12961922102256 FOXC1 -GO_Biological_Process_2018 regulation of RNA biosynthetic process (GO:2001141) 1/7 0.09141554395380712 0.7243688211122324 0 0 10.504201680672267 25.12961922102256 ACTN4 -GO_Biological_Process_2018 regulation of pinocytosis (GO:0048548) 1/7 0.09141554395380712 0.7232457686763996 0 0 10.504201680672267 25.12961922102256 CAV1 -GO_Biological_Process_2018 regulation of toll-like receptor 3 signaling pathway (GO:0034139) 1/7 0.09141554395380712 0.7221261931830925 0 0 10.504201680672267 25.12961922102256 CAV1 -GO_Biological_Process_2018 positive regulation of cell migration by vascular endothelial growth factor signaling pathway (GO:0038089) 1/7 0.09141554395380712 0.7210100785104756 0 0 10.504201680672267 25.12961922102256 HSPB1 -GO_Biological_Process_2018 negative regulation of dendritic cell apoptotic process (GO:2000669) 1/7 0.09141554395380712 0.7198974086362311 0 0 10.504201680672267 25.12961922102256 GAS6 -GO_Biological_Process_2018 fusion of virus membrane with host plasma membrane (GO:0019064) 1/7 0.09141554395380712 0.7187881676367915 0 0 10.504201680672267 25.12961922102256 GAS6 -GO_Biological_Process_2018 mannose metabolic process (GO:0006013) 1/7 0.09141554395380712 0.7176823396865811 0 0 10.504201680672267 25.12961922102256 MAN2A1 -GO_Biological_Process_2018 epithelial cell differentiation involved in kidney development (GO:0035850) 1/7 0.09141554395380712 0.7165799090572622 0 0 10.504201680672267 25.12961922102256 MEF2C -GO_Biological_Process_2018 positive regulation of membrane depolarization (GO:1904181) 1/7 0.09141554395380712 0.7154808601169904 0 0 10.504201680672267 25.12961922102256 ANK3 -GO_Biological_Process_2018 regulation of microglial cell activation (GO:1903978) 1/7 0.09141554395380712 0.7143851773296749 0 0 10.504201680672267 25.12961922102256 APP -GO_Biological_Process_2018 positive regulation of myeloid cell apoptotic process (GO:0033034) 1/7 0.09141554395380712 0.7132928452542473 0 0 10.504201680672267 25.12961922102256 MEF2C -GO_Biological_Process_2018 outer mitochondrial membrane organization (GO:0007008) 1/7 0.09141554395380712 0.7122038485439355 0 0 10.504201680672267 25.12961922102256 TOMM7 -GO_Biological_Process_2018 regulation of transcription involved in cell fate commitment (GO:0060850) 1/7 0.09141554395380712 0.7111181719455453 0 0 10.504201680672267 25.12961922102256 NR2F2 -GO_Biological_Process_2018 metanephric nephron tubule development (GO:0072234) 1/7 0.09141554395380712 0.7100358002987485 0 0 10.504201680672267 25.12961922102256 PKD2 -GO_Biological_Process_2018 negative regulation of heart rate (GO:0010459) 1/7 0.09141554395380712 0.7089567185353765 0 0 10.504201680672267 25.12961922102256 PLN -GO_Biological_Process_2018 positive regulation of CD4-positive, alpha-beta T cell activation (GO:2000516) 1/7 0.09141554395380712 0.7078809116787219 0 0 10.504201680672267 25.12961922102256 CD55 -GO_Biological_Process_2018 regulation of relaxation of muscle (GO:1901077) 1/7 0.09141554395380712 0.7068083648428449 0 0 10.504201680672267 25.12961922102256 PLN -GO_Biological_Process_2018 hyperosmotic response (GO:0006972) 1/7 0.09141554395380712 0.7057390632318876 0 0 10.504201680672267 25.12961922102256 AQP1 -GO_Biological_Process_2018 microglial cell activation (GO:0001774) 1/7 0.09141554395380712 0.7046729921393923 0 0 10.504201680672267 25.12961922102256 CLU -GO_Biological_Process_2018 negative regulation of mitotic cell cycle phase transition (GO:1901991) 3/79 0.09286012628534522 0.7147288453003268 0 0 2.792256142963515 6.636246099507928 FHL1;PKD2;SKP1 -GO_Biological_Process_2018 G2/M transition of mitotic cell cycle (GO:0000086) 4/126 0.09327850582409217 0.7168677940065395 0 0 2.334267040149393 5.537267914773756 PPP1CB;PPP1R12A;PPP1R12B;SKP1 -GO_Biological_Process_2018 muscle filament sliding (GO:0030049) 2/38 0.09416303168836827 0.7225773694823209 0 0 3.8699690402476783 9.143682737415872 TPM2;TPM1 -GO_Biological_Process_2018 regulation of cellular amide metabolic process (GO:0034248) 2/38 0.09416303168836827 0.7214924184770921 0 0 3.8699690402476783 9.143682737415872 APP;ROCK1 -GO_Biological_Process_2018 actin-myosin filament sliding (GO:0033275) 2/38 0.09416303168836827 0.7204107206982661 0 0 3.8699690402476783 9.143682737415872 TPM2;TPM1 -GO_Biological_Process_2018 retina homeostasis (GO:0001895) 2/38 0.09416303168836827 0.7193322615355441 0 0 3.8699690402476783 9.143682737415872 HSPB1;SOD1 -GO_Biological_Process_2018 cellular response to cytokine stimulus (GO:0071345) 10/456 0.09460017190937282 0.7215914458199245 0 0 1.6124871001031988 3.8023993577498407 ITGB1;FOXC1;ZEB1;PTGIS;MMP2;BGN;GAS6;NPNT;IL6ST;ASPN -GO_Biological_Process_2018 cell cycle G2/M phase transition (GO:0044839) 4/127 0.09534009875618127 0.7261500357504372 0 0 2.315886984715146 5.4430402810894645 PPP1CB;PPP1R12A;PPP1R12B;SKP1 -GO_Biological_Process_2018 regulation of phosphatidylinositol 3-kinase signaling (GO:0014066) 3/80 0.09555663199464888 0.7267145947372476 0 0 2.7573529411764706 6.474364527777502 PDGFRB;PDGFRA;FGFR1 -GO_Biological_Process_2018 apoptotic process (GO:0006915) 6/231 0.09626111481373856 0.7309828406168272 0 0 1.9098548510313216 4.470379743882899 APP;NCKAP1;PRUNE2;PAWR;OGT;RHOB -GO_Biological_Process_2018 positive regulation of fat cell differentiation (GO:0045600) 2/39 0.09839609466366596 0.7460850981704121 0 0 3.770739064856712 8.743416908329232 FRZB;ADIRF -GO_Biological_Process_2018 positive regulation of reactive oxygen species metabolic process (GO:2000379) 2/39 0.09839609466366596 0.7449781469861829 0 0 3.770739064856712 8.743416908329232 PDGFRB;SOD1 -GO_Biological_Process_2018 regulation of plasma membrane bounded cell projection assembly (GO:0120032) 2/39 0.09839609466366596 0.7438744756573147 0 0 3.770739064856712 8.743416908329232 ATP8B1;CAV1 -GO_Biological_Process_2018 Golgi to plasma membrane transport (GO:0006893) 2/39 0.09839609466366596 0.7427740696282358 0 0 3.770739064856712 8.743416908329232 ANK3;SPTBN1 -GO_Biological_Process_2018 DNA replication-independent nucleosome assembly (GO:0006336) 2/39 0.09839609466366596 0.7416769144293758 0 0 3.770739064856712 8.743416908329232 ATRX;SMARCA5 -GO_Biological_Process_2018 modulation of chemical synaptic transmission (GO:0050804) 3/82 0.10104452464419272 0.7605165328308486 0 0 2.6901004304160687 6.166232123081367 APP;GRIA2;MEF2C -GO_Biological_Process_2018 positive regulation of DNA metabolic process (GO:0051054) 3/82 0.10104452464419272 0.7593964790269742 0 0 2.6901004304160687 6.166232123081367 PDGFRB;PDGFRA;ATRX -GO_Biological_Process_2018 ephrin receptor signaling pathway (GO:0048013) 3/82 0.10104452464419272 0.7582797194989932 0 0 2.6901004304160687 6.166232123081367 ROCK1;MMP2;KALRN -GO_Biological_Process_2018 regulation of cellular localization (GO:0060341) 2/40 0.10268178947857368 0.7694349070619113 0 0 3.6764705882352944 8.36809005664813 MEF2C;GSN -GO_Biological_Process_2018 negative regulation of cytokine-mediated signaling pathway (GO:0001960) 2/40 0.10268178947857368 0.7683067033858675 0 0 3.6764705882352944 8.36809005664813 CCDC3;GAS6 -GO_Biological_Process_2018 neuron migration (GO:0001764) 2/40 0.10268178947857368 0.7671818033809101 0 0 3.6764705882352944 8.36809005664813 MEF2C;FGFR1 -GO_Biological_Process_2018 proteolysis (GO:0006508) 7/291 0.10334006862735184 0.7709713014698486 0 0 1.7687487366080454 4.014582232720211 SENP6;TINAGL1;MMP2;HTRA1;CPE;AEBP1;OGT -GO_Biological_Process_2018 neuron projection extension involved in neuron projection guidance (GO:1902284) 1/8 0.10377659812326197 0.7730977813474537 0 0 9.191176470588236 20.822746188186787 ALCAM -GO_Biological_Process_2018 cell migration involved in heart development (GO:0060973) 1/8 0.10377659812326197 0.7719708166516119 0 0 9.191176470588236 20.822746188186787 PDGFRB -GO_Biological_Process_2018 cellular response to nitric oxide (GO:0071732) 1/8 0.10377659812326197 0.7708471327845791 0 0 9.191176470588236 20.822746188186787 AQP1 -GO_Biological_Process_2018 histone H2A-K119 monoubiquitination (GO:0036353) 1/8 0.10377659812326197 0.7697267154404154 0 0 9.191176470588236 20.822746188186787 PCGF5 -GO_Biological_Process_2018 regulation of fibroblast apoptotic process (GO:2000269) 1/8 0.10377659812326197 0.7686095503962348 0 0 9.191176470588236 20.822746188186787 GAS6 -GO_Biological_Process_2018 late endosome to vacuole transport (GO:0045324) 1/8 0.10377659812326197 0.7674956235116026 0 0 9.191176470588236 20.822746188186787 LEPROT -GO_Biological_Process_2018 positive regulation of mitochondrial calcium ion concentration (GO:0051561) 1/8 0.10377659812326197 0.7663849207279388 0 0 9.191176470588236 20.822746188186787 TGM2 -GO_Biological_Process_2018 regulation of calcium ion transmembrane transport via high voltage-gated calcium channel (GO:1902514) 1/8 0.10377659812326197 0.7652774280679274 0 0 9.191176470588236 20.822746188186787 CACNA2D1 -GO_Biological_Process_2018 regulation of amyloid precursor protein catabolic process (GO:1902991) 1/8 0.10377659812326197 0.7641731316349291 0 0 9.191176470588236 20.822746188186787 ROCK1 -GO_Biological_Process_2018 positive regulation of membrane potential (GO:0045838) 1/8 0.10377659812326197 0.7630720176124003 0 0 9.191176470588236 20.822746188186787 ANK3 -GO_Biological_Process_2018 negative regulation of interleukin-1 production (GO:0032692) 1/8 0.10377659812326197 0.7619740722633177 0 0 9.191176470588236 20.822746188186787 GAS6 -GO_Biological_Process_2018 response to magnesium ion (GO:0032026) 1/8 0.10377659812326197 0.7608792819296059 0 0 9.191176470588236 20.822746188186787 ANK3 -GO_Biological_Process_2018 glomerular visceral epithelial cell development (GO:0072015) 1/8 0.10377659812326197 0.7597876330315722 0 0 9.191176470588236 20.822746188186787 JAG1 -GO_Biological_Process_2018 negative regulation of platelet aggregation (GO:0090331) 1/8 0.10377659812326197 0.7586991120673435 0 0 9.191176470588236 20.822746188186787 PRKG1 -GO_Biological_Process_2018 negative regulation of lipase activity (GO:0060192) 1/8 0.10377659812326197 0.7576137056123116 0 0 9.191176470588236 20.822746188186787 SORT1 -GO_Biological_Process_2018 cytoskeletal anchoring at nuclear membrane (GO:0090286) 1/8 0.10377659812326197 0.7565314003185797 0 0 9.191176470588236 20.822746188186787 SYNE1 -GO_Biological_Process_2018 cellular response to reactive nitrogen species (GO:1902170) 1/8 0.10377659812326197 0.7554521829144162 0 0 9.191176470588236 20.822746188186787 AQP1 -GO_Biological_Process_2018 regulation of microvillus organization (GO:0032530) 1/8 0.10377659812326197 0.754376040203712 0 0 9.191176470588236 20.822746188186787 ATP8B1 -GO_Biological_Process_2018 cellular response to UV-B (GO:0071493) 1/8 0.10377659812326197 0.7533029590654421 0 0 9.191176470588236 20.822746188186787 MFAP4 -GO_Biological_Process_2018 axon extension involved in axon guidance (GO:0048846) 1/8 0.10377659812326197 0.7522329264531332 0 0 9.191176470588236 20.822746188186787 ALCAM -GO_Biological_Process_2018 protein desumoylation (GO:0016926) 1/8 0.10377659812326197 0.7511659293943345 0 0 9.191176470588236 20.822746188186787 SENP6 -GO_Biological_Process_2018 plasma membrane to endosome transport (GO:0048227) 1/8 0.10377659812326197 0.7501019549900931 0 0 9.191176470588236 20.822746188186787 SORT1 -GO_Biological_Process_2018 atrial cardiac muscle cell to AV node cell signaling (GO:0086026) 1/8 0.10377659812326197 0.7490409904144353 0 0 9.191176470588236 20.822746188186787 GJA1 -GO_Biological_Process_2018 insulin metabolic process (GO:1901142) 1/8 0.10377659812326197 0.74798302291385 0 0 9.191176470588236 20.822746188186787 CPE -GO_Biological_Process_2018 central nervous system myelination (GO:0022010) 1/8 0.10377659812326197 0.7469280398067782 0 0 9.191176470588236 20.822746188186787 CLU -GO_Biological_Process_2018 negative regulation of calcium ion transmembrane transport (GO:1903170) 1/8 0.10377659812326197 0.7458760284831067 0 0 9.191176470588236 20.822746188186787 PLN -GO_Biological_Process_2018 regulation of positive chemotaxis (GO:0050926) 1/8 0.10377659812326197 0.7448269764036649 0 0 9.191176470588236 20.822746188186787 CDH13 -GO_Biological_Process_2018 response to hydroxyurea (GO:0072710) 1/8 0.10377659812326197 0.7437808710997272 0 0 9.191176470588236 20.822746188186787 ATRX -GO_Biological_Process_2018 regulation of chemokine secretion (GO:0090196) 1/8 0.10377659812326197 0.7427377001725186 0 0 9.191176470588236 20.822746188186787 IL33 -GO_Biological_Process_2018 positive regulation of tumor necrosis factor biosynthetic process (GO:0042535) 1/8 0.10377659812326197 0.7416974512927251 0 0 9.191176470588236 20.822746188186787 HSPB1 -GO_Biological_Process_2018 angiogenesis involved in wound healing (GO:0060055) 1/8 0.10377659812326197 0.7406601122000079 0 0 9.191176470588236 20.822746188186787 MCAM -GO_Biological_Process_2018 protein localization to endosome (GO:0036010) 1/8 0.10377659812326197 0.7396256707025219 0 0 9.191176470588236 20.822746188186787 TMEM30A -GO_Biological_Process_2018 late endosome to vacuole transport via multivesicular body sorting pathway (GO:0032511) 1/8 0.10377659812326197 0.7385941146764377 0 0 9.191176470588236 20.822746188186787 LEPROT -GO_Biological_Process_2018 polyol transport (GO:0015791) 1/8 0.10377659812326197 0.7375654320654677 0 0 9.191176470588236 20.822746188186787 AQP1 -GO_Biological_Process_2018 glomerular epithelial cell development (GO:0072310) 1/8 0.10377659812326197 0.7365396108803974 0 0 9.191176470588236 20.822746188186787 JAG1 -GO_Biological_Process_2018 calcium ion import into cytosol (GO:1902656) 1/8 0.10377659812326197 0.7355166391986191 0 0 9.191176470588236 20.822746188186787 CACNA2D1 -GO_Biological_Process_2018 positive regulation of chemokine secretion (GO:0090197) 1/8 0.10377659812326197 0.7344965051636696 0 0 9.191176470588236 20.822746188186787 IL33 -GO_Biological_Process_2018 positive regulation of protein homodimerization activity (GO:0090073) 1/8 0.10377659812326197 0.7334791969847725 0 0 9.191176470588236 20.822746188186787 MEF2C -GO_Biological_Process_2018 calcium ion import across plasma membrane (GO:0098703) 1/8 0.10377659812326197 0.7324647029363842 0 0 9.191176470588236 20.822746188186787 CACNA2D1 -GO_Biological_Process_2018 regulation of dopamine receptor signaling pathway (GO:0060159) 1/8 0.10377659812326197 0.7314530113577428 0 0 9.191176470588236 20.822746188186787 CAV2 -GO_Biological_Process_2018 regulation of peroxisome proliferator activated receptor signaling pathway (GO:0035358) 1/8 0.10377659812326197 0.7304441106524218 0 0 9.191176470588236 20.822746188186787 PTGIS -GO_Biological_Process_2018 kidney mesenchyme development (GO:0072074) 1/8 0.10377659812326197 0.7294379892878867 0 0 9.191176470588236 20.822746188186787 PKD2 -GO_Biological_Process_2018 regulation of cardiac muscle cell action potential involved in regulation of contraction (GO:0098909) 1/8 0.10377659812326197 0.7284346357950561 0 0 9.191176470588236 20.822746188186787 CAV1 -GO_Biological_Process_2018 protein maturation (GO:0051604) 3/83 0.10383490650924927 0.7278427581273338 0 0 2.6576895818568387 6.019542200298399 CPE;AEBP1;OGT -GO_Biological_Process_2018 positive regulation of gene expression (GO:0010628) 15/771 0.1054083764775105 0.7378586353425736 0 0 1.4305333028152896 3.2185757221497213 APP;FOXC1;MEF2C;GSN;ROCK1;SMARCA5;FAM129A;ILK;NR2F2;ANK3;CLU;ACTA2;SFRP4;TBL1XR1;GAS6 -GO_Biological_Process_2018 negative regulation of programmed cell death (GO:0043069) 9/408 0.10617221839987716 0.7421874390336618 0 0 1.6219723183391006 3.637585642384408 PDGFRB;PDGFRA;FLNA;HSPB1;GAS6;IL6ST;CRYAB;AQP1;FGFR1 -GO_Biological_Process_2018 odontogenesis (GO:0042476) 2/41 0.1070179147951342 0.7470758128585088 0 0 3.586800573888092 8.015634973252599 FOXC1;AQP1 -GO_Biological_Process_2018 regulation of calcium-mediated signaling (GO:0050848) 2/41 0.1070179147951342 0.7460552174857512 0 0 3.586800573888092 8.015634973252599 RCAN2;CDH13 -GO_Biological_Process_2018 calcium-dependent cell-cell adhesion via plasma membrane cell adhesion molecules (GO:0016339) 2/41 0.1070179147951342 0.7450374068206956 0 0 3.586800573888092 8.015634973252599 CDH11;CDH13 -GO_Biological_Process_2018 regulation of substrate adhesion-dependent cell spreading (GO:1900024) 2/41 0.1070179147951342 0.7440223694817029 0 0 3.586800573888092 8.015634973252599 FLNA;ACTN4 -GO_Biological_Process_2018 negative regulation of proteolysis (GO:0045861) 2/41 0.1070179147951342 0.7430100941490747 0 0 3.586800573888092 8.015634973252599 ROCK1;TIMP2 -GO_Biological_Process_2018 regulation of protein modification process (GO:0031399) 2/42 0.11140231906500314 0.7723995029737922 0 0 3.5014005602240896 7.68419864932073 PPP1R14A;HSPB1 -GO_Biological_Process_2018 plasma membrane bounded cell projection assembly (GO:0120031) 6/241 0.11178725608905933 0.7740167813059292 0 0 1.8306077617769096 4.011150317113015 GSN;SEPT7;FLNA;CDH13;VCL;MYLK -GO_Biological_Process_2018 calcium ion transmembrane transport (GO:0070588) 3/86 0.11238568933912156 0.7771059250644138 0 0 2.5649794801641588 5.606580032511796 CACNA2D1;PKD2;GAS6 -GO_Biological_Process_2018 cellular response to organic cyclic compound (GO:0071407) 4/135 0.11258803748618007 0.7774516309769648 0 0 2.1786492374727677 4.758213089197828 MEF2C;IGFBP5;PKD2;AQP1 -GO_Biological_Process_2018 regulation of GTPase activity (GO:0043087) 5/188 0.11421876880946025 0.7876464557225347 0 0 1.9555694618272843 4.24288103277286 NET1;ITGB1;CAV2;PRKG1;SOD1 -GO_Biological_Process_2018 positive regulation of cellular component biogenesis (GO:0044089) 3/87 0.11529411601659315 0.7939890337822872 0 0 2.5354969574036508 5.477355184978663 SYNPO2;SYNPO;PFN2 -GO_Biological_Process_2018 eye development (GO:0001654) 2/43 0.11583289950730888 0.7966243749134734 0 0 3.4199726402188784 7.372115758936582 FOXC1;EFEMP1 -GO_Biological_Process_2018 branching morphogenesis of an epithelial tube (GO:0048754) 2/43 0.11583289950730888 0.7955522021343167 0 0 3.4199726402188784 7.372115758936582 PKD2;NPNT -GO_Biological_Process_2018 regulation of multicellular organismal development (GO:2000026) 2/43 0.11583289950730888 0.79448291154005 0 0 3.4199726402188784 7.372115758936582 PHLDB2;CLASP2 -GO_Biological_Process_2018 removal of superoxide radicals (GO:0019430) 1/9 0.11597009421931867 0.7943562292633332 0 0 8.169934640522875 17.601494522616292 SOD1 -GO_Biological_Process_2018 negative regulation of leukocyte chemotaxis (GO:0002689) 1/9 0.11597009421931867 0.7932914085806745 0 0 8.169934640522875 17.601494522616292 NOV -GO_Biological_Process_2018 actin filament reorganization (GO:0090527) 1/9 0.11597009421931867 0.7922294388235385 0 0 8.169934640522875 17.601494522616292 GSN -GO_Biological_Process_2018 regulation of organ growth (GO:0046620) 1/9 0.11597009421931867 0.7911703085577315 0 0 8.169934640522875 17.601494522616292 SOD1 -GO_Biological_Process_2018 epithelial cell apoptotic process (GO:1904019) 1/9 0.11597009421931867 0.7901140064101244 0 0 8.169934640522875 17.601494522616292 GSN -GO_Biological_Process_2018 neuron maturation (GO:0042551) 1/9 0.11597009421931867 0.7890605210682443 0 0 8.169934640522875 17.601494522616292 APP -GO_Biological_Process_2018 negative regulation of pri-miRNA transcription from RNA polymerase II promoter (GO:1902894) 1/9 0.11597009421931867 0.7880098412798711 0 0 8.169934640522875 17.601494522616292 NFIB -GO_Biological_Process_2018 regulation of interleukin-1 secretion (GO:0050704) 1/9 0.11597009421931867 0.7869619558526373 0 0 8.169934640522875 17.601494522616292 GAS6 -GO_Biological_Process_2018 vasodilation (GO:0042311) 1/9 0.11597009421931867 0.7859168536536297 0 0 8.169934640522875 17.601494522616292 SOD1 -GO_Biological_Process_2018 regulation of ER-associated ubiquitin-dependent protein catabolic process (GO:1903069) 1/9 0.11597009421931867 0.7848745236089963 0 0 8.169934640522875 17.601494522616292 CAV1 -GO_Biological_Process_2018 regulation of endothelial cell differentiation (GO:0045601) 1/9 0.11597009421931867 0.7838349547035539 0 0 8.169934640522875 17.601494522616292 ZEB1 -GO_Biological_Process_2018 embryonic digestive tract morphogenesis (GO:0048557) 1/9 0.11597009421931867 0.7827981359804009 0 0 8.169934640522875 17.601494522616292 PDGFRA -GO_Biological_Process_2018 regulation of cysteine-type endopeptidase activity involved in apoptotic signaling pathway (GO:2001267) 1/9 0.11597009421931867 0.7817640565405326 0 0 8.169934640522875 17.601494522616292 GSN -GO_Biological_Process_2018 neurotrophin TRK receptor signaling pathway (GO:0048011) 1/9 0.11597009421931867 0.7807327055424581 0 0 8.169934640522875 17.601494522616292 SORT1 -GO_Biological_Process_2018 positive regulation of long-term synaptic potentiation (GO:1900273) 1/9 0.11597009421931867 0.7797040722018224 0 0 8.169934640522875 17.601494522616292 APP -GO_Biological_Process_2018 positive regulation of leukocyte apoptotic process (GO:2000108) 1/9 0.11597009421931867 0.7786781457910306 0 0 8.169934640522875 17.601494522616292 MEF2C -GO_Biological_Process_2018 regulation of sensory perception of pain (GO:0051930) 1/9 0.11597009421931867 0.7776549156388741 0 0 8.169934640522875 17.601494522616292 NOV -GO_Biological_Process_2018 L-ascorbic acid metabolic process (GO:0019852) 1/9 0.11597009421931867 0.7766343711301616 0 0 8.169934640522875 17.601494522616292 CYB5R3 -GO_Biological_Process_2018 protein polyglutamylation (GO:0018095) 1/9 0.11597009421931867 0.7756165017053515 0 0 8.169934640522875 17.601494522616292 TTLL7 -GO_Biological_Process_2018 virion attachment to host cell (GO:0019062) 1/9 0.11597009421931867 0.7746012968601874 0 0 8.169934640522875 17.601494522616292 GAS6 -GO_Biological_Process_2018 regulation of skeletal muscle cell differentiation (GO:2001014) 1/9 0.11597009421931867 0.7735887461453376 0 0 8.169934640522875 17.601494522616292 MEF2C -GO_Biological_Process_2018 positive regulation of astrocyte differentiation (GO:0048711) 1/9 0.11597009421931867 0.7725788391660355 0 0 8.169934640522875 17.601494522616292 APP -GO_Biological_Process_2018 Golgi vesicle budding (GO:0048194) 1/9 0.11597009421931867 0.7715715655817251 0 0 8.169934640522875 17.601494522616292 SEC31A -GO_Biological_Process_2018 positive regulation of epidermis development (GO:0045684) 1/9 0.11597009421931867 0.7705669151057073 0 0 8.169934640522875 17.601494522616292 SFRP4 -GO_Biological_Process_2018 regulation of Rac protein signal transduction (GO:0035020) 1/9 0.11597009421931867 0.7695648775047896 0 0 8.169934640522875 17.601494522616292 OGT -GO_Biological_Process_2018 negative regulation of alcohol biosynthetic process (GO:1902931) 1/9 0.11597009421931867 0.7685654425989392 0 0 8.169934640522875 17.601494522616292 SOD1 -GO_Biological_Process_2018 Notch signaling involved in heart development (GO:0061314) 1/9 0.11597009421931867 0.767568600260938 0 0 8.169934640522875 17.601494522616292 JAG1 -GO_Biological_Process_2018 positive regulation of cell activation (GO:0050867) 1/9 0.11597009421931867 0.7665743404160404 0 0 8.169934640522875 17.601494522616292 APP -GO_Biological_Process_2018 cellular response to superoxide (GO:0071451) 1/9 0.11597009421931867 0.7655826530416342 0 0 8.169934640522875 17.601494522616292 SOD1 -GO_Biological_Process_2018 endothelial cell chemotaxis (GO:0035767) 1/9 0.11597009421931867 0.7645935281669034 0 0 8.169934640522875 17.601494522616292 NOV -GO_Biological_Process_2018 glucose import (GO:0046323) 1/9 0.11597009421931867 0.7636069558724945 0 0 8.169934640522875 17.601494522616292 SORT1 -GO_Biological_Process_2018 protein modification by small protein conjugation or removal (GO:0070647) 1/9 0.11597009421931867 0.7626229262901846 0 0 8.169934640522875 17.601494522616292 SENP6 -GO_Biological_Process_2018 B cell homeostasis (GO:0001782) 1/9 0.11597009421931867 0.7616414296025524 0 0 8.169934640522875 17.601494522616292 MEF2C -GO_Biological_Process_2018 regulation of cell communication by electrical coupling involved in cardiac conduction (GO:1901844) 1/9 0.11597009421931867 0.7606624560426519 0 0 8.169934640522875 17.601494522616292 CAV1 -GO_Biological_Process_2018 regulation of homotypic cell-cell adhesion (GO:0034110) 1/9 0.11597009421931867 0.7596859958936883 0 0 8.169934640522875 17.601494522616292 ANK3 -GO_Biological_Process_2018 T cell mediated immunity (GO:0002456) 1/9 0.11597009421931867 0.7587120394886964 0 0 8.169934640522875 17.601494522616292 JAG1 -GO_Biological_Process_2018 muscle cell fate commitment (GO:0042693) 1/9 0.11597009421931867 0.7577405772102218 0 0 8.169934640522875 17.601494522616292 MEF2C -GO_Biological_Process_2018 positive regulation of protein homooligomerization (GO:0032464) 1/9 0.11597009421931867 0.756771599490004 0 0 8.169934640522875 17.601494522616292 CLU -GO_Biological_Process_2018 positive regulation of vascular endothelial cell proliferation (GO:1905564) 1/9 0.11597009421931867 0.7558050968086631 0 0 8.169934640522875 17.601494522616292 FGFR1 -GO_Biological_Process_2018 negative regulation of potassium ion transmembrane transport (GO:1901380) 1/9 0.11597009421931867 0.7548410596953867 0 0 8.169934640522875 17.601494522616292 CAV1 -GO_Biological_Process_2018 neuron projection organization (GO:0106027) 1/9 0.11597009421931867 0.7538794787276221 0 0 8.169934640522875 17.601494522616292 APP -GO_Biological_Process_2018 histone H3-K4 trimethylation (GO:0080182) 1/9 0.11597009421931867 0.7529203445307674 0 0 8.169934640522875 17.601494522616292 OGT -GO_Biological_Process_2018 cyclic nucleotide catabolic process (GO:0009214) 1/9 0.11597009421931867 0.7519636477778694 0 0 8.169934640522875 17.601494522616292 PDE5A -GO_Biological_Process_2018 divalent metal ion transport (GO:0070838) 3/88 0.1182307626576421 0.7656492155354665 0 0 2.5066844919786098 5.352064543367697 CACNA2D1;CAV1;PKD2 -GO_Biological_Process_2018 neutrophil degranulation (GO:0043312) 10/479 0.11976760398474452 0.7746186098024731 0 0 1.535060788407221 3.2577091479795564 CYB5R3;GSN;TMEM30A;ROCK1;FGL2;TIMP2;HBB;SPTAN1;CD55;VCL -GO_Biological_Process_2018 cholesterol transport (GO:0030301) 2/44 0.1203076012637693 0.7771261889228034 0 0 3.3422459893048133 7.077885936155263 CAV1;CLU -GO_Biological_Process_2018 heart contraction (GO:0060047) 2/44 0.1203076012637693 0.7761437285069718 0 0 3.3422459893048133 7.077885936155263 TPM1;SOD1 -GO_Biological_Process_2018 microtubule cytoskeleton organization involved in mitosis (GO:1902850) 2/44 0.1203076012637693 0.7751637490517862 0 0 3.3422459893048133 7.077885936155263 SBDS;CLASP2 -GO_Biological_Process_2018 inorganic anion transport (GO:0015698) 2/44 0.1203076012637693 0.7741862411715191 0 0 3.3422459893048133 7.077885936155263 ANO1;CLIC4 -GO_Biological_Process_2018 posttranscriptional regulation of gene expression (GO:0010608) 2/44 0.1203076012637693 0.7732111955277263 0 0 3.3422459893048133 7.077885936155263 APP;MATR3 -GO_Biological_Process_2018 regulation of small GTPase mediated signal transduction (GO:0051056) 4/140 0.1240206538342501 0.7960721968756959 0 0 2.100840336134454 4.3850990841759865 NET1;A2M;KALRN;RHOB -GO_Biological_Process_2018 negative regulation of sequence-specific DNA binding transcription factor activity (GO:0043433) 4/140 0.1240206538342501 0.7950721061761034 0 0 2.100840336134454 4.3850990841759865 SFRP4;PTGIS;FLNA;GAS6 -GO_Biological_Process_2018 neutrophil activation involved in immune response (GO:0002283) 10/483 0.1244923329135962 0.7970945732221849 0 0 1.5223480696626477 3.1718291742738565 CYB5R3;GSN;TMEM30A;ROCK1;FGL2;TIMP2;HBB;SPTAN1;CD55;VCL -GO_Biological_Process_2018 positive regulation of blood vessel endothelial cell migration (GO:0043536) 2/45 0.12482441640536875 0.7982192943817004 0 0 3.2679738562091507 6.800154241532245 HSPB1;FGFR1 -GO_Biological_Process_2018 metal ion homeostasis (GO:0055065) 2/45 0.12482441640536875 0.7972202714851024 0 0 3.2679738562091507 6.800154241532245 CAV1;ANK3 -GO_Biological_Process_2018 positive regulation of cell proliferation (GO:0008284) 9/424 0.12585857273848236 0.8028203708555943 0 0 1.5607658157602664 3.234837675010548 PDGFRB;PDGFRA;MEF2C;CDH13;GAS6;IL6ST;CYR61;AQP1;FGFR1 -GO_Biological_Process_2018 lipid transport (GO:0006869) 3/91 0.12720480601215092 0.8103946630212312 0 0 2.4240465416936012 4.9982793608811065 CAV2;ATP8B1;CAV1 -GO_Biological_Process_2018 negative regulation of kinase activity (GO:0033673) 3/91 0.12720480601215092 0.8093841958603568 0 0 2.4240465416936012 4.9982793608811065 BGN;HSPB1;ASPN -GO_Biological_Process_2018 glomerular filtration (GO:0003094) 1/10 0.12799829502750046 0.8134188038920731 0 0 7.352941176470589 15.115723053300636 MCAM -GO_Biological_Process_2018 adrenal gland development (GO:0030325) 1/10 0.12799829502750046 0.8124070889618591 0 0 7.352941176470589 15.115723053300636 DKK3 -GO_Biological_Process_2018 endothelium development (GO:0003158) 1/10 0.12799829502750046 0.8113978876091115 0 0 7.352941176470589 15.115723053300636 JAG1 -GO_Biological_Process_2018 positive regulation of calcium ion import (GO:0090280) 1/10 0.12799829502750046 0.8103911904780827 0 0 7.352941176470589 15.115723053300636 PDGFRB -GO_Biological_Process_2018 regulation of coagulation (GO:0050818) 1/10 0.12799829502750046 0.8093869882593987 0 0 7.352941176470589 15.115723053300636 CAV1 -GO_Biological_Process_2018 adenylate cyclase-inhibiting dopamine receptor signaling pathway (GO:0007195) 1/10 0.12799829502750046 0.8083852716897707 0 0 7.352941176470589 15.115723053300636 FLNA -GO_Biological_Process_2018 regulation of actin filament length (GO:0030832) 1/10 0.12799829502750046 0.8073860315517116 0 0 7.352941176470589 15.115723053300636 EPS8 -GO_Biological_Process_2018 regulation of Arp2/3 complex-mediated actin nucleation (GO:0034315) 1/10 0.12799829502750046 0.8063892586732527 0 0 7.352941176470589 15.115723053300636 NCKAP1 -GO_Biological_Process_2018 contractile actin filament bundle assembly (GO:0030038) 1/10 0.12799829502750046 0.805394943927663 0 0 7.352941176470589 15.115723053300636 SORBS1 -GO_Biological_Process_2018 regulation of actin filament-based movement (GO:1903115) 1/10 0.12799829502750046 0.8044030782331708 0 0 7.352941176470589 15.115723053300636 PLN -GO_Biological_Process_2018 positive regulation of glucose import in response to insulin stimulus (GO:2001275) 1/10 0.12799829502750046 0.8034136525526873 0 0 7.352941176470589 15.115723053300636 SORBS1 -GO_Biological_Process_2018 glutamine metabolic process (GO:0006541) 1/10 0.12799829502750046 0.8024266578935316 0 0 7.352941176470589 15.115723053300636 GLS -GO_Biological_Process_2018 regulation of protein exit from endoplasmic reticulum (GO:0070861) 1/10 0.12799829502750046 0.8014420853071591 0 0 7.352941176470589 15.115723053300636 TMEM30A -GO_Biological_Process_2018 cellular copper ion homeostasis (GO:0006878) 1/10 0.12799829502750046 0.8004599258888906 0 0 7.352941176470589 15.115723053300636 APP -GO_Biological_Process_2018 astrocyte development (GO:0014002) 1/10 0.12799829502750046 0.7994801707776434 0 0 7.352941176470589 15.115723053300636 APP -GO_Biological_Process_2018 positive regulation of cardiac muscle cell differentiation (GO:2000727) 1/10 0.12799829502750046 0.7985028111556659 0 0 7.352941176470589 15.115723053300636 MEF2C -GO_Biological_Process_2018 positive regulation of lipase activity (GO:0060193) 1/10 0.12799829502750046 0.7975278382482719 0 0 7.352941176470589 15.115723053300636 FGFR1 -GO_Biological_Process_2018 positive regulation of natural killer cell activation (GO:0032816) 1/10 0.12799829502750046 0.7965552433235792 0 0 7.352941176470589 15.115723053300636 GAS6 -GO_Biological_Process_2018 glycoprotein catabolic process (GO:0006516) 1/10 0.12799829502750046 0.7955850176922469 0 0 7.352941176470589 15.115723053300636 MGEA5 -GO_Biological_Process_2018 negative regulation of cell activation (GO:0050866) 1/10 0.12799829502750046 0.7946171527072199 0 0 7.352941176470589 15.115723053300636 PDGFRA -GO_Biological_Process_2018 central nervous system projection neuron axonogenesis (GO:0021952) 1/10 0.12799829502750046 0.7936516397634686 0 0 7.352941176470589 15.115723053300636 NFIB -GO_Biological_Process_2018 cardiovascular system development (GO:0072358) 1/10 0.12799829502750046 0.7926884702977363 0 0 7.352941176470589 15.115723053300636 HSPG2 -GO_Biological_Process_2018 positive regulation of protein exit from endoplasmic reticulum (GO:0070863) 1/10 0.12799829502750046 0.7917276357882845 0 0 7.352941176470589 15.115723053300636 TMEM30A -GO_Biological_Process_2018 positive regulation of smooth muscle cell migration (GO:0014911) 1/10 0.12799829502750046 0.7907691277546425 0 0 7.352941176470589 15.115723053300636 PDGFRB -GO_Biological_Process_2018 regulation of gene expression by genetic imprinting (GO:0006349) 1/10 0.12799829502750046 0.7898129377573576 0 0 7.352941176470589 15.115723053300636 PCGF5 -GO_Biological_Process_2018 regulation of podosome assembly (GO:0071801) 1/10 0.12799829502750046 0.7888590573977472 0 0 7.352941176470589 15.115723053300636 GSN -GO_Biological_Process_2018 negative regulation of stem cell differentiation (GO:2000737) 1/10 0.12799829502750046 0.7879074783176534 0 0 7.352941176470589 15.115723053300636 JAG1 -GO_Biological_Process_2018 actin filament-based transport (GO:0099515) 1/10 0.12799829502750046 0.7869581921991985 0 0 7.352941176470589 15.115723053300636 ACTN4 -GO_Biological_Process_2018 positive regulation of insulin secretion involved in cellular response to glucose stimulus (GO:0035774) 1/10 0.12799829502750046 0.7860111907645424 0 0 7.352941176470589 15.115723053300636 ANO1 -GO_Biological_Process_2018 regulation of phospholipase activity (GO:0010517) 1/10 0.12799829502750046 0.7850664657756427 0 0 7.352941176470589 15.115723053300636 FGFR1 -GO_Biological_Process_2018 stress fiber assembly (GO:0043149) 1/10 0.12799829502750046 0.7841240090340152 0 0 7.352941176470589 15.115723053300636 SORBS1 -GO_Biological_Process_2018 UV protection (GO:0009650) 1/10 0.12799829502750046 0.7831838123804973 0 0 7.352941176470589 15.115723053300636 MFAP4 -GO_Biological_Process_2018 negative regulation of protein autophosphorylation (GO:0031953) 1/10 0.12799829502750046 0.7822458676950116 0 0 7.352941176470589 15.115723053300636 CAV1 -GO_Biological_Process_2018 chaperone-mediated protein transport (GO:0072321) 1/10 0.12799829502750046 0.7813101668963334 0 0 7.352941176470589 15.115723053300636 CLU -GO_Biological_Process_2018 positive regulation of execution phase of apoptosis (GO:1900119) 1/10 0.12799829502750046 0.7803767019418575 0 0 7.352941176470589 15.115723053300636 PTGIS -GO_Biological_Process_2018 dicarboxylic acid biosynthetic process (GO:0043650) 1/10 0.12799829502750046 0.7794454648273684 0 0 7.352941176470589 15.115723053300636 GLS -GO_Biological_Process_2018 negative regulation of homotypic cell-cell adhesion (GO:0034111) 1/10 0.12799829502750046 0.7785164475868114 0 0 7.352941176470589 15.115723053300636 PRKG1 -GO_Biological_Process_2018 endothelial tube morphogenesis (GO:0061154) 1/10 0.12799829502750046 0.7775896422920652 0 0 7.352941176470589 15.115723053300636 RHOB -GO_Biological_Process_2018 sequestering of actin monomers (GO:0042989) 1/10 0.12799829502750046 0.7766650410527166 0 0 7.352941176470589 15.115723053300636 GSN -GO_Biological_Process_2018 regulation of dendritic cell apoptotic process (GO:2000668) 1/10 0.12799829502750046 0.7757426360158369 0 0 7.352941176470589 15.115723053300636 GAS6 -GO_Biological_Process_2018 pulmonary valve development (GO:0003177) 1/10 0.12799829502750046 0.7748224193657588 0 0 7.352941176470589 15.115723053300636 JAG1 -GO_Biological_Process_2018 smooth muscle cell differentiation (GO:0051145) 1/10 0.12799829502750046 0.7739043833238562 0 0 7.352941176470589 15.115723053300636 MEF2C -GO_Biological_Process_2018 positive regulation of macromolecule biosynthetic process (GO:0010557) 4/142 0.12872713583467205 0.7773900285968419 0 0 2.071251035625518 4.24618960430536 PDGFRB;PDGFRA;FAM129A;SORBS1 -GO_Biological_Process_2018 neutrophil mediated immunity (GO:0002446) 10/487 0.129318182954916 0.7800362737812487 0 0 1.5098441840802033 3.0883551410040617 CYB5R3;GSN;TMEM30A;ROCK1;FGL2;TIMP2;HBB;SPTAN1;CD55;VCL -GO_Biological_Process_2018 regulation of G1/S transition of mitotic cell cycle (GO:2000045) 2/46 0.12938138314755313 0.7794961017732746 0 0 3.1969309462915603 6.537694302560821 FHL1;PKD2 -GO_Biological_Process_2018 negative regulation of protein modification by small protein conjugation or removal (GO:1903321) 2/46 0.12938138314755313 0.7785768846721268 0 0 3.1969309462915603 6.537694302560821 CAV1;OGT -GO_Biological_Process_2018 cellular protein complex assembly (GO:0043623) 4/144 0.1335072864609778 0.8024589903537924 0 0 2.042483660130719 4.112743509802361 SEPT7;FMOD;CLU;SKP1 -GO_Biological_Process_2018 negative regulation of cellular biosynthetic process (GO:0031327) 2/47 0.1339765848430687 0.8043323675931524 0 0 3.1289111389236544 6.289393724271383 PTGIS;CAV1 -GO_Biological_Process_2018 positive regulation of tumor necrosis factor production (GO:0032760) 2/47 0.1339765848430687 0.8033872061741241 0 0 3.1289111389236544 6.289393724271383 HSPB1;CLU -GO_Biological_Process_2018 regulation of organelle assembly (GO:1902115) 2/47 0.1339765848430687 0.8024442634438728 0 0 3.1289111389236544 6.289393724271383 SENP6;GSN -GO_Biological_Process_2018 histone H4 acetylation (GO:0043967) 2/47 0.1339765848430687 0.8015035315992727 0 0 3.1289111389236544 6.289393724271383 MORF4L2;OGT -GO_Biological_Process_2018 endosome to lysosome transport (GO:0008333) 2/48 0.13860814927485698 0.8282404985358258 0 0 3.063725490196078 6.054241412003446 SORT1;RHOB -GO_Biological_Process_2018 glycoprotein metabolic process (GO:0009100) 2/48 0.13860814927485698 0.827271796198357 0 0 3.063725490196078 6.054241412003446 MGEA5;MAN2A1 -GO_Biological_Process_2018 regulation of histone H3-K9 methylation (GO:0051570) 1/11 0.1398634329517421 0.8337886662999299 0 0 6.6844919786096275 13.148989380870011 ATRX -GO_Biological_Process_2018 interleukin-27-mediated signaling pathway (GO:0070106) 1/11 0.1398634329517421 0.8328157507033138 0 0 6.6844919786096275 13.148989380870011 IL6ST -GO_Biological_Process_2018 regulation of tumor necrosis factor biosynthetic process (GO:0042534) 1/11 0.1398634329517421 0.8318451029752214 0 0 6.6844919786096275 13.148989380870011 HSPB1 -GO_Biological_Process_2018 regulation of feeding behavior (GO:0060259) 1/11 0.1398634329517421 0.8308767151952735 0 0 6.6844919786096275 13.148989380870011 SGIP1 -GO_Biological_Process_2018 negative regulation of ryanodine-sensitive calcium-release channel activity (GO:0060315) 1/11 0.1398634329517421 0.8299105794799302 0 0 6.6844919786096275 13.148989380870011 PKD2 -GO_Biological_Process_2018 mononuclear cell differentiation (GO:1903131) 1/11 0.1398634329517421 0.8289466879822763 0 0 6.6844919786096275 13.148989380870011 MYH9 -GO_Biological_Process_2018 positive regulation of superoxide anion generation (GO:0032930) 1/11 0.1398634329517421 0.8279850328918097 0 0 6.6844919786096275 13.148989380870011 SOD1 -GO_Biological_Process_2018 positive regulation of extracellular matrix organization (GO:1903055) 1/11 0.1398634329517421 0.8270256064342294 0 0 6.6844919786096275 13.148989380870011 CLASP2 -GO_Biological_Process_2018 embryonic eye morphogenesis (GO:0048048) 1/11 0.1398634329517421 0.8260684008712268 0 0 6.6844919786096275 13.148989380870011 EFEMP1 -GO_Biological_Process_2018 modulation by host of viral process (GO:0044788) 1/11 0.1398634329517421 0.8251134085002774 0 0 6.6844919786096275 13.148989380870011 CAV2 -GO_Biological_Process_2018 regulation of non-canonical Wnt signaling pathway (GO:2000050) 1/11 0.1398634329517421 0.8241606216544342 0 0 6.6844919786096275 13.148989380870011 SFRP4 -GO_Biological_Process_2018 regulation of ventricular cardiac muscle cell action potential (GO:0098911) 1/11 0.1398634329517421 0.8232100327021222 0 0 6.6844919786096275 13.148989380870011 CAV1 -GO_Biological_Process_2018 relaxation of cardiac muscle (GO:0055119) 1/11 0.1398634329517421 0.8222616340469354 0 0 6.6844919786096275 13.148989380870011 PLN -GO_Biological_Process_2018 regulation of integrin-mediated signaling pathway (GO:2001044) 1/11 0.1398634329517421 0.8213154181274338 0 0 6.6844919786096275 13.148989380870011 FLNA -GO_Biological_Process_2018 mitochondrion distribution (GO:0048311) 1/11 0.1398634329517421 0.8203713774169424 0 0 6.6844919786096275 13.148989380870011 MEF2A -GO_Biological_Process_2018 lymphocyte mediated immunity (GO:0002449) 1/11 0.1398634329517421 0.8194295044233524 0 0 6.6844919786096275 13.148989380870011 JAG1 -GO_Biological_Process_2018 response to axon injury (GO:0048678) 1/11 0.1398634329517421 0.818489791688922 0 0 6.6844919786096275 13.148989380870011 SOD1 -GO_Biological_Process_2018 negative regulation of myotube differentiation (GO:0010832) 1/11 0.1398634329517421 0.8175522317900801 0 0 6.6844919786096275 13.148989380870011 NOV -GO_Biological_Process_2018 stress granule assembly (GO:0034063) 1/11 0.1398634329517421 0.8166168173372309 0 0 6.6844919786096275 13.148989380870011 RPS23 -GO_Biological_Process_2018 monocyte differentiation (GO:0030224) 1/11 0.1398634329517421 0.8156835409745601 0 0 6.6844919786096275 13.148989380870011 MYH9 -GO_Biological_Process_2018 renal filtration (GO:0097205) 1/11 0.1398634329517421 0.8147523953798401 0 0 6.6844919786096275 13.148989380870011 MCAM -GO_Biological_Process_2018 transcytosis (GO:0045056) 1/11 0.1398634329517421 0.8138233732642417 0 0 6.6844919786096275 13.148989380870011 USO1 -GO_Biological_Process_2018 negative regulation of protein localization to cell surface (GO:2000009) 1/11 0.1398634329517421 0.8128964673721412 0 0 6.6844919786096275 13.148989380870011 LEPROT -GO_Biological_Process_2018 relaxation of muscle (GO:0090075) 1/11 0.1398634329517421 0.8119716704809328 0 0 6.6844919786096275 13.148989380870011 PLN -GO_Biological_Process_2018 negative regulation of potassium ion transport (GO:0043267) 1/11 0.1398634329517421 0.8110489754008409 0 0 6.6844919786096275 13.148989380870011 CAV1 -GO_Biological_Process_2018 phosphate ion homeostasis (GO:0055062) 1/11 0.1398634329517421 0.8101283749747332 0 0 6.6844919786096275 13.148989380870011 SFRP4 -GO_Biological_Process_2018 metanephric mesenchyme development (GO:0072075) 1/11 0.1398634329517421 0.8092098620779364 0 0 6.6844919786096275 13.148989380870011 PKD2 -GO_Biological_Process_2018 response to nitric oxide (GO:0071731) 1/11 0.1398634329517421 0.8082934296180521 0 0 6.6844919786096275 13.148989380870011 AQP1 -GO_Biological_Process_2018 negative regulation of heart contraction (GO:0045822) 1/11 0.1398634329517421 0.8073790705347738 0 0 6.6844919786096275 13.148989380870011 PLN -GO_Biological_Process_2018 heart process (GO:0003015) 1/11 0.1398634329517421 0.8064667777997062 0 0 6.6844919786096275 13.148989380870011 SOD1 -GO_Biological_Process_2018 modulation by host of viral genome replication (GO:0044827) 1/11 0.1398634329517421 0.8055565444161851 0 0 6.6844919786096275 13.148989380870011 NUCKS1 -GO_Biological_Process_2018 cellular response to increased oxygen levels (GO:0036295) 1/11 0.1398634329517421 0.8046483634190981 0 0 6.6844919786096275 13.148989380870011 CAV1 -GO_Biological_Process_2018 cyclooxygenase pathway (GO:0019371) 1/11 0.1398634329517421 0.8037422278747072 0 0 6.6844919786096275 13.148989380870011 PTGIS -GO_Biological_Process_2018 negative regulation of release of sequestered calcium ion into cytosol (GO:0051280) 1/11 0.1398634329517421 0.8028381308804724 0 0 6.6844919786096275 13.148989380870011 PKD2 -GO_Biological_Process_2018 regulation of cardiac muscle cell differentiation (GO:2000725) 1/11 0.1398634329517421 0.8019360655648764 0 0 6.6844919786096275 13.148989380870011 MEF2C -GO_Biological_Process_2018 carbohydrate derivative catabolic process (GO:1901136) 1/11 0.1398634329517421 0.8010360250872502 0 0 6.6844919786096275 13.148989380870011 MGEA5 -GO_Biological_Process_2018 regulation of symbiosis, encompassing mutualism through parasitism (GO:0043903) 1/11 0.1398634329517421 0.8001380026376008 0 0 6.6844919786096275 13.148989380870011 CAV1 -GO_Biological_Process_2018 interleukin-35-mediated signaling pathway (GO:0070757) 1/11 0.1398634329517421 0.7992419914364389 0 0 6.6844919786096275 13.148989380870011 IL6ST -GO_Biological_Process_2018 spinal cord development (GO:0021510) 1/11 0.1398634329517421 0.7983479847346084 0 0 6.6844919786096275 13.148989380870011 PKD2 -GO_Biological_Process_2018 epithelial cell differentiation (GO:0030855) 3/96 0.14267707658726972 0.8134984601394831 0 0 2.297794117647059 4.474199007346493 TAGLN;JAG1;CNN3 -GO_Biological_Process_2018 regulation of megakaryocyte differentiation (GO:0045652) 2/49 0.14327424767052624 0.8159916136860439 0 0 3.0012004801920766 5.831316535130304 MEF2C;MYL9 -GO_Biological_Process_2018 wound healing (GO:0042060) 2/50 0.14797309400925476 0.8418134879924494 0 0 2.9411764705882355 5.61977887935267 PDGFRA;TPM1 -GO_Biological_Process_2018 negative regulation of protein ubiquitination (GO:0031397) 2/50 0.14797309400925476 0.8408760564913442 0 0 2.9411764705882355 5.61977887935267 CAV1;OGT -GO_Biological_Process_2018 receptor internalization (GO:0031623) 2/50 0.14797309400925476 0.839940710488573 0 0 2.9411764705882355 5.61977887935267 ITGB1;CAV1 -GO_Biological_Process_2018 negative regulation of cell differentiation (GO:0045596) 4/150 0.14827189571325802 0.8407016486941731 0 0 1.9607843137254903 3.7425638382069777 EFEMP1;JAG1;CAV1;SKIL -GO_Biological_Process_2018 pulmonary valve morphogenesis (GO:0003184) 1/12 0.15156771036528016 0.8584351009922583 0 0 6.127450980392156 11.560801597876258 JAG1 -GO_Biological_Process_2018 cardiac right ventricle morphogenesis (GO:0003215) 1/12 0.15156771036528016 0.8574833991064575 0 0 6.127450980392156 11.560801597876258 JAG1 -GO_Biological_Process_2018 negative regulation of chondrocyte differentiation (GO:0032331) 1/12 0.15156771036528016 0.8565338050875135 0 0 6.127450980392156 11.560801597876258 EFEMP1 -GO_Biological_Process_2018 positive regulation of interleukin-1 production (GO:0032732) 1/12 0.15156771036528016 0.8555863119402928 0 0 6.127450980392156 11.560801597876258 HSPB1 -GO_Biological_Process_2018 negative regulation of ATPase activity (GO:0032780) 1/12 0.15156771036528016 0.8546409127005798 0 0 6.127450980392156 11.560801597876258 PLN -GO_Biological_Process_2018 skeletal myofibril assembly (GO:0014866) 1/12 0.15156771036528016 0.8536976004349058 0 0 6.127450980392156 11.560801597876258 MYH11 -GO_Biological_Process_2018 positive regulation of skeletal muscle tissue development (GO:0048643) 1/12 0.15156771036528016 0.85275636824038 0 0 6.127450980392156 11.560801597876258 MEF2C -GO_Biological_Process_2018 regulation of actomyosin structure organization (GO:0110020) 1/12 0.15156771036528016 0.8518172092445206 0 0 6.127450980392156 11.560801597876258 ROCK1 -GO_Biological_Process_2018 skeletal muscle fiber development (GO:0048741) 1/12 0.15156771036528016 0.8508801166050877 0 0 6.127450980392156 11.560801597876258 CAV2 -GO_Biological_Process_2018 positive regulation of keratinocyte differentiation (GO:0045618) 1/12 0.15156771036528016 0.8499450835099173 0 0 6.127450980392156 11.560801597876258 FOXC1 -GO_Biological_Process_2018 negative regulation of substrate adhesion-dependent cell spreading (GO:1900025) 1/12 0.15156771036528016 0.849012103176756 0 0 6.127450980392156 11.560801597876258 ACTN4 -GO_Biological_Process_2018 positive regulation of cardiocyte differentiation (GO:1905209) 1/12 0.15156771036528016 0.8480811688530973 0 0 6.127450980392156 11.560801597876258 MEF2C -GO_Biological_Process_2018 positive regulation of ruffle assembly (GO:1900029) 1/12 0.15156771036528016 0.8471522738160183 0 0 6.127450980392156 11.560801597876258 EPS8 -GO_Biological_Process_2018 regulation of protein localization to cell periphery (GO:1904375) 1/12 0.15156771036528016 0.8462254113720182 0 0 6.127450980392156 11.560801597876258 SPTBN1 -GO_Biological_Process_2018 cargo loading into COPII-coated vesicle (GO:0090110) 1/12 0.15156771036528016 0.8453005748568576 0 0 6.127450980392156 11.560801597876258 SEC31A -GO_Biological_Process_2018 regulation of skeletal muscle tissue development (GO:0048641) 1/12 0.15156771036528016 0.8443777576353981 0 0 6.127450980392156 11.560801597876258 MEF2C -GO_Biological_Process_2018 response to interleukin-6 (GO:0070741) 1/12 0.15156771036528016 0.8434569531014446 0 0 6.127450980392156 11.560801597876258 PTGIS -GO_Biological_Process_2018 regulation of protein import into nucleus, translocation (GO:0033158) 1/12 0.15156771036528016 0.8425381546775869 0 0 6.127450980392156 11.560801597876258 GAS6 -GO_Biological_Process_2018 positive regulation of DNA-dependent DNA replication (GO:2000105) 1/12 0.15156771036528016 0.8416213558150432 0 0 6.127450980392156 11.560801597876258 ATRX -GO_Biological_Process_2018 negative regulation of interleukin-1 secretion (GO:0050711) 1/12 0.15156771036528016 0.8407065499935051 0 0 6.127450980392156 11.560801597876258 GAS6 -GO_Biological_Process_2018 establishment of protein localization to mitochondrial membrane (GO:0090151) 1/12 0.15156771036528016 0.8397937307209823 0 0 6.127450980392156 11.560801597876258 TOMM7 -GO_Biological_Process_2018 regulation of T cell differentiation in thymus (GO:0033081) 1/12 0.15156771036528016 0.8388828915336494 0 0 6.127450980392156 11.560801597876258 SOD1 -GO_Biological_Process_2018 negative regulation of leukocyte apoptotic process (GO:2000107) 1/12 0.15156771036528016 0.837974025995693 0 0 6.127450980392156 11.560801597876258 GAS6 -GO_Biological_Process_2018 regulation of catabolic process (GO:0009894) 1/12 0.15156771036528016 0.8370671276991609 0 0 6.127450980392156 11.560801597876258 ITGB1 -GO_Biological_Process_2018 microtubule anchoring (GO:0034453) 1/12 0.15156771036528016 0.8361621902638106 0 0 6.127450980392156 11.560801597876258 CLASP2 -GO_Biological_Process_2018 positive regulation of SMAD protein import into nucleus (GO:0060391) 1/12 0.15156771036528016 0.8352592073369597 0 0 6.127450980392156 11.560801597876258 RBPMS -GO_Biological_Process_2018 regulation of lipid metabolic process (GO:0019216) 3/100 0.1554809796525169 0.8559001501259911 0 0 2.2058823529411766 4.10565854159864 TBL1XR1;CAV1;CTGF -GO_Biological_Process_2018 cytokine-mediated signaling pathway (GO:0019221) 12/633 0.15589602037992956 0.8572601206883411 0 0 1.3939224979091165 2.590697003056296 ITGB1;IL33;FOXC1;ZEB1;MMP2;BGN;ILK;TNFRSF11B;IL6ST;ASPN;SKP1;SOD1 -GO_Biological_Process_2018 plasma membrane bounded cell projection morphogenesis (GO:0120039) 2/52 0.15746209469068284 0.8649397946249241 0 0 2.828054298642533 5.227857800657175 NCKAP1;CTTN -GO_Biological_Process_2018 positive regulation of mitotic cell cycle (GO:0045931) 2/52 0.15746209469068284 0.8640097518350048 0 0 2.828054298642533 5.227857800657175 PDGFRB;APP -GO_Biological_Process_2018 regulation of DNA binding (GO:0051101) 2/52 0.15746209469068284 0.8630817069887803 0 0 2.828054298642533 5.227857800657175 FOXC1;ID4 -GO_Biological_Process_2018 positive regulation of secretion by cell (GO:1903532) 2/52 0.15746209469068284 0.8621556536551014 0 0 2.828054298642533 5.227857800657175 MYH10;CLASP2 -GO_Biological_Process_2018 protein transport (GO:0015031) 7/326 0.15755426968070516 0.86173573224077 0 0 1.5788523998556478 2.917696042642288 CTTN;USO1;MYH9;ANK3;CLU;SPTBN1;SEC31A -GO_Biological_Process_2018 regulation of programmed cell death (GO:0043067) 6/268 0.15949733924460852 0.8714292528535731 0 0 1.6461808604038628 3.0219403622942806 PTGIS;ACTN1;IGFBP3;TNFRSF11B;ACTN4;CLU -GO_Biological_Process_2018 positive regulation of protein import into nucleus (GO:0042307) 2/53 0.16224888306189925 0.8855144922618949 0 0 2.774694783573807 5.046125991891576 RBPMS;FLNA -GO_Biological_Process_2018 regulation of interleukin-1 beta production (GO:0032651) 1/13 0.1631133000996947 0.8892811649666048 0 0 5.656108597285066 10.256279564503453 HSPB1 -GO_Biological_Process_2018 cellular response to exogenous dsRNA (GO:0071360) 1/13 0.1631133000996947 0.8883320922185082 0 0 5.656108597285066 10.256279564503453 CAV1 -GO_Biological_Process_2018 mitochondrial genome maintenance (GO:0000002) 1/13 0.1631133000996947 0.8873850430796825 0 0 5.656108597285066 10.256279564503453 MEF2A -GO_Biological_Process_2018 positive regulation of sodium ion transport (GO:0010765) 1/13 0.1631133000996947 0.8864400110849224 0 0 5.656108597285066 10.256279564503453 ANK3 -GO_Biological_Process_2018 positive regulation of cardiac muscle cell proliferation (GO:0060045) 1/13 0.1631133000996947 0.8854969897965342 0 0 5.656108597285066 10.256279564503453 MEF2C -GO_Biological_Process_2018 atrial cardiac muscle cell action potential (GO:0086014) 1/13 0.1631133000996947 0.8845559728041893 0 0 5.656108597285066 10.256279564503453 GJA1 -GO_Biological_Process_2018 regulation of superoxide anion generation (GO:0032928) 1/13 0.1631133000996947 0.8836169537247793 0 0 5.656108597285066 10.256279564503453 SOD1 -GO_Biological_Process_2018 embryonic appendage morphogenesis (GO:0035113) 1/13 0.1631133000996947 0.8826799262022716 0 0 5.656108597285066 10.256279564503453 MBNL1 -GO_Biological_Process_2018 glucosamine-containing compound metabolic process (GO:1901071) 1/13 0.1631133000996947 0.8817448839075658 0 0 5.656108597285066 10.256279564503453 MGEA5 -GO_Biological_Process_2018 DNA replication-independent nucleosome organization (GO:0034724) 1/13 0.1631133000996947 0.8808118205383515 0 0 5.656108597285066 10.256279564503453 ATRX -GO_Biological_Process_2018 Notch receptor processing, ligand-dependent (GO:0035333) 1/13 0.1631133000996947 0.8798807298189664 0 0 5.656108597285066 10.256279564503453 JAG1 -GO_Biological_Process_2018 water transport (GO:0006833) 1/13 0.1631133000996947 0.8789516055002556 0 0 5.656108597285066 10.256279564503453 AQP1 -GO_Biological_Process_2018 regulation of glucose import in response to insulin stimulus (GO:2001273) 1/13 0.1631133000996947 0.8780244413594326 0 0 5.656108597285066 10.256279564503453 SORBS1 -GO_Biological_Process_2018 striated muscle myosin thick filament assembly (GO:0071688) 1/13 0.1631133000996947 0.8770992311999392 0 0 5.656108597285066 10.256279564503453 MYH11 -GO_Biological_Process_2018 regulation of biomineral tissue development (GO:0070167) 1/13 0.1631133000996947 0.8761759688513076 0 0 5.656108597285066 10.256279564503453 GAS6 -GO_Biological_Process_2018 positive regulation of glycogen biosynthetic process (GO:0045725) 1/13 0.1631133000996947 0.8752546481690243 0 0 5.656108597285066 10.256279564503453 SORBS1 -GO_Biological_Process_2018 myelination in peripheral nervous system (GO:0022011) 1/13 0.1631133000996947 0.8743352630343929 0 0 5.656108597285066 10.256279564503453 SOD1 -GO_Biological_Process_2018 multivesicular body sorting pathway (GO:0071985) 1/13 0.1631133000996947 0.8734178073543989 0 0 5.656108597285066 10.256279564503453 SORT1 -GO_Biological_Process_2018 placenta development (GO:0001890) 1/13 0.1631133000996947 0.8725022750615745 0 0 5.656108597285066 10.256279564503453 SOD1 -GO_Biological_Process_2018 glutamine family amino acid biosynthetic process (GO:0009084) 1/13 0.1631133000996947 0.8715886601138662 0 0 5.656108597285066 10.256279564503453 GLS -GO_Biological_Process_2018 regulation of cAMP metabolic process (GO:0030814) 1/13 0.1631133000996947 0.8706769564945002 0 0 5.656108597285066 10.256279564503453 PKD2 -GO_Biological_Process_2018 copper ion homeostasis (GO:0055070) 1/13 0.1631133000996947 0.8697671582118518 0 0 5.656108597285066 10.256279564503453 APP -GO_Biological_Process_2018 response to UV-B (GO:0010224) 1/13 0.1631133000996947 0.8688592592993133 0 0 5.656108597285066 10.256279564503453 MFAP4 -GO_Biological_Process_2018 leukocyte differentiation (GO:0002521) 1/13 0.1631133000996947 0.8679532538151639 0 0 5.656108597285066 10.256279564503453 GAS6 -GO_Biological_Process_2018 regulation of execution phase of apoptosis (GO:1900117) 1/13 0.1631133000996947 0.8670491358424397 0 0 5.656108597285066 10.256279564503453 PTGIS -GO_Biological_Process_2018 positive regulation of muscle contraction (GO:0045933) 1/13 0.1631133000996947 0.8661468994888055 0 0 5.656108597285066 10.256279564503453 CTTN -GO_Biological_Process_2018 establishment of chromosome localization (GO:0051303) 1/13 0.1631133000996947 0.8652465388864263 0 0 5.656108597285066 10.256279564503453 GEM -GO_Biological_Process_2018 protein localization to endoplasmic reticulum (GO:0070972) 1/13 0.1631133000996947 0.8643480481918402 0 0 5.656108597285066 10.256279564503453 BCAP29 -GO_Biological_Process_2018 regulation of endothelial cell chemotaxis (GO:2001026) 1/13 0.1631133000996947 0.8634514215858321 0 0 5.656108597285066 10.256279564503453 HSPB1 -GO_Biological_Process_2018 prostanoid biosynthetic process (GO:0046457) 1/13 0.1631133000996947 0.8625566532733079 0 0 5.656108597285066 10.256279564503453 PTGIS -GO_Biological_Process_2018 positive regulation of lamellipodium assembly (GO:0010592) 1/13 0.1631133000996947 0.8616637374831699 0 0 5.656108597285066 10.256279564503453 NCKAP1 -GO_Biological_Process_2018 regulation of neurotransmitter transport (GO:0051588) 1/13 0.1631133000996947 0.8607726684681924 0 0 5.656108597285066 10.256279564503453 MEF2C -GO_Biological_Process_2018 positive regulation of ERAD pathway (GO:1904294) 1/13 0.1631133000996947 0.8598834405048991 0 0 5.656108597285066 10.256279564503453 CAV1 -GO_Biological_Process_2018 secretion (GO:0046903) 1/13 0.1631133000996947 0.8589960478934388 0 0 5.656108597285066 10.256279564503453 FBLN5 -GO_Biological_Process_2018 positive regulation of alpha-beta T cell proliferation (GO:0046641) 1/13 0.1631133000996947 0.8581104849574661 0 0 5.656108597285066 10.256279564503453 CD55 -GO_Biological_Process_2018 regulation of transcription initiation from RNA polymerase II promoter (GO:0060260) 1/13 0.1631133000996947 0.8572267460440187 0 0 5.656108597285066 10.256279564503453 SUB1 -GO_Biological_Process_2018 glomerular visceral epithelial cell differentiation (GO:0072112) 1/13 0.1631133000996947 0.8563448255233973 0 0 5.656108597285066 10.256279564503453 JAG1 -GO_Biological_Process_2018 negative regulation of cartilage development (GO:0061037) 1/13 0.1631133000996947 0.8554647177890464 0 0 5.656108597285066 10.256279564503453 EFEMP1 -GO_Biological_Process_2018 endothelial cell differentiation (GO:0045446) 1/13 0.1631133000996947 0.8545864172574354 0 0 5.656108597285066 10.256279564503453 JAG1 -GO_Biological_Process_2018 ribonucleoprotein complex disassembly (GO:0032988) 1/13 0.1631133000996947 0.8537099183679406 0 0 5.656108597285066 10.256279564503453 KLC1 -GO_Biological_Process_2018 plasma membrane invagination (GO:0099024) 3/103 0.16531039302242875 0.8643226799113257 0 0 2.1416333523700746 3.8547909817948094 GSN;MYH9;GULP1 -GO_Biological_Process_2018 cytoplasmic translation (GO:0002181) 2/54 0.16706168577706124 0.8725852431119175 0 0 2.7233115468409586 4.873072328042818 RPL35A;RPS23 -GO_Biological_Process_2018 positive regulation of response to stimulus (GO:0048584) 2/54 0.16706168577706124 0.8716930291619054 0 0 2.7233115468409586 4.873072328042818 ANO1;ILK -GO_Biological_Process_2018 phagocytosis, engulfment (GO:0006911) 3/104 0.16862721850390616 0.8789629172884915 0 0 2.1210407239819005 3.7755899493186655 GSN;MYH9;GULP1 -GO_Biological_Process_2018 nervous system development (GO:0007399) 9/455 0.16908916000398996 0.880471411735062 0 0 1.4544279250161605 2.5849971174374065 NCKAP1;MBNL1;MEF2C;VCAN;JAG1;CRIM1;PKD2;KALRN;FGFR1 -GO_Biological_Process_2018 cellular response to oxygen-containing compound (GO:1901701) 6/274 0.17114898338216636 0.8902887484191591 0 0 1.6101331043366254 2.842240533404877 PDGFRA;MEF2C;IGFBP5;TPM1;PKD2;AQP1 -GO_Biological_Process_2018 positive regulation of cellular process (GO:0048522) 10/519 0.17142976546949762 0.8908412354285604 0 0 1.4167516717669726 2.4985572184161846 PDGFRB;PDGFRA;CAV1;HBB;FLNA;ACTN4;HBA1;IL6ST;CTGF;FGFR1 -GO_Biological_Process_2018 regulation of cellular catabolic process (GO:0031329) 2/55 0.17189891825679854 0.8923704779902778 0 0 2.6737967914438503 4.7081514959262964 ROCK1;HSPB1 -GO_Biological_Process_2018 DNA recombination (GO:0006310) 2/55 0.17189891825679854 0.8914635974232145 0 0 2.6737967914438503 4.7081514959262964 ATRX;NUCKS1 -GO_Biological_Process_2018 regulation of gene expression (GO:0010468) 18/1037 0.17308200629775258 0.8966877950633823 0 0 1.2763060865619151 2.238627821714969 APP;FOXC1;MBNL1;MEF2C;GSN;ROCK1;SORT1;ATRX;ANK3;CLU;ACTA2;SFRP4;EFEMP1;NFIA;APBB2;MATR3;GAS6;ZNF532 -GO_Biological_Process_2018 central nervous system development (GO:0007417) 5/217 0.1744475137648933 0.9028454997385909 0 0 1.6942260775277855 2.9583412904923283 NCKAP1;VCAN;PKD2;HSPG2;FGFR1 -GO_Biological_Process_2018 ncRNA transcription (GO:0098781) 1/14 0.17450234575219342 0.9022142556975108 0 0 5.2521008403361344 9.169207430273257 CAVIN1 -GO_Biological_Process_2018 fluid transport (GO:0042044) 1/14 0.17450234575219342 0.9013010833739302 0 0 5.2521008403361344 9.169207430273257 AQP1 -GO_Biological_Process_2018 gliogenesis (GO:0042063) 1/14 0.17450234575219342 0.9003897577082336 0 0 5.2521008403361344 9.169207430273257 NFIB -GO_Biological_Process_2018 myotube cell development (GO:0014904) 1/14 0.17450234575219342 0.8994802731044879 0 0 5.2521008403361344 9.169207430273257 CAV2 -GO_Biological_Process_2018 regulation of platelet aggregation (GO:0090330) 1/14 0.17450234575219342 0.8985726239893471 0 0 5.2521008403361344 9.169207430273257 PRKG1 -GO_Biological_Process_2018 positive regulation of response to cytokine stimulus (GO:0060760) 1/14 0.17450234575219342 0.8976668048119385 0 0 5.2521008403361344 9.169207430273257 GAS6 -GO_Biological_Process_2018 regulation of extracellular matrix disassembly (GO:0010715) 1/14 0.17450234575219342 0.8967628100437492 0 0 5.2521008403361344 9.169207430273257 CLASP2 -GO_Biological_Process_2018 cell communication by electrical coupling involved in cardiac conduction (GO:0086064) 1/14 0.17450234575219342 0.8958606341785141 0 0 5.2521008403361344 9.169207430273257 GJA1 -GO_Biological_Process_2018 cell surface receptor signaling pathway involved in heart development (GO:0061311) 1/14 0.17450234575219342 0.8949602717321036 0 0 5.2521008403361344 9.169207430273257 JAG1 -GO_Biological_Process_2018 thymus development (GO:0048538) 1/14 0.17450234575219342 0.8940617172424127 0 0 5.2521008403361344 9.169207430273257 SOD1 -GO_Biological_Process_2018 N-acetylglucosamine metabolic process (GO:0006044) 1/14 0.17450234575219342 0.8931649652692508 0 0 5.2521008403361344 9.169207430273257 MGEA5 -GO_Biological_Process_2018 cardiac left ventricle morphogenesis (GO:0003214) 1/14 0.17450234575219342 0.8922700103942315 0 0 5.2521008403361344 9.169207430273257 CPE -GO_Biological_Process_2018 positive regulation of glycogen metabolic process (GO:0070875) 1/14 0.17450234575219342 0.8913768472206637 0 0 5.2521008403361344 9.169207430273257 SORBS1 -GO_Biological_Process_2018 regulation of cyclin-dependent protein serine/threonine kinase activity involved in G1/S transition of mitotic cell cycle (GO:0031657) 1/14 0.17450234575219342 0.890485470373443 0 0 5.2521008403361344 9.169207430273257 PKD2 -GO_Biological_Process_2018 response to ethanol (GO:0045471) 1/14 0.17450234575219342 0.8895958744989441 0 0 5.2521008403361344 9.169207430273257 SOD1 -GO_Biological_Process_2018 regulation of ventricular cardiac muscle cell membrane repolarization (GO:0060307) 1/14 0.17450234575219342 0.8887080542649132 0 0 5.2521008403361344 9.169207430273257 CACNA2D1 -GO_Biological_Process_2018 regulation of fatty acid metabolic process (GO:0019217) 1/14 0.17450234575219342 0.887822004360362 0 0 5.2521008403361344 9.169207430273257 CAV1 -GO_Biological_Process_2018 adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains (GO:0002460) 1/14 0.17450234575219342 0.8869377194954612 0 0 5.2521008403361344 9.169207430273257 JAG1 -GO_Biological_Process_2018 positive regulation of vascular smooth muscle cell proliferation (GO:1904707) 1/14 0.17450234575219342 0.8860551944014359 0 0 5.2521008403361344 9.169207430273257 MMP2 -GO_Biological_Process_2018 cellular response to nitrogen compound (GO:1901699) 1/14 0.17450234575219342 0.8851744238304603 0 0 5.2521008403361344 9.169207430273257 ATRX -GO_Biological_Process_2018 positive regulation of heart rate (GO:0010460) 1/14 0.17450234575219342 0.8842954025555542 0 0 5.2521008403361344 9.169207430273257 TPM1 -GO_Biological_Process_2018 interleukin-6-mediated signaling pathway (GO:0070102) 1/14 0.17450234575219342 0.8834181253704793 0 0 5.2521008403361344 9.169207430273257 IL6ST -GO_Biological_Process_2018 histone H4-K16 acetylation (GO:0043984) 1/14 0.17450234575219342 0.8825425870896363 0 0 5.2521008403361344 9.169207430273257 OGT -GO_Biological_Process_2018 regulation of adaptive immune response (GO:0002819) 1/14 0.17450234575219342 0.8816687825479634 0 0 5.2521008403361344 9.169207430273257 IL6ST -GO_Biological_Process_2018 positive regulation of smooth muscle contraction (GO:0045987) 1/14 0.17450234575219342 0.8807967066008339 0 0 5.2521008403361344 9.169207430273257 CTTN -GO_Biological_Process_2018 negative regulation of cation transmembrane transport (GO:1904063) 1/14 0.17450234575219342 0.8799263541239556 0 0 5.2521008403361344 9.169207430273257 CAV1 -GO_Biological_Process_2018 protein modification by small protein conjugation (GO:0032446) 8/398 0.17661318220051248 0.8896910846685243 0 0 1.4779781259237363 2.5625086449911603 PCNP;RNF11;EPAS1;UBA2;LMO7;FBXO32;PJA2;SKP1 -GO_Biological_Process_2018 regulation of tumor necrosis factor-mediated signaling pathway (GO:0010803) 2/56 0.17675903387387054 0.8895476823060765 0 0 2.6260504201680672 4.550860988669741 CCDC3;GAS6 -GO_Biological_Process_2018 negative regulation of cellular catabolic process (GO:0031330) 2/56 0.17675903387387054 0.8886712806486321 0 0 2.6260504201680672 4.550860988669741 ROCK1;TIMP2 -GO_Biological_Process_2018 regulation of response to external stimulus (GO:0032101) 3/107 0.17869150382275664 0.8975027007948102 0 0 2.061572292468389 3.5502221048123563 PDGFRA;CAV1;CD55 -GO_Biological_Process_2018 viral process (GO:0016032) 5/220 0.18127734280106367 0.90959516255047 0 0 1.6711229946524069 2.8538220918978037 NFIA;RPL35A;NUCKS1;GAS6;RPS23 -GO_Biological_Process_2018 chloride transmembrane transport (GO:1902476) 2/57 0.18164052322941088 0.9105221906087266 0 0 2.5799793601651193 4.400737079906612 ANO1;CLIC4 -GO_Biological_Process_2018 epidermal growth factor receptor signaling pathway (GO:0007173) 2/57 0.18164052322941088 0.9096286457700526 0 0 2.5799793601651193 4.400737079906612 EPS8;EFEMP1 -GO_Biological_Process_2018 protein homotetramerization (GO:0051289) 2/57 0.18164052322941088 0.908736852980082 0 0 2.5799793601651193 4.400737079906612 PKD2;GLS -GO_Biological_Process_2018 regulation of protein activation cascade (GO:2000257) 3/108 0.18208248755349327 0.9100557629632479 0 0 2.042483660130719 3.4789531582238045 SERPING1;CLU;CD55 -GO_Biological_Process_2018 positive regulation of I-kappaB kinase/NF-kappaB signaling (GO:0043123) 4/163 0.1822432479176688 0.9099679981642502 0 0 1.804402742692169 3.0718386092428798 APP;GJA1;FLNA;S100A4 -GO_Biological_Process_2018 regulation of complement activation (GO:0030449) 3/109 0.18549073056974366 0.9252778085018594 0 0 2.023745277927685 3.409505602045904 SERPING1;CLU;CD55 -GO_Biological_Process_2018 regulation of Wnt signaling pathway (GO:0030111) 3/109 0.18549073056974366 0.9243742168919944 0 0 2.023745277927685 3.409505602045904 APP;SFRP4;FRZB -GO_Biological_Process_2018 myeloid cell development (GO:0061515) 1/15 0.185736962021256 0.9246982606775309 0 0 4.901960784313727 8.252077396325646 APP -GO_Biological_Process_2018 positive regulation of alpha-beta T cell activation (GO:0046635) 1/15 0.185736962021256 0.9237969953162468 0 0 4.901960784313727 8.252077396325646 CD55 -GO_Biological_Process_2018 NAD biosynthesis via nicotinamide riboside salvage pathway (GO:0034356) 1/15 0.185736962021256 0.9228974850968544 0 0 4.901960784313727 8.252077396325646 PTGIS -GO_Biological_Process_2018 septin ring assembly (GO:0000921) 1/15 0.185736962021256 0.9219997248973436 0 0 4.901960784313727 8.252077396325646 SEPT7 -GO_Biological_Process_2018 digestive tract morphogenesis (GO:0048546) 1/15 0.185736962021256 0.9211037096156164 0 0 4.901960784313727 8.252077396325646 PDGFRA -GO_Biological_Process_2018 histone H4-K8 acetylation (GO:0043982) 1/15 0.185736962021256 0.9202094341693876 0 0 4.901960784313727 8.252077396325646 OGT -GO_Biological_Process_2018 positive regulation of response to wounding (GO:1903036) 1/15 0.185736962021256 0.9193168934960906 0 0 4.901960784313727 8.252077396325646 MYLK -GO_Biological_Process_2018 positive regulation of vasoconstriction (GO:0045907) 1/15 0.185736962021256 0.9184260825527804 0 0 4.901960784313727 8.252077396325646 CAV1 -GO_Biological_Process_2018 response to X-ray (GO:0010165) 1/15 0.185736962021256 0.91753699631604 0 0 4.901960784313727 8.252077396325646 NUCKS1 -GO_Biological_Process_2018 protein localization to chromosome, telomeric region (GO:0070198) 1/15 0.185736962021256 0.9166496297818852 0 0 4.901960784313727 8.252077396325646 ATRX -GO_Biological_Process_2018 substrate-dependent cell migration (GO:0006929) 1/15 0.185736962021256 0.9157639779656708 0 0 4.901960784313727 8.252077396325646 ITGA11 -GO_Biological_Process_2018 transport along microtubule (GO:0010970) 1/15 0.185736962021256 0.9148800359019974 0 0 4.901960784313727 8.252077396325646 APP -GO_Biological_Process_2018 positive regulation of potassium ion transmembrane transport (GO:1901381) 1/15 0.185736962021256 0.9139977986446184 0 0 4.901960784313727 8.252077396325646 FLNA -GO_Biological_Process_2018 negative regulation of insulin secretion (GO:0046676) 1/15 0.185736962021256 0.9131172612663482 0 0 4.901960784313727 8.252077396325646 NOV -GO_Biological_Process_2018 cardiac muscle cell differentiation (GO:0055007) 1/15 0.185736962021256 0.9122384188589696 0 0 4.901960784313727 8.252077396325646 MEF2C -GO_Biological_Process_2018 mitochondrion localization (GO:0051646) 1/15 0.185736962021256 0.9113612665331436 0 0 4.901960784313727 8.252077396325646 MEF2A -GO_Biological_Process_2018 regulation of clathrin-dependent endocytosis (GO:2000369) 1/15 0.185736962021256 0.9104857994183184 0 0 4.901960784313727 8.252077396325646 ROCK1 -GO_Biological_Process_2018 regulation of cell-substrate junction assembly (GO:0090109) 1/15 0.185736962021256 0.9096120126626386 0 0 4.901960784313727 8.252077396325646 ROCK1 -GO_Biological_Process_2018 negative regulation of release of cytochrome c from mitochondria (GO:0090201) 1/15 0.185736962021256 0.9087399014328564 0 0 4.901960784313727 8.252077396325646 CLU -GO_Biological_Process_2018 negative regulation of cellular component movement (GO:0051271) 1/15 0.185736962021256 0.9078694609142428 0 0 4.901960784313727 8.252077396325646 ACTN1 -GO_Biological_Process_2018 positive regulation of acute inflammatory response (GO:0002675) 1/15 0.185736962021256 0.9070006863104971 0 0 4.901960784313727 8.252077396325646 IL6ST -GO_Biological_Process_2018 neural crest cell differentiation (GO:0014033) 1/15 0.185736962021256 0.9061335728436608 0 0 4.901960784313727 8.252077396325646 MEF2C -GO_Biological_Process_2018 mesenchymal cell development (GO:0014031) 1/15 0.185736962021256 0.9052681157540301 0 0 4.901960784313727 8.252077396325646 FOXC1 -GO_Biological_Process_2018 regulation of delayed rectifier potassium channel activity (GO:1902259) 1/15 0.185736962021256 0.9044043103000662 0 0 4.901960784313727 8.252077396325646 ANK3 -GO_Biological_Process_2018 positive regulation of viral release from host cell (GO:1902188) 1/15 0.185736962021256 0.903542151758312 0 0 4.901960784313727 8.252077396325646 CAV2 -GO_Biological_Process_2018 negative regulation of viral entry into host cell (GO:0046597) 1/15 0.185736962021256 0.9026816354233042 0 0 4.901960784313727 8.252077396325646 GSN -GO_Biological_Process_2018 positive regulation of cellular response to insulin stimulus (GO:1900078) 1/15 0.185736962021256 0.9018227566074876 0 0 4.901960784313727 8.252077396325646 SORBS1 -GO_Biological_Process_2018 amide transport (GO:0042886) 1/15 0.185736962021256 0.9009655106411306 0 0 4.901960784313727 8.252077396325646 SLC14A1 -GO_Biological_Process_2018 positive regulation of NF-kappaB import into nucleus (GO:0042346) 1/15 0.185736962021256 0.9001098928722406 0 0 4.901960784313727 8.252077396325646 APP -GO_Biological_Process_2018 monovalent inorganic anion homeostasis (GO:0055083) 1/15 0.185736962021256 0.8992558986664795 0 0 4.901960784313727 8.252077396325646 SFRP4 -GO_Biological_Process_2018 negative regulation of protein processing (GO:0010955) 1/15 0.185736962021256 0.8984035234070801 0 0 4.901960784313727 8.252077396325646 CD55 -GO_Biological_Process_2018 embryonic cranial skeleton morphogenesis (GO:0048701) 1/15 0.185736962021256 0.8975527624947627 0 0 4.901960784313727 8.252077396325646 PDGFRA -GO_Biological_Process_2018 positive regulation of cysteine-type endopeptidase activity involved in apoptotic signaling pathway (GO:2001269) 1/15 0.185736962021256 0.8967036113476531 0 0 4.901960784313727 8.252077396325646 GSN -GO_Biological_Process_2018 positive regulation of protein export from nucleus (GO:0046827) 1/15 0.185736962021256 0.8958560654011998 0 0 4.901960784313727 8.252077396325646 GAS6 -GO_Biological_Process_2018 histone H4-K5 acetylation (GO:0043981) 1/15 0.185736962021256 0.8950101201080919 0 0 4.901960784313727 8.252077396325646 OGT -GO_Biological_Process_2018 regulation of cell junction assembly (GO:1901888) 1/15 0.185736962021256 0.8941657709381786 0 0 4.901960784313727 8.252077396325646 TJP1 -GO_Biological_Process_2018 phospholipid transport (GO:0015914) 2/58 0.18654191351699626 0.8971945190171835 0 0 2.5354969574036508 4.257351236793548 TMEM30A;ATP8B1 -GO_Biological_Process_2018 receptor metabolic process (GO:0043112) 2/58 0.18654191351699626 0.8963497030859056 0 0 2.5354969574036508 4.257351236793548 ITGB1;CAV1 -GO_Biological_Process_2018 post-Golgi vesicle-mediated transport (GO:0006892) 2/58 0.18654191351699626 0.8955064766483836 0 0 2.5354969574036508 4.257351236793548 SORT1;VPS13A -GO_Biological_Process_2018 Ras protein signal transduction (GO:0007265) 5/223 0.1882053803190833 0.9026429095566559 0 0 1.6486415193880242 2.753596452014736 EPS8;NCKAP1;ROCK1;CDH13;RHOB -GO_Biological_Process_2018 regulation of protein metabolic process (GO:0051246) 2/59 0.19146176778668986 0.9173984986060828 0 0 2.4925224327018944 4.120306920029571 APP;LTBP4 -GO_Biological_Process_2018 renal system development (GO:0072001) 2/59 0.19146176778668986 0.9165378996392854 0 0 2.4925224327018944 4.120306920029571 FOXC1;ITGA8 -GO_Biological_Process_2018 keratinocyte differentiation (GO:0030216) 2/59 0.19146176778668986 0.9156789137914508 0 0 2.4925224327018944 4.120306920029571 CLIC4;JAG1 -GO_Biological_Process_2018 protein phosphorylation (GO:0006468) 9/470 0.19222489238635984 0.9184678144640396 0 0 1.4080100125156447 2.3219342148093483 PDGFRB;APP;PDGFRA;EFEMP1;IGFBP3;STK38L;ILK;GAS6;FGFR1 -GO_Biological_Process_2018 nucleosome assembly (GO:0006334) 2/60 0.19639868409618547 0.9375327267940452 0 0 2.4509803921568634 3.9892367235428257 ATRX;SMARCA5 -GO_Biological_Process_2018 neural tube development (GO:0021915) 1/16 0.19681923528001455 0.9386622033961816 0 0 4.595588235294118 7.469988781692437 PKD2 -GO_Biological_Process_2018 regulation of actin nucleation (GO:0051125) 1/16 0.19681923528001455 0.937785768098893 0 0 4.595588235294118 7.469988781692437 GSN -GO_Biological_Process_2018 negative regulation of NF-kappaB import into nucleus (GO:0042347) 1/16 0.19681923528001455 0.9369109679420844 0 0 4.595588235294118 7.469988781692437 NOV -GO_Biological_Process_2018 positive regulation of secretion (GO:0051047) 1/16 0.19681923528001455 0.9360377983540676 0 0 4.595588235294118 7.469988781692437 AQP1 -GO_Biological_Process_2018 Notch receptor processing (GO:0007220) 1/16 0.19681923528001455 0.9351662547801809 0 0 4.595588235294118 7.469988781692437 JAG1 -GO_Biological_Process_2018 positive regulation of cardiac muscle tissue growth (GO:0055023) 1/16 0.19681923528001455 0.9342963326827112 0 0 4.595588235294118 7.469988781692437 MEF2C -GO_Biological_Process_2018 cellular response to dsRNA (GO:0071359) 1/16 0.19681923528001455 0.9334280275408128 0 0 4.595588235294118 7.469988781692437 CAV1 -GO_Biological_Process_2018 negative regulation of immune effector process (GO:0002698) 1/16 0.19681923528001455 0.9325613348504312 0 0 4.595588235294118 7.469988781692437 CD55 -GO_Biological_Process_2018 DNA alkylation (GO:0006305) 1/16 0.19681923528001455 0.9316962501242247 0 0 4.595588235294118 7.469988781692437 ATRX -GO_Biological_Process_2018 regulation of membrane depolarization (GO:0003254) 1/16 0.19681923528001455 0.9308327688914868 0 0 4.595588235294118 7.469988781692437 FHL1 -GO_Biological_Process_2018 histone H2A acetylation (GO:0043968) 1/16 0.19681923528001455 0.9299708866980688 0 0 4.595588235294118 7.469988781692437 MORF4L2 -GO_Biological_Process_2018 cytoplasmic sequestering of transcription factor (GO:0042994) 1/16 0.19681923528001455 0.9291105991063038 0 0 4.595588235294118 7.469988781692437 PKD2 -GO_Biological_Process_2018 epithelial cell development (GO:0002064) 1/16 0.19681923528001455 0.92825190169493 0 0 4.595588235294118 7.469988781692437 ACTA2 -GO_Biological_Process_2018 negative regulation of striated muscle cell differentiation (GO:0051154) 1/16 0.19681923528001455 0.927394790059016 0 0 4.595588235294118 7.469988781692437 NOV -GO_Biological_Process_2018 negative regulation of steroid biosynthetic process (GO:0010894) 1/16 0.19681923528001455 0.9265392598098842 0 0 4.595588235294118 7.469988781692437 SOD1 -GO_Biological_Process_2018 positive regulation of lymphocyte differentiation (GO:0045621) 1/16 0.19681923528001455 0.9256853065750364 0 0 4.595588235294118 7.469988781692437 GAS6 -GO_Biological_Process_2018 regulation of energy homeostasis (GO:2000505) 1/16 0.19681923528001455 0.9248329259980796 0 0 4.595588235294118 7.469988781692437 SGIP1 -GO_Biological_Process_2018 DNA methylation or demethylation (GO:0044728) 1/16 0.19681923528001455 0.9239821137386516 0 0 4.595588235294118 7.469988781692437 ATRX -GO_Biological_Process_2018 glutamine family amino acid catabolic process (GO:0009065) 1/16 0.19681923528001455 0.923132865472348 0 0 4.595588235294118 7.469988781692437 GLS -GO_Biological_Process_2018 neuron apoptotic process (GO:0051402) 1/16 0.19681923528001455 0.9222851768906468 0 0 4.595588235294118 7.469988781692437 APP -GO_Biological_Process_2018 positive regulation of cilium assembly (GO:0045724) 1/16 0.19681923528001455 0.9214390437008392 0 0 4.595588235294118 7.469988781692437 SEPT7 -GO_Biological_Process_2018 rRNA transcription (GO:0009303) 1/16 0.19681923528001455 0.9205944616259528 0 0 4.595588235294118 7.469988781692437 CAVIN1 -GO_Biological_Process_2018 regulation of osteoblast proliferation (GO:0033688) 1/16 0.19681923528001455 0.9197514264046835 0 0 4.595588235294118 7.469988781692437 CYR61 -GO_Biological_Process_2018 bone morphogenesis (GO:0060349) 1/16 0.19681923528001455 0.9189099337913216 0 0 4.595588235294118 7.469988781692437 SFRP4 -GO_Biological_Process_2018 negative regulation of peptide hormone secretion (GO:0090278) 1/16 0.19681923528001455 0.9180699795556804 0 0 4.595588235294118 7.469988781692437 NOV -GO_Biological_Process_2018 regulation of protein homodimerization activity (GO:0043496) 1/16 0.19681923528001455 0.9172315594830268 0 0 4.595588235294118 7.469988781692437 MEF2C -GO_Biological_Process_2018 odontogenesis of dentin-containing tooth (GO:0042475) 1/16 0.19681923528001455 0.9163946693740096 0 0 4.595588235294118 7.469988781692437 FOXC1 -GO_Biological_Process_2018 artery development (GO:0060840) 1/16 0.19681923528001455 0.9155593050445892 0 0 4.595588235294118 7.469988781692437 PKD2 -GO_Biological_Process_2018 positive regulation of leukocyte activation (GO:0002696) 1/16 0.19681923528001455 0.9147254623259694 0 0 4.595588235294118 7.469988781692437 IL33 -GO_Biological_Process_2018 ruffle organization (GO:0031529) 1/16 0.19681923528001455 0.9138931370645264 0 0 4.595588235294118 7.469988781692437 TPM1 -GO_Biological_Process_2018 positive regulation of positive chemotaxis (GO:0050927) 1/16 0.19681923528001455 0.9130623251217403 0 0 4.595588235294118 7.469988781692437 CDH13 -GO_Biological_Process_2018 mesenchyme development (GO:0060485) 1/16 0.19681923528001455 0.9122330223741276 0 0 4.595588235294118 7.469988781692437 PKD2 -GO_Biological_Process_2018 regulation of intrinsic apoptotic signaling pathway in response to DNA damage (GO:1902229) 1/16 0.19681923528001455 0.9114052247131709 0 0 4.595588235294118 7.469988781692437 CLU -GO_Biological_Process_2018 positive regulation of DNA-templated transcription, initiation (GO:2000144) 1/16 0.19681923528001455 0.9105789280452532 0 0 4.595588235294118 7.469988781692437 SUB1 -GO_Biological_Process_2018 regulation of humoral immune response (GO:0002920) 3/113 0.19928618789578356 0.9211570804639344 0 0 1.9521082769390945 3.1487767254948458 SERPING1;CLU;CD55 -GO_Biological_Process_2018 positive regulation of T cell proliferation (GO:0042102) 2/61 0.20135129510036415 0.9298603247938084 0 0 2.410800385728062 3.8637998062201406 IGFBP2;IL6ST -GO_Biological_Process_2018 regulation of tumor necrosis factor production (GO:0032680) 2/61 0.20135129510036415 0.9290195830896548 0 0 2.410800385728062 3.8637998062201406 GAS6;CLU -GO_Biological_Process_2018 regulation of DNA replication (GO:0006275) 2/61 0.20135129510036415 0.9281803603407032 0 0 2.410800385728062 3.8637998062201406 PDGFRA;NUCKS1 -GO_Biological_Process_2018 endosomal transport (GO:0016197) 5/229 0.202340502062058 0.9318985397316624 0 0 1.6054456717184689 2.5651864675830804 MYO1D;TINAGL1;SORT1;VPS13A;RHOB -GO_Biological_Process_2018 regulation of immune effector process (GO:0002697) 3/114 0.2027731706243024 0.9330491340809874 0 0 1.9349845201238391 3.0875915456052483 SERPING1;CLU;CD55 -GO_Biological_Process_2018 protein O-linked glycosylation (GO:0006493) 3/115 0.20627440986599446 0.9483047869785316 0 0 1.918158567774936 3.0279051980271667 MGEA5;EOGT;OGT -GO_Biological_Process_2018 negative regulation of G2/M transition of mitotic cell cycle (GO:0010972) 2/62 0.20631826717083934 0.9476526709026044 0 0 2.3719165085389 3.743679592480466 FHL1;SKP1 -GO_Biological_Process_2018 negative regulation of intrinsic apoptotic signaling pathway (GO:2001243) 2/62 0.20631826717083934 0.9468004652633034 0 0 2.3719165085389 3.743679592480466 HSPB1;CLU -GO_Biological_Process_2018 cellular amide metabolic process (GO:0043603) 2/62 0.20631826717083934 0.9459497909908297 0 0 2.3719165085389 3.743679592480466 CPE;AEBP1 -GO_Biological_Process_2018 negative regulation of peptidase activity (GO:0010466) 2/62 0.20631826717083934 0.9451006439612148 0 0 2.3719165085389 3.743679592480466 TIMP2;SERPING1 -GO_Biological_Process_2018 positive regulation of transcription initiation from RNA polymerase II promoter (GO:0060261) 1/17 0.20775122366844606 0.9508112057220448 0 0 4.3252595155709335 6.796773162470168 SUB1 -GO_Biological_Process_2018 reverse cholesterol transport (GO:0043691) 1/17 0.20775122366844606 0.9499592243549104 0 0 4.3252595155709335 6.796773162470168 CLU -GO_Biological_Process_2018 positive regulation of focal adhesion assembly (GO:0051894) 1/17 0.20775122366844606 0.9491087684691852 0 0 4.3252595155709335 6.796773162470168 ROCK1 -GO_Biological_Process_2018 positive regulation of G-protein coupled receptor protein signaling pathway (GO:0045745) 1/17 0.20775122366844606 0.9482598339714491 0 0 4.3252595155709335 6.796773162470168 CAV2 -GO_Biological_Process_2018 response to interferon-alpha (GO:0035455) 1/17 0.20775122366844606 0.9474124167829132 0 0 4.3252595155709335 6.796773162470168 GAS6 -GO_Biological_Process_2018 positive regulation of lymphocyte migration (GO:2000403) 1/17 0.20775122366844606 0.9465665128393572 0 0 4.3252595155709335 6.796773162470168 APP -GO_Biological_Process_2018 microtubule nucleation (GO:0007020) 1/17 0.20775122366844606 0.9457221180910615 0 0 4.3252595155709335 6.796773162470168 CLASP2 -GO_Biological_Process_2018 cellular response to gamma radiation (GO:0071480) 1/17 0.20775122366844606 0.9448792285027452 0 0 4.3252595155709335 6.796773162470168 CRYAB -GO_Biological_Process_2018 positive regulation of nitrogen compound metabolic process (GO:0051173) 1/17 0.20775122366844606 0.9440378400534996 0 0 4.3252595155709335 6.796773162470168 APP -GO_Biological_Process_2018 cardiac muscle cell contraction (GO:0086003) 1/17 0.20775122366844606 0.9431979487367259 0 0 4.3252595155709335 6.796773162470168 CACNA2D1 -GO_Biological_Process_2018 histone H2A ubiquitination (GO:0033522) 1/17 0.20775122366844606 0.9423595505600711 0 0 4.3252595155709335 6.796773162470168 SKP1 -GO_Biological_Process_2018 positive regulation of striated muscle cell differentiation (GO:0051155) 1/17 0.20775122366844606 0.941522641545364 0 0 4.3252595155709335 6.796773162470168 MEF2C -GO_Biological_Process_2018 N-glycan processing (GO:0006491) 1/17 0.20775122366844606 0.9406872177285536 0 0 4.3252595155709335 6.796773162470168 MAN2A1 -GO_Biological_Process_2018 positive regulation of stem cell proliferation (GO:2000648) 1/17 0.20775122366844606 0.9398532751596452 0 0 4.3252595155709335 6.796773162470168 LTBP2 -GO_Biological_Process_2018 negative regulation of phosphoprotein phosphatase activity (GO:0032515) 1/17 0.20775122366844606 0.9390208099026396 0 0 4.3252595155709335 6.796773162470168 ROCK1 -GO_Biological_Process_2018 peptidyl-lysine trimethylation (GO:0018023) 1/17 0.20775122366844606 0.9381898180354691 0 0 4.3252595155709335 6.796773162470168 OGT -GO_Biological_Process_2018 embryo development ending in birth or egg hatching (GO:0009792) 1/17 0.20775122366844606 0.937360295649938 0 0 4.3252595155709335 6.796773162470168 FGFR1 -GO_Biological_Process_2018 positive regulation of calcium ion transmembrane transporter activity (GO:1901021) 1/17 0.20775122366844606 0.9365322388516608 0 0 4.3252595155709335 6.796773162470168 PKD2 -GO_Biological_Process_2018 long-term memory (GO:0007616) 1/17 0.20775122366844606 0.93570564376 0 0 4.3252595155709335 6.796773162470168 PJA2 -GO_Biological_Process_2018 macrophage activation (GO:0042116) 1/17 0.20775122366844606 0.934880506508007 0 0 4.3252595155709335 6.796773162470168 CLU -GO_Biological_Process_2018 regulation of transcription from RNA polymerase I promoter (GO:0006356) 1/17 0.20775122366844606 0.9340568232423612 0 0 4.3252595155709335 6.796773162470168 FLNA -GO_Biological_Process_2018 neurotrophin signaling pathway (GO:0038179) 1/17 0.20775122366844606 0.93323459012331 0 0 4.3252595155709335 6.796773162470168 SORT1 -GO_Biological_Process_2018 chaperone-mediated protein complex assembly (GO:0051131) 1/17 0.20775122366844606 0.9324138033246088 0 0 4.3252595155709335 6.796773162470168 CLU -GO_Biological_Process_2018 positive regulation by host of viral transcription (GO:0043923) 1/17 0.20775122366844606 0.9315944590334622 0 0 4.3252595155709335 6.796773162470168 NUCKS1 -GO_Biological_Process_2018 regulation of T cell migration (GO:2000404) 1/17 0.20775122366844606 0.9307765534504652 0 0 4.3252595155709335 6.796773162470168 APP -GO_Biological_Process_2018 regulation of establishment or maintenance of cell polarity (GO:0032878) 1/17 0.20775122366844606 0.929960082789544 0 0 4.3252595155709335 6.796773162470168 ROCK1 -GO_Biological_Process_2018 septin ring organization (GO:0031106) 1/17 0.20775122366844606 0.9291450432778966 0 0 4.3252595155709335 6.796773162470168 SEPT7 -GO_Biological_Process_2018 detection of mechanical stimulus (GO:0050982) 1/17 0.20775122366844606 0.928331431155937 0 0 4.3252595155709335 6.796773162470168 PKD2 -GO_Biological_Process_2018 cellular response to glucocorticoid stimulus (GO:0071385) 1/17 0.20775122366844606 0.9275192426772352 0 0 4.3252595155709335 6.796773162470168 AQP1 -GO_Biological_Process_2018 viral entry into host cell (GO:0046718) 1/17 0.20775122366844606 0.9267084741084616 0 0 4.3252595155709335 6.796773162470168 GAS6 -GO_Biological_Process_2018 membrane depolarization during cardiac muscle cell action potential (GO:0086012) 1/17 0.20775122366844606 0.9258991217293276 0 0 4.3252595155709335 6.796773162470168 CACNA2D1 -GO_Biological_Process_2018 chromatin remodeling (GO:0006338) 3/116 0.2097894156929583 0.9341670054809478 0 0 1.9016227180527383 2.9696710835480755 MORF4L2;ATRX;SMARCA5 -GO_Biological_Process_2018 kidney development (GO:0001822) 2/63 0.2112982997926156 0.9400655831226828 0 0 2.334267040149393 3.6285817012301624 FOXC1;ITGA8 -GO_Biological_Process_2018 vesicle coating (GO:0006901) 2/63 0.2112982997926156 0.9392467106635168 0 0 2.334267040149393 3.6285817012301624 USO1;SEC31A -GO_Biological_Process_2018 COPII vesicle coating (GO:0048208) 2/63 0.2112982997926156 0.9384292635698148 0 0 2.334267040149393 3.6285817012301624 USO1;SEC31A -GO_Biological_Process_2018 vesicle targeting, rough ER to cis-Golgi (GO:0048207) 2/63 0.2112982997926156 0.9376132381232324 0 0 2.334267040149393 3.6285817012301624 USO1;SEC31A -GO_Biological_Process_2018 negative regulation of neuron apoptotic process (GO:0043524) 2/63 0.2112982997926156 0.9367986306183468 0 0 2.334267040149393 3.6285817012301624 MEF2C;SOD1 -GO_Biological_Process_2018 inorganic anion transmembrane transport (GO:0098661) 2/64 0.21629012489710592 0.958097662630149 0 0 2.297794117647059 3.5182320811696566 ANO1;CLIC4 -GO_Biological_Process_2018 cytosolic transport (GO:0016482) 3/118 0.21685878384945695 0.9597834986849776 0 0 1.869391824526421 2.857382046894826 MYO1D;SORT1;VPS13A -GO_Biological_Process_2018 long-chain fatty-acyl-CoA biosynthetic process (GO:0035338) 1/18 0.21853495779522505 0.9663638558310516 0 0 4.084967320261438 6.212456239109543 HSD17B12 -GO_Biological_Process_2018 membrane protein ectodomain proteolysis (GO:0006509) 1/18 0.21853495779522505 0.9655271771679944 0 0 4.084967320261438 6.212456239109543 MYH9 -GO_Biological_Process_2018 regulation of spindle organization (GO:0090224) 1/18 0.21853495779522505 0.9646919460458768 0 0 4.084967320261438 6.212456239109543 SENP6 -GO_Biological_Process_2018 actin filament polymerization (GO:0030041) 1/18 0.21853495779522505 0.9638581587113514 0 0 4.084967320261438 6.212456239109543 GSN -GO_Biological_Process_2018 release of cytochrome c from mitochondria (GO:0001836) 1/18 0.21853495779522505 0.963025811424036 0 0 4.084967320261438 6.212456239109543 CLU -GO_Biological_Process_2018 endoplasmic reticulum calcium ion homeostasis (GO:0032469) 1/18 0.21853495779522505 0.9621949004564568 0 0 4.084967320261438 6.212456239109543 TGM2 -GO_Biological_Process_2018 maintenance of protein localization in organelle (GO:0072595) 1/18 0.21853495779522505 0.9613654220939944 0 0 4.084967320261438 6.212456239109543 SKP1 -GO_Biological_Process_2018 regulation of lipopolysaccharide-mediated signaling pathway (GO:0031664) 1/18 0.21853495779522505 0.9605373726348264 0 0 4.084967320261438 6.212456239109543 CD55 -GO_Biological_Process_2018 establishment or maintenance of apical/basal cell polarity (GO:0035088) 1/18 0.21853495779522505 0.959710748389874 0 0 4.084967320261438 6.212456239109543 CLIC4 -GO_Biological_Process_2018 regulation of dephosphorylation (GO:0035303) 1/18 0.21853495779522505 0.958885545682746 0 0 4.084967320261438 6.212456239109543 PPP1R14A -GO_Biological_Process_2018 prostaglandin biosynthetic process (GO:0001516) 1/18 0.21853495779522505 0.9580617608496852 0 0 4.084967320261438 6.212456239109543 PTGIS -GO_Biological_Process_2018 negative regulation of microtubule depolymerization (GO:0007026) 1/18 0.21853495779522505 0.9572393902395138 0 0 4.084967320261438 6.212456239109543 CLASP2 -GO_Biological_Process_2018 regulation of cardiac muscle cell proliferation (GO:0060043) 1/18 0.21853495779522505 0.9564184302135792 0 0 4.084967320261438 6.212456239109543 MEF2C -GO_Biological_Process_2018 negative regulation of peptidyl-serine phosphorylation (GO:0033137) 1/18 0.21853495779522505 0.9555988771457014 0 0 4.084967320261438 6.212456239109543 CAV1 -GO_Biological_Process_2018 negative regulation of oxidative stress-induced intrinsic apoptotic signaling pathway (GO:1902176) 1/18 0.21853495779522505 0.9547807274221176 0 0 4.084967320261438 6.212456239109543 HSPB1 -GO_Biological_Process_2018 blood vessel endothelial cell migration (GO:0043534) 1/18 0.21853495779522505 0.9539639774414316 0 0 4.084967320261438 6.212456239109543 MYH9 -GO_Biological_Process_2018 regulation of cellular response to growth factor stimulus (GO:0090287) 1/18 0.21853495779522505 0.9531486236145584 0 0 4.084967320261438 6.212456239109543 SFRP4 -GO_Biological_Process_2018 regulation of epithelial cell differentiation (GO:0030856) 1/18 0.21853495779522505 0.9523346623646742 0 0 4.084967320261438 6.212456239109543 CAV1 -GO_Biological_Process_2018 DNA methylation (GO:0006306) 1/18 0.21853495779522505 0.9515220901271616 0 0 4.084967320261438 6.212456239109543 ATRX -GO_Biological_Process_2018 regulation of peptidase activity (GO:0052547) 1/18 0.21853495779522505 0.9507109033495597 0 0 4.084967320261438 6.212456239109543 CAV1 -GO_Biological_Process_2018 negative regulation of calcium-mediated signaling (GO:0050849) 1/18 0.21853495779522505 0.9499010984915106 0 0 4.084967320261438 6.212456239109543 PKD2 -GO_Biological_Process_2018 chromatin assembly (GO:0031497) 2/65 0.22129250630251385 0.9610686465206196 0 0 2.2624434389140275 3.4123753272967168 ATRX;SMARCA5 -GO_Biological_Process_2018 regulation of myeloid cell differentiation (GO:0045637) 2/65 0.22129250630251385 0.9602514112769798 0 0 2.2624434389140275 3.4123753272967168 MEF2C;MYL9 -GO_Biological_Process_2018 negative regulation of cell adhesion (GO:0007162) 2/65 0.22129250630251385 0.9594355647083502 0 0 2.2624434389140275 3.4123753272967168 JAG1;CDH13 -GO_Biological_Process_2018 positive regulation of chromosome organization (GO:2001252) 1/19 0.2291724408383681 0.992756337519688 0 0 3.8699690402476783 5.701550085299294 ATRX -GO_Biological_Process_2018 protein import into mitochondrial matrix (GO:0030150) 1/19 0.2291724408383681 0.9919143050027078 0 0 3.8699690402476783 5.701550085299294 TOMM7 -GO_Biological_Process_2018 regulation of protein kinase A signaling (GO:0010738) 1/19 0.2291724408383681 0.9910736996594852 0 0 3.8699690402476783 5.701550085299294 PJA2 -GO_Biological_Process_2018 regulation of gluconeogenesis (GO:0006111) 1/19 0.2291724408383681 0.9902345178646844 0 0 3.8699690402476783 5.701550085299294 OGT -GO_Biological_Process_2018 protein targeting to lysosome (GO:0006622) 1/19 0.2291724408383681 0.989396756005239 0 0 3.8699690402476783 5.701550085299294 CLU -GO_Biological_Process_2018 NADP metabolic process (GO:0006739) 1/19 0.2291724408383681 0.988560410480298 0 0 3.8699690402476783 5.701550085299294 FMO2 -GO_Biological_Process_2018 regulation of long-term synaptic potentiation (GO:1900271) 1/19 0.2291724408383681 0.9877254777011759 0 0 3.8699690402476783 5.701550085299294 APP -GO_Biological_Process_2018 positive regulation of activated T cell proliferation (GO:0042104) 1/19 0.2291724408383681 0.9868919540913016 0 0 3.8699690402476783 5.701550085299294 IGFBP2 -GO_Biological_Process_2018 regulation of myotube differentiation (GO:0010830) 1/19 0.2291724408383681 0.9860598360861657 0 0 3.8699690402476783 5.701550085299294 NOV -GO_Biological_Process_2018 muscle tissue morphogenesis (GO:0060415) 1/19 0.2291724408383681 0.9852291201332708 0 0 3.8699690402476783 5.701550085299294 MYLK -GO_Biological_Process_2018 morphogenesis of an epithelium (GO:0002009) 1/19 0.2291724408383681 0.9843998026920812 0 0 3.8699690402476783 5.701550085299294 VCL -GO_Biological_Process_2018 excitatory postsynaptic potential (GO:0060079) 1/19 0.2291724408383681 0.9835718802339718 0 0 3.8699690402476783 5.701550085299294 MEF2C -GO_Biological_Process_2018 positive regulation of toll-like receptor signaling pathway (GO:0034123) 1/19 0.2291724408383681 0.9827453492421786 0 0 3.8699690402476783 5.701550085299294 CAV1 -GO_Biological_Process_2018 regulation of transcription factor import into nucleus (GO:0042990) 1/19 0.2291724408383681 0.9819202062117484 0 0 3.8699690402476783 5.701550085299294 FLNA -GO_Biological_Process_2018 purine ribonucleotide catabolic process (GO:0009154) 1/19 0.2291724408383681 0.9810964476494904 0 0 3.8699690402476783 5.701550085299294 PDE5A -GO_Biological_Process_2018 macromolecule biosynthetic process (GO:0009059) 1/19 0.2291724408383681 0.9802740700739251 0 0 3.8699690402476783 5.701550085299294 RPS23 -GO_Biological_Process_2018 regulation of platelet activation (GO:0010543) 1/19 0.2291724408383681 0.9794530700152366 0 0 3.8699690402476783 5.701550085299294 PDGFRA -GO_Biological_Process_2018 calcium ion transport into cytosol (GO:0060402) 1/19 0.2291724408383681 0.9786334440152238 0 0 3.8699690402476783 5.701550085299294 CACNA2D1 -GO_Biological_Process_2018 negative regulation of calcium ion transport (GO:0051926) 1/19 0.2291724408383681 0.9778151886272513 0 0 3.8699690402476783 5.701550085299294 PLN -GO_Biological_Process_2018 positive regulation of epithelial cell differentiation (GO:0030858) 1/19 0.2291724408383681 0.976998300416201 0 0 3.8699690402476783 5.701550085299294 SFRP4 -GO_Biological_Process_2018 mitochondrial calcium ion homeostasis (GO:0051560) 1/19 0.2291724408383681 0.9761827759584244 0 0 3.8699690402476783 5.701550085299294 TGM2 -GO_Biological_Process_2018 positive regulation of transcription regulatory region DNA binding (GO:2000679) 1/19 0.2291724408383681 0.9753686118416952 0 0 3.8699690402476783 5.701550085299294 FOXC1 -GO_Biological_Process_2018 epidermal cell differentiation (GO:0009913) 2/67 0.23132414856873426 0.9837059417885424 0 0 2.1949078138718177 3.2132030542691368 CLIC4;JAG1 -GO_Biological_Process_2018 positive regulation of synaptic transmission (GO:0050806) 2/67 0.23132414856873426 0.982886869397378 0 0 2.1949078138718177 3.2132030542691368 APP;NPTN -GO_Biological_Process_2018 positive regulation of endothelial cell proliferation (GO:0001938) 2/67 0.23132414856873426 0.98206915985545 0 0 2.1949078138718177 3.2132030542691368 CDH13;FGFR1 -GO_Biological_Process_2018 negative regulation of protein binding (GO:0032091) 2/67 0.23132414856873426 0.981252809764132 0 0 2.1949078138718177 3.2132030542691368 ROCK1;CAV1 -GO_Biological_Process_2018 insulin receptor signaling pathway (GO:0008286) 2/67 0.23132414856873426 0.9804378157360888 0 0 2.1949078138718177 3.2132030542691368 CAV2;SORBS1 -GO_Biological_Process_2018 regulation of cardiac conduction (GO:1903779) 2/67 0.23132414856873426 0.9796241743952291 0 0 2.1949078138718177 3.2132030542691368 PLN;CAV1 -GO_Biological_Process_2018 cellular response to UV (GO:0034644) 2/67 0.23132414856873426 0.9788118823766592 0 0 2.1949078138718177 3.2132030542691368 MFAP4;AQP1 -GO_Biological_Process_2018 negative regulation of cysteine-type endopeptidase activity involved in apoptotic process (GO:0043154) 2/68 0.2363510906537297 0.999254031156572 0 0 2.1626297577854667 3.119456979202147 GAS6;AQP1 -GO_Biological_Process_2018 monocarboxylic acid biosynthetic process (GO:0072330) 2/68 0.2363510906537297 0.9984268341109128 0 0 2.1626297577854667 3.119456979202147 VCAN;BGN -GO_Biological_Process_2018 regulation of endopeptidase activity (GO:0052548) 2/68 0.2363510906537297 0.9976010054640054 0 0 2.1626297577854667 3.119456979202147 TIMP2;SERPING1 -GO_Biological_Process_2018 COPII-coated vesicle budding (GO:0090114) 2/68 0.2363510906537297 0.9967765418231259 0 0 2.1626297577854667 3.119456979202147 USO1;SEC31A -GO_Biological_Process_2018 organic anion transport (GO:0015711) 3/124 0.23834739776290104 1.0 0 0 1.778937381404175 2.5510424841759303 HBB;HBA1;AQP1 -GO_Biological_Process_2018 positive regulation of interleukin-2 production (GO:0032743) 1/20 0.2396656490013005 1.0 0 0 3.6764705882352944 5.251876676867765 SPTBN1 -GO_Biological_Process_2018 cell projection assembly (GO:0030031) 1/20 0.2396656490013005 1.0 0 0 3.6764705882352944 5.251876676867765 NCKAP1 -GO_Biological_Process_2018 negative regulation of ion transport (GO:0043271) 1/20 0.2396656490013005 1.0 0 0 3.6764705882352944 5.251876676867765 PLN -GO_Biological_Process_2018 regulation of lipoprotein lipase activity (GO:0051004) 1/20 0.2396656490013005 1.0 0 0 3.6764705882352944 5.251876676867765 SORT1 -GO_Biological_Process_2018 embryonic digestive tract development (GO:0048566) 1/20 0.2396656490013005 1.0 0 0 3.6764705882352944 5.251876676867765 PDGFRA -GO_Biological_Process_2018 response to progesterone (GO:0032570) 1/20 0.2396656490013005 1.0 0 0 3.6764705882352944 5.251876676867765 CAV1 -GO_Biological_Process_2018 neuromuscular junction development (GO:0007528) 1/20 0.2396656490013005 1.0 0 0 3.6764705882352944 5.251876676867765 ANK3 -GO_Biological_Process_2018 regulation of calcium ion transmembrane transport (GO:1903169) 1/20 0.2396656490013005 1.0 0 0 3.6764705882352944 5.251876676867765 CACNA2D1 -GO_Biological_Process_2018 axon extension (GO:0048675) 1/20 0.2396656490013005 1.0 0 0 3.6764705882352944 5.251876676867765 ALCAM -GO_Biological_Process_2018 negative regulation of protein depolymerization (GO:1901880) 1/20 0.2396656490013005 1.0 0 0 3.6764705882352944 5.251876676867765 CLASP2 -GO_Biological_Process_2018 glial cell development (GO:0021782) 1/20 0.2396656490013005 1.0 0 0 3.6764705882352944 5.251876676867765 APP -GO_Biological_Process_2018 striated muscle cell development (GO:0055002) 1/20 0.2396656490013005 1.0 0 0 3.6764705882352944 5.251876676867765 LMOD1 -GO_Biological_Process_2018 regulation of glycogen biosynthetic process (GO:0005979) 1/20 0.2396656490013005 0.999194286645128 0 0 3.6764705882352944 5.251876676867765 SORBS1 -GO_Biological_Process_2018 organelle disassembly (GO:1903008) 1/20 0.2396656490013005 0.9983786178397034 0 0 3.6764705882352944 5.251876676867765 KLC1 -GO_Biological_Process_2018 positive regulation of T cell migration (GO:2000406) 1/20 0.2396656490013005 0.9975642796522322 0 0 3.6764705882352944 5.251876676867765 APP -GO_Biological_Process_2018 inactivation of MAPK activity (GO:0000188) 1/20 0.2396656490013005 0.9967512688293696 0 0 3.6764705882352944 5.251876676867765 CAV1 -GO_Biological_Process_2018 vesicle cytoskeletal trafficking (GO:0099518) 1/20 0.2396656490013005 0.9959395821283686 0 0 3.6764705882352944 5.251876676867765 ACTN4 -GO_Biological_Process_2018 eye morphogenesis (GO:0048592) 1/20 0.2396656490013005 0.9951292163170355 0 0 3.6764705882352944 5.251876676867765 EFEMP1 -GO_Biological_Process_2018 regulation of fibroblast growth factor receptor signaling pathway (GO:0040036) 1/20 0.2396656490013005 0.9943201681736884 0 0 3.6764705882352944 5.251876676867765 NPTN -GO_Biological_Process_2018 negative regulation of plasma membrane bounded cell projection assembly (GO:0120033) 1/20 0.2396656490013005 0.9935124344871136 0 0 3.6764705882352944 5.251876676867765 PFN2 -GO_Biological_Process_2018 negative regulation of interferon-gamma production (GO:0032689) 1/20 0.2396656490013005 0.9927060120565232 0 0 3.6764705882352944 5.251876676867765 GAS6 -GO_Biological_Process_2018 entrainment of circadian clock by photoperiod (GO:0043153) 1/20 0.2396656490013005 0.991900897691514 0 0 3.6764705882352944 5.251876676867765 PPP1CB -GO_Biological_Process_2018 negative regulation of cell cycle G2/M phase transition (GO:1902750) 2/69 0.2413839501392483 0.998202834327864 0 0 2.131287297527707 3.0293402794340234 FHL1;SKP1 -GO_Biological_Process_2018 positive regulation of plasma membrane bounded cell projection assembly (GO:0120034) 2/69 0.2413839501392483 0.9973945729235498 0 0 2.131287297527707 3.0293402794340234 EPS8;NCKAP1 -GO_Biological_Process_2018 regulation of Notch signaling pathway (GO:0008593) 2/70 0.2464216409203803 1.0 0 0 2.100840336134454 2.9426706360689643 JAG1;NOV -GO_Biological_Process_2018 positive regulation of proteasomal ubiquitin-dependent protein catabolic process (GO:0032436) 2/70 0.2464216409203803 1.0 0 0 2.100840336134454 2.9426706360689643 CAV1;CLU -GO_Biological_Process_2018 neuropeptide signaling pathway (GO:0007218) 2/70 0.2464216409203803 1.0 0 0 2.100840336134454 2.9426706360689643 SORT1;CPE -GO_Biological_Process_2018 vesicle organization (GO:0016050) 3/127 0.2492264367516885 1.0 0 0 1.7369152385363595 2.4132585882563298 CAV2;SORT1;CAV1 -GO_Biological_Process_2018 dendritic cell differentiation (GO:0097028) 1/21 0.2500165320035174 1.0 0 0 3.5014005602240896 4.8537403196504965 GAS6 -GO_Biological_Process_2018 establishment of protein localization to vacuole (GO:0072666) 1/21 0.2500165320035174 1.0 0 0 3.5014005602240896 4.8537403196504965 VPS13A -GO_Biological_Process_2018 cellular response to hexose stimulus (GO:0071331) 1/21 0.2500165320035174 1.0 0 0 3.5014005602240896 4.8537403196504965 GAS6 -GO_Biological_Process_2018 sterol transport (GO:0015918) 1/21 0.2500165320035174 1.0 0 0 3.5014005602240896 4.8537403196504965 CAV1 -GO_Biological_Process_2018 ventricular cardiac muscle tissue development (GO:0003229) 1/21 0.2500165320035174 1.0 0 0 3.5014005602240896 4.8537403196504965 TPM1 -GO_Biological_Process_2018 negative regulation of ion transmembrane transporter activity (GO:0032413) 1/21 0.2500165320035174 1.0 0 0 3.5014005602240896 4.8537403196504965 PLN -GO_Biological_Process_2018 positive regulation of lamellipodium organization (GO:1902745) 1/21 0.2500165320035174 1.0 0 0 3.5014005602240896 4.8537403196504965 NCKAP1 -GO_Biological_Process_2018 regulation of macrophage activation (GO:0043030) 1/21 0.2500165320035174 1.0 0 0 3.5014005602240896 4.8537403196504965 IL33 -GO_Biological_Process_2018 embryonic skeletal system morphogenesis (GO:0048704) 1/21 0.2500165320035174 1.0 0 0 3.5014005602240896 4.8537403196504965 PDGFRA -GO_Biological_Process_2018 establishment of endothelial barrier (GO:0061028) 1/21 0.2500165320035174 1.0 0 0 3.5014005602240896 4.8537403196504965 TJP1 -GO_Biological_Process_2018 negative regulation of protein tyrosine kinase activity (GO:0061099) 1/21 0.2500165320035174 1.0 0 0 3.5014005602240896 4.8537403196504965 CAV1 -GO_Biological_Process_2018 regulation of nuclear division (GO:0051783) 1/21 0.2500165320035174 1.0 0 0 3.5014005602240896 4.8537403196504965 CAV2 -GO_Biological_Process_2018 positive regulation of BMP signaling pathway (GO:0030513) 1/21 0.2500165320035174 1.0 0 0 3.5014005602240896 4.8537403196504965 CYR61 -GO_Biological_Process_2018 limb morphogenesis (GO:0035108) 1/21 0.2500165320035174 1.0 0 0 3.5014005602240896 4.8537403196504965 MBNL1 -GO_Biological_Process_2018 negative regulation of cyclin-dependent protein kinase activity (GO:1904030) 1/21 0.2500165320035174 1.0 0 0 3.5014005602240896 4.8537403196504965 NR2F2 -GO_Biological_Process_2018 long-term synaptic potentiation (GO:0060291) 1/21 0.2500165320035174 1.0 0 0 3.5014005602240896 4.8537403196504965 NPTN -GO_Biological_Process_2018 cellular response to copper ion (GO:0071280) 1/21 0.2500165320035174 1.0 0 0 3.5014005602240896 4.8537403196504965 AQP1 -GO_Biological_Process_2018 positive regulation of lipid metabolic process (GO:0045834) 1/21 0.2500165320035174 1.0 0 0 3.5014005602240896 4.8537403196504965 SORBS1 -GO_Biological_Process_2018 positive regulation of receptor internalization (GO:0002092) 1/21 0.2500165320035174 1.0 0 0 3.5014005602240896 4.8537403196504965 SFRP4 -GO_Biological_Process_2018 embryonic skeletal system development (GO:0048706) 1/21 0.2500165320035174 1.0 0 0 3.5014005602240896 4.8537403196504965 PDGFRA -GO_Biological_Process_2018 photoperiodism (GO:0009648) 1/21 0.2500165320035174 1.0 0 0 3.5014005602240896 4.8537403196504965 PPP1CB -GO_Biological_Process_2018 synaptic transmission, glutamatergic (GO:0035249) 1/21 0.2500165320035174 1.0 0 0 3.5014005602240896 4.8537403196504965 GRIA2 -GO_Biological_Process_2018 hepaticobiliary system development (GO:0061008) 1/21 0.2500165320035174 1.0 0 0 3.5014005602240896 4.8537403196504965 PKD2 -GO_Biological_Process_2018 glutamate metabolic process (GO:0006536) 1/21 0.2500165320035174 1.0 0 0 3.5014005602240896 4.8537403196504965 GLS -GO_Biological_Process_2018 positive regulation of lymphocyte proliferation (GO:0050671) 2/71 0.2514631049356125 1.0 0 0 2.071251035625518 2.8592771339292797 MEF2C;IL6ST -GO_Biological_Process_2018 regulation of protein processing (GO:0070613) 3/128 0.25286971715740136 1.0 0 0 1.7233455882352942 2.3693948899958683 SERPING1;CLU;CD55 -GO_Biological_Process_2018 lysosomal transport (GO:0007041) 2/72 0.2565073119201078 1.0 0 0 2.042483660130719 2.77899940413904 SORT1;RHOB -GO_Biological_Process_2018 regulation of neuron death (GO:1901214) 2/72 0.2565073119201078 1.0 0 0 2.042483660130719 2.77899940413904 MEF2C;CLU -GO_Biological_Process_2018 negative regulation of cysteine-type endopeptidase activity (GO:2000117) 2/72 0.2565073119201078 1.0 0 0 2.042483660130719 2.77899940413904 GAS6;AQP1 -GO_Biological_Process_2018 response to metal ion (GO:0010038) 2/72 0.2565073119201078 1.0 0 0 2.042483660130719 2.77899940413904 CAV1;SEC31A -GO_Biological_Process_2018 regulation of fat cell differentiation (GO:0045598) 2/72 0.2565073119201078 1.0 0 0 2.042483660130719 2.77899940413904 FRZB;ADIRF -GO_Biological_Process_2018 regulation of macromolecule metabolic process (GO:0060255) 3/129 0.25652072682543176 1.0 0 0 1.7099863201094392 2.326514725203494 APP;MFAP4;SORT1 -GO_Biological_Process_2018 transmembrane transport (GO:0055085) 7/378 0.25652575608578704 1.0 0 0 1.3616557734204793 1.852568363048212 ANO1;SLC14A1;GJA1;TMEM30A;ATP8B1;ADD1;AQP1 -GO_Biological_Process_2018 generation of neurons (GO:0048699) 3/130 0.2601790295419775 1.0 0 0 1.6968325791855206 2.2845904577447924 MEF2C;ID4;FGFR1 -GO_Biological_Process_2018 protein glycosylation (GO:0006486) 3/130 0.2601790295419775 1.0 0 0 1.6968325791855206 2.2845904577447924 MGEA5;EOGT;OGT -GO_Biological_Process_2018 regulation of calcium ion transport into cytosol (GO:0010522) 1/22 0.2602270131722876 1.0 0 0 3.3422459893048133 4.4993345632855855 CAV1 -GO_Biological_Process_2018 negative regulation of protein polymerization (GO:0032272) 1/22 0.2602270131722876 1.0 0 0 3.3422459893048133 4.4993345632855855 PFN2 -GO_Biological_Process_2018 NAD biosynthetic process (GO:0009435) 1/22 0.2602270131722876 1.0 0 0 3.3422459893048133 4.4993345632855855 PTGIS -GO_Biological_Process_2018 protein homotrimerization (GO:0070207) 1/22 0.2602270131722876 1.0 0 0 3.3422459893048133 4.4993345632855855 ITLN1 -GO_Biological_Process_2018 positive regulation of protein targeting to membrane (GO:0090314) 1/22 0.2602270131722876 1.0 0 0 3.3422459893048133 4.4993345632855855 ANK3 -GO_Biological_Process_2018 positive regulation of proteolysis involved in cellular protein catabolic process (GO:1903052) 1/22 0.2602270131722876 1.0 0 0 3.3422459893048133 4.4993345632855855 CLU -GO_Biological_Process_2018 negative regulation of response to endoplasmic reticulum stress (GO:1903573) 1/22 0.2602270131722876 1.0 0 0 3.3422459893048133 4.4993345632855855 CLU -GO_Biological_Process_2018 regulation of DNA biosynthetic process (GO:2000278) 1/22 0.2602270131722876 1.0 0 0 3.3422459893048133 4.4993345632855855 PDGFRB -GO_Biological_Process_2018 cytosolic calcium ion transport (GO:0060401) 1/22 0.2602270131722876 1.0 0 0 3.3422459893048133 4.4993345632855855 CACNA2D1 -GO_Biological_Process_2018 STAT cascade (GO:0097696) 1/22 0.2602270131722876 1.0 0 0 3.3422459893048133 4.4993345632855855 PKD2 -GO_Biological_Process_2018 beta-catenin destruction complex disassembly (GO:1904886) 1/22 0.2602270131722876 1.0 0 0 3.3422459893048133 4.4993345632855855 CAV1 -GO_Biological_Process_2018 positive regulation of biosynthetic process (GO:0009891) 1/22 0.2602270131722876 1.0 0 0 3.3422459893048133 4.4993345632855855 SORBS1 -GO_Biological_Process_2018 regulation of ERBB signaling pathway (GO:1901184) 1/22 0.2602270131722876 1.0 0 0 3.3422459893048133 4.4993345632855855 CDH13 -GO_Biological_Process_2018 regulation of monocyte chemotaxis (GO:0090025) 1/22 0.2602270131722876 1.0 0 0 3.3422459893048133 4.4993345632855855 NOV -GO_Biological_Process_2018 regulation of insulin secretion involved in cellular response to glucose stimulus (GO:0061178) 1/22 0.2602270131722876 1.0 0 0 3.3422459893048133 4.4993345632855855 ANO1 -GO_Biological_Process_2018 glucose transport (GO:0015758) 1/22 0.2602270131722876 1.0 0 0 3.3422459893048133 4.4993345632855855 SORT1 -GO_Biological_Process_2018 positive regulation of inflammatory response (GO:0050729) 2/73 0.2615532587344643 1.0 0 0 2.0145044319097503 2.701686845136462 IL33;IL6ST -GO_Biological_Process_2018 B cell activation (GO:0042113) 2/73 0.2615532587344643 1.0 0 0 2.0145044319097503 2.701686845136462 ITGB1;MEF2C -GO_Biological_Process_2018 regulation of protein complex assembly (GO:0043254) 2/73 0.2615532587344643 1.0 0 0 2.0145044319097503 2.701686845136462 SENP6;GSN -GO_Biological_Process_2018 positive regulation of cytokine secretion (GO:0050715) 2/73 0.2615532587344643 1.0 0 0 2.0145044319097503 2.701686845136462 IL33;SPTBN1 -GO_Biological_Process_2018 chloride transport (GO:0006821) 2/73 0.2615532587344643 1.0 0 0 2.0145044319097503 2.701686845136462 ANO1;CLIC4 -GO_Biological_Process_2018 positive regulation of defense response (GO:0031349) 2/74 0.2665999687415824 1.0 0 0 1.987281399046105 2.627197910345616 IL33;MEF2C -GO_Biological_Process_2018 cellular response to hypoxia (GO:0071456) 3/132 0.2675157906114184 1.0 0 0 1.6711229946524069 2.20350381695394 PTGIS;EPAS1;AQP1 -GO_Biological_Process_2018 ammonium transport (GO:0015696) 1/23 0.2702989899983517 1.0 0 0 3.1969309462915603 4.182309981783217 AQP1 -GO_Biological_Process_2018 neuronal action potential (GO:0019228) 1/23 0.2702989899983517 1.0 0 0 3.1969309462915603 4.182309981783217 ANK3 -GO_Biological_Process_2018 positive regulation of protein oligomerization (GO:0032461) 1/23 0.2702989899983517 1.0 0 0 3.1969309462915603 4.182309981783217 CLU -GO_Biological_Process_2018 protein K48-linked deubiquitination (GO:0071108) 1/23 0.2702989899983517 1.0 0 0 3.1969309462915603 4.182309981783217 USP34 -GO_Biological_Process_2018 vesicle-mediated transport between endosomal compartments (GO:0098927) 1/23 0.2702989899983517 1.0 0 0 3.1969309462915603 4.182309981783217 MYO1D -GO_Biological_Process_2018 metanephros development (GO:0001656) 1/23 0.2702989899983517 1.0 0 0 3.1969309462915603 4.182309981783217 PKD2 -GO_Biological_Process_2018 positive regulation of blood circulation (GO:1903524) 1/23 0.2702989899983517 1.0 0 0 3.1969309462915603 4.182309981783217 CAV1 -GO_Biological_Process_2018 regulation of dendritic spine morphogenesis (GO:0061001) 1/23 0.2702989899983517 1.0 0 0 3.1969309462915603 4.182309981783217 PDLIM5 -GO_Biological_Process_2018 protein localization to Golgi apparatus (GO:0034067) 1/23 0.2702989899983517 1.0 0 0 3.1969309462915603 4.182309981783217 VPS13A -GO_Biological_Process_2018 histone H3-K4 methylation (GO:0051568) 1/23 0.2702989899983517 1.0 0 0 3.1969309462915603 4.182309981783217 OGT -GO_Biological_Process_2018 regulation of monooxygenase activity (GO:0032768) 1/23 0.2702989899983517 1.0 0 0 3.1969309462915603 4.182309981783217 CAV1 -GO_Biological_Process_2018 positive regulation of ion transmembrane transporter activity (GO:0032414) 1/23 0.2702989899983517 1.0 0 0 3.1969309462915603 4.182309981783217 ANK3 -GO_Biological_Process_2018 regulation of chondrocyte differentiation (GO:0032330) 1/23 0.2702989899983517 1.0 0 0 3.1969309462915603 4.182309981783217 EFEMP1 -GO_Biological_Process_2018 regulation of ion homeostasis (GO:2000021) 1/23 0.2702989899983517 1.0 0 0 3.1969309462915603 4.182309981783217 FHL1 -GO_Biological_Process_2018 protein targeting (GO:0006605) 3/133 0.27119339815688503 1.0 0 0 1.6585581601061476 2.164290799908547 VPS13A;TOMM7;LTBP2 -GO_Biological_Process_2018 transforming growth factor beta receptor signaling pathway (GO:0007179) 2/76 0.2766919018489793 1.0 0 0 1.9349845201238391 2.4861661368358834 LTBP2;FMOD -GO_Biological_Process_2018 positive regulation of JNK cascade (GO:0046330) 2/76 0.2766919018489793 1.0 0 0 1.9349845201238391 2.4861661368358834 APP;CTGF -GO_Biological_Process_2018 positive regulation of apoptotic signaling pathway (GO:2001235) 2/76 0.2766919018489793 1.0 0 0 1.9349845201238391 2.4861661368358834 GSN;CAV1 -GO_Biological_Process_2018 positive regulation of histone methylation (GO:0031062) 1/24 0.280234334435909 1.0 0 0 3.063725490196078 3.897454402959639 OGT -GO_Biological_Process_2018 membrane protein intracellular domain proteolysis (GO:0031293) 1/24 0.280234334435909 1.0 0 0 3.063725490196078 3.897454402959639 JAG1 -GO_Biological_Process_2018 negative regulation of cytoskeleton organization (GO:0051494) 1/24 0.280234334435909 1.0 0 0 3.063725490196078 3.897454402959639 PFN2 -GO_Biological_Process_2018 histone monoubiquitination (GO:0010390) 1/24 0.280234334435909 1.0 0 0 3.063725490196078 3.897454402959639 SKP1 -GO_Biological_Process_2018 receptor clustering (GO:0043113) 1/24 0.280234334435909 1.0 0 0 3.063725490196078 3.897454402959639 FLNA -GO_Biological_Process_2018 negative regulation of sequestering of calcium ion (GO:0051283) 1/24 0.280234334435909 1.0 0 0 3.063725490196078 3.897454402959639 PKD2 -GO_Biological_Process_2018 camera-type eye development (GO:0043010) 1/24 0.280234334435909 1.0 0 0 3.063725490196078 3.897454402959639 EFEMP1 -GO_Biological_Process_2018 intrinsic apoptotic signaling pathway in response to endoplasmic reticulum stress (GO:0070059) 1/24 0.280234334435909 1.0 0 0 3.063725490196078 3.897454402959639 DNAJC10 -GO_Biological_Process_2018 negative regulation of cyclin-dependent protein serine/threonine kinase activity (GO:0045736) 1/24 0.280234334435909 1.0 0 0 3.063725490196078 3.897454402959639 NR2F2 -GO_Biological_Process_2018 regulation of epidermal growth factor-activated receptor activity (GO:0007176) 1/24 0.280234334435909 1.0 0 0 3.063725490196078 3.897454402959639 APP -GO_Biological_Process_2018 sensory perception of pain (GO:0019233) 1/24 0.280234334435909 1.0 0 0 3.063725490196078 3.897454402959639 ANO1 -GO_Biological_Process_2018 negative regulation of oxidative stress-induced cell death (GO:1903202) 1/24 0.280234334435909 1.0 0 0 3.063725490196078 3.897454402959639 HSPB1 -GO_Biological_Process_2018 negative regulation of endothelial cell apoptotic process (GO:2000352) 1/24 0.280234334435909 1.0 0 0 3.063725490196078 3.897454402959639 GAS6 -GO_Biological_Process_2018 regulation of AMPA receptor activity (GO:2000311) 1/24 0.280234334435909 1.0 0 0 3.063725490196078 3.897454402959639 MEF2C -GO_Biological_Process_2018 positive regulation of vascular endothelial growth factor production (GO:0010575) 1/24 0.280234334435909 1.0 0 0 3.063725490196078 3.897454402959639 IL6ST -GO_Biological_Process_2018 regulation of neuronal synaptic plasticity (GO:0048168) 1/24 0.280234334435909 1.0 0 0 3.063725490196078 3.897454402959639 APP -GO_Biological_Process_2018 negative regulation of interleukin-6 production (GO:0032715) 1/24 0.280234334435909 1.0 0 0 3.063725490196078 3.897454402959639 GAS6 -GO_Biological_Process_2018 mammary gland development (GO:0030879) 1/24 0.280234334435909 1.0 0 0 3.063725490196078 3.897454402959639 CAV1 -GO_Biological_Process_2018 actin nucleation (GO:0045010) 1/24 0.280234334435909 1.0 0 0 3.063725490196078 3.897454402959639 LMOD1 -GO_Biological_Process_2018 negative regulation of proteasomal ubiquitin-dependent protein catabolic process (GO:0032435) 1/24 0.280234334435909 1.0 0 0 3.063725490196078 3.897454402959639 OGT -GO_Biological_Process_2018 negative regulation of intrinsic apoptotic signaling pathway in response to DNA damage (GO:1902230) 1/24 0.280234334435909 1.0 0 0 3.063725490196078 3.897454402959639 CLU -GO_Biological_Process_2018 cGMP biosynthetic process (GO:0006182) 1/24 0.280234334435909 1.0 0 0 3.063725490196078 3.897454402959639 AQP1 -GO_Biological_Process_2018 response to ionizing radiation (GO:0010212) 2/77 0.2817352998961376 1.0 0 0 1.9098548510313216 2.4193798733862746 NET1;RHOB -GO_Biological_Process_2018 positive regulation of transferase activity (GO:0051347) 3/137 0.2859556232907379 1.0 0 0 1.6101331043366254 2.015755651868489 PDGFRB;PDGFRA;FGFR1 -GO_Biological_Process_2018 negative regulation of inflammatory response (GO:0050728) 2/78 0.28677581014083325 1.0 0 0 1.885369532428356 2.354929332227962 PTGIS;NOV -GO_Biological_Process_2018 rRNA metabolic process (GO:0016072) 4/200 0.2895614748730245 1.0 0 0 1.4705882352941178 1.8226289059897849 CAVIN1;SBDS;RPL35A;RPS23 -GO_Biological_Process_2018 heterotypic cell-cell adhesion (GO:0034113) 1/25 0.2900348932660936 1.0 0 0 2.9411764705882355 3.640453063624888 ITGB1 -GO_Biological_Process_2018 signal peptide processing (GO:0006465) 1/25 0.2900348932660936 1.0 0 0 2.9411764705882355 3.640453063624888 GAS6 -GO_Biological_Process_2018 regulation of spindle assembly (GO:0090169) 1/25 0.2900348932660936 1.0 0 0 2.9411764705882355 3.640453063624888 SENP6 -GO_Biological_Process_2018 regulation of telomere maintenance (GO:0032204) 1/25 0.2900348932660936 1.0 0 0 2.9411764705882355 3.640453063624888 ATRX -GO_Biological_Process_2018 response to copper ion (GO:0046688) 1/25 0.2900348932660936 1.0 0 0 2.9411764705882355 3.640453063624888 AQP1 -GO_Biological_Process_2018 glial cell differentiation (GO:0010001) 1/25 0.2900348932660936 1.0 0 0 2.9411764705882355 3.640453063624888 NFIB -GO_Biological_Process_2018 regulation of protein ubiquitination involved in ubiquitin-dependent protein catabolic process (GO:2000058) 1/25 0.2900348932660936 1.0 0 0 2.9411764705882355 3.640453063624888 CLU -GO_Biological_Process_2018 inorganic cation import across plasma membrane (GO:0098659) 1/25 0.2900348932660936 1.0 0 0 2.9411764705882355 3.640453063624888 CACNA2D1 -GO_Biological_Process_2018 regulation of ryanodine-sensitive calcium-release channel activity (GO:0060314) 1/25 0.2900348932660936 1.0 0 0 2.9411764705882355 3.640453063624888 PKD2 -GO_Biological_Process_2018 prostaglandin metabolic process (GO:0006693) 1/25 0.2900348932660936 1.0 0 0 2.9411764705882355 3.640453063624888 PTGIS -GO_Biological_Process_2018 in utero embryonic development (GO:0001701) 1/25 0.2900348932660936 1.0 0 0 2.9411764705882355 3.640453063624888 MBNL1 -GO_Biological_Process_2018 positive regulation of nuclear division (GO:0051785) 1/25 0.2900348932660936 1.0 0 0 2.9411764705882355 3.640453063624888 PDGFRB -GO_Biological_Process_2018 response to alcohol (GO:0097305) 1/25 0.2900348932660936 1.0 0 0 2.9411764705882355 3.640453063624888 SOD1 -GO_Biological_Process_2018 regulation of lamellipodium assembly (GO:0010591) 1/25 0.2900348932660936 1.0 0 0 2.9411764705882355 3.640453063624888 NCKAP1 -GO_Biological_Process_2018 negative regulation of cellular protein localization (GO:1903828) 1/25 0.2900348932660936 1.0 0 0 2.9411764705882355 3.640453063624888 LEPROT -GO_Biological_Process_2018 modulation of excitatory postsynaptic potential (GO:0098815) 1/25 0.2900348932660936 1.0 0 0 2.9411764705882355 3.640453063624888 APP -GO_Biological_Process_2018 cellular response to epidermal growth factor stimulus (GO:0071364) 1/25 0.2900348932660936 1.0 0 0 2.9411764705882355 3.640453063624888 FOXC1 -GO_Biological_Process_2018 regulation of potassium ion transmembrane transporter activity (GO:1901016) 1/25 0.2900348932660936 1.0 0 0 2.9411764705882355 3.640453063624888 FHL1 -GO_Biological_Process_2018 long-chain fatty-acyl-CoA metabolic process (GO:0035336) 1/25 0.2900348932660936 1.0 0 0 2.9411764705882355 3.640453063624888 HSD17B12 -GO_Biological_Process_2018 peptide hormone secretion (GO:0030072) 1/25 0.2900348932660936 1.0 0 0 2.9411764705882355 3.640453063624888 LTBP4 -GO_Biological_Process_2018 positive regulation of peptidyl-threonine phosphorylation (GO:0010800) 1/25 0.2900348932660936 1.0 0 0 2.9411764705882355 3.640453063624888 APP -GO_Biological_Process_2018 signal transduction in response to DNA damage (GO:0042770) 1/25 0.2900348932660936 1.0 0 0 2.9411764705882355 3.640453063624888 ATRX -GO_Biological_Process_2018 nucleosome organization (GO:0034728) 2/79 0.2918125810924951 1.0 0 0 1.8615040953090096 2.292709472000288 ATRX;SMARCA5 -GO_Biological_Process_2018 cation transport (GO:0006812) 2/79 0.2918125810924951 1.0 0 0 1.8615040953090096 2.292709472000288 ANO1;AQP1 -GO_Biological_Process_2018 cellular response to growth factor stimulus (GO:0071363) 3/139 0.2933621239670923 1.0 0 0 1.586965721540415 1.9461714691703849 MEF2C;FOXC1;HSPB1 -GO_Biological_Process_2018 neuron differentiation (GO:0030182) 3/139 0.2933621239670923 1.0 0 0 1.586965721540415 1.9461714691703849 MEF2C;ID4;SKIL -GO_Biological_Process_2018 ERBB signaling pathway (GO:0038127) 2/80 0.2968447847903564 1.0 0 0 1.8382352941176472 2.232621115663953 EPS8;EFEMP1 -GO_Biological_Process_2018 positive regulation of stress-activated MAPK cascade (GO:0032874) 2/80 0.2968447847903564 1.0 0 0 1.8382352941176472 2.232621115663953 APP;CTGF -GO_Biological_Process_2018 cellular response to hormone stimulus (GO:0032870) 2/80 0.2968447847903564 1.0 0 0 1.8382352941176472 2.232621115663953 MEF2C;CAV1 -GO_Biological_Process_2018 negative regulation of defense response (GO:0031348) 2/80 0.2968447847903564 1.0 0 0 1.8382352941176472 2.232621115663953 PTGIS;NOV -GO_Biological_Process_2018 response to gamma radiation (GO:0010332) 1/26 0.2997024883317964 1.0 0 0 2.828054298642533 3.407706453483947 CRYAB -GO_Biological_Process_2018 regulation of pri-miRNA transcription from RNA polymerase II promoter (GO:1902893) 1/26 0.2997024883317964 1.0 0 0 2.828054298642533 3.407706453483947 NFIB -GO_Biological_Process_2018 positive regulation of wound healing (GO:0090303) 1/26 0.2997024883317964 1.0 0 0 2.828054298642533 3.407706453483947 MYLK -GO_Biological_Process_2018 response to ketone (GO:1901654) 1/26 0.2997024883317964 1.0 0 0 2.828054298642533 3.407706453483947 CAV1 -GO_Biological_Process_2018 regulation of supramolecular fiber organization (GO:1902903) 1/26 0.2997024883317964 1.0 0 0 2.828054298642533 3.407706453483947 MAP1B -GO_Biological_Process_2018 internal peptidyl-lysine acetylation (GO:0018393) 1/26 0.2997024883317964 1.0 0 0 2.828054298642533 3.407706453483947 MORF4L2 -GO_Biological_Process_2018 regulation of viral entry into host cell (GO:0046596) 1/26 0.2997024883317964 1.0 0 0 2.828054298642533 3.407706453483947 GSN -GO_Biological_Process_2018 endocrine system development (GO:0035270) 1/26 0.2997024883317964 1.0 0 0 2.828054298642533 3.407706453483947 DKK3 -GO_Biological_Process_2018 RNA biosynthetic process (GO:0032774) 1/26 0.2997024883317964 1.0 0 0 2.828054298642533 3.407706453483947 SMARCA5 -GO_Biological_Process_2018 regulation of I-kappaB kinase/NF-kappaB signaling (GO:0043122) 4/204 0.3017468531757698 1.0 0 0 1.441753171856978 1.7274608529982514 GJA1;FLNA;HSPB1;S100A4 -GO_Biological_Process_2018 positive regulation of protein targeting to mitochondrion (GO:1903955) 2/81 0.30187161631424897 1.0 0 0 1.8155410312273057 2.174570558342443 LEPROT;TOMM7 -GO_Biological_Process_2018 positive regulation of transmembrane receptor protein serine/threonine kinase signaling pathway (GO:0090100) 2/82 0.3068922933208021 1.0 0 0 1.793400286944046 2.1184692055970427 RBPMS;CYR61 -GO_Biological_Process_2018 regulation of calcium ion transmembrane transporter activity (GO:1901019) 1/27 0.3092389169797937 1.0 0 0 2.7233115468409586 3.196190377820267 PLN -GO_Biological_Process_2018 mRNA polyadenylation (GO:0006378) 1/27 0.3092389169797937 1.0 0 0 2.7233115468409586 3.196190377820267 APP -GO_Biological_Process_2018 negative regulation of peptidyl-tyrosine phosphorylation (GO:0050732) 1/27 0.3092389169797937 1.0 0 0 2.7233115468409586 3.196190377820267 CAV1 -GO_Biological_Process_2018 peptidyl-glutamic acid modification (GO:0018200) 1/27 0.3092389169797937 1.0 0 0 2.7233115468409586 3.196190377820267 TTLL7 -GO_Biological_Process_2018 negative regulation of microtubule polymerization or depolymerization (GO:0031111) 1/27 0.3092389169797937 1.0 0 0 2.7233115468409586 3.196190377820267 CLASP2 -GO_Biological_Process_2018 modulation by host of viral transcription (GO:0043921) 1/27 0.3092389169797937 1.0 0 0 2.7233115468409586 3.196190377820267 NUCKS1 -GO_Biological_Process_2018 release of sequestered calcium ion into cytosol (GO:0051209) 1/27 0.3092389169797937 1.0 0 0 2.7233115468409586 3.196190377820267 PKD2 -GO_Biological_Process_2018 microtubule polymerization (GO:0046785) 1/27 0.3092389169797937 1.0 0 0 2.7233115468409586 3.196190377820267 CLASP2 -GO_Biological_Process_2018 extrinsic apoptotic signaling pathway via death domain receptors (GO:0008625) 1/27 0.3092389169797937 1.0 0 0 2.7233115468409586 3.196190377820267 SORT1 -GO_Biological_Process_2018 regulation of neurotransmitter secretion (GO:0046928) 1/27 0.3092389169797937 1.0 0 0 2.7233115468409586 3.196190377820267 MEF2C -GO_Biological_Process_2018 positive regulation of substrate adhesion-dependent cell spreading (GO:1900026) 1/27 0.3092389169797937 1.0 0 0 2.7233115468409586 3.196190377820267 FLNA -GO_Biological_Process_2018 regulation of protein targeting to membrane (GO:0090313) 1/27 0.3092389169797937 1.0 0 0 2.7233115468409586 3.196190377820267 ANK3 -GO_Biological_Process_2018 protein targeting to vacuole (GO:0006623) 1/27 0.3092389169797937 1.0 0 0 2.7233115468409586 3.196190377820267 VPS13A -GO_Biological_Process_2018 regulation of vascular endothelial growth factor production (GO:0010574) 1/27 0.3092389169797937 1.0 0 0 2.7233115468409586 3.196190377820267 IL6ST -GO_Biological_Process_2018 cargo loading into vesicle (GO:0035459) 1/27 0.3092389169797937 1.0 0 0 2.7233115468409586 3.196190377820267 SEC31A -GO_Biological_Process_2018 protein localization to chromosome (GO:0034502) 1/27 0.3092389169797937 1.0 0 0 2.7233115468409586 3.196190377820267 ATRX -GO_Biological_Process_2018 regulation of protein localization to cell surface (GO:2000008) 1/27 0.3092389169797937 1.0 0 0 2.7233115468409586 3.196190377820267 LEPROT -GO_Biological_Process_2018 cellular response to glucose stimulus (GO:0071333) 1/27 0.3092389169797937 1.0 0 0 2.7233115468409586 3.196190377820267 GAS6 -GO_Biological_Process_2018 establishment of protein localization to membrane (GO:0090150) 2/83 0.31190605563790696 1.0 0 0 1.7717930545712264 2.064233239259639 ANK3;SPTBN1 -GO_Biological_Process_2018 positive regulation of protein ubiquitination (GO:0031398) 2/83 0.31190605563790696 1.0 0 0 1.7717930545712264 2.064233239259639 CAV1;CLU -GO_Biological_Process_2018 negative regulation of neuron death (GO:1901215) 2/83 0.31190605563790696 1.0 0 0 1.7717930545712264 2.064233239259639 MEF2C;SOD1 -GO_Biological_Process_2018 negative regulation of endopeptidase activity (GO:0010951) 2/83 0.31190605563790696 1.0 0 0 1.7717930545712264 2.064233239259639 TIMP2;SERPING1 -GO_Biological_Process_2018 regulation of mitotic nuclear division (GO:0007088) 2/84 0.3169121646868541 1.0 0 0 1.7507002801120448 2.0117833097097595 PDGFRB;CAV2 -GO_Biological_Process_2018 negative regulation of endocytosis (GO:0045806) 1/28 0.3186459522993792 1.0 0 0 2.6260504201680672 3.0033473213137447 CAV1 -GO_Biological_Process_2018 RNA polyadenylation (GO:0043631) 1/28 0.3186459522993792 1.0 0 0 2.6260504201680672 3.0033473213137447 APP -GO_Biological_Process_2018 neuron projection extension (GO:1990138) 1/28 0.3186459522993792 1.0 0 0 2.6260504201680672 3.0033473213137447 ALCAM -GO_Biological_Process_2018 cellular response to ketone (GO:1901655) 1/28 0.3186459522993792 1.0 0 0 2.6260504201680672 3.0033473213137447 AQP1 -GO_Biological_Process_2018 negative regulation of proteasomal protein catabolic process (GO:1901799) 1/28 0.3186459522993792 1.0 0 0 2.6260504201680672 3.0033473213137447 OGT -GO_Biological_Process_2018 cellular amino acid biosynthetic process (GO:0008652) 1/28 0.3186459522993792 1.0 0 0 2.6260504201680672 3.0033473213137447 GLS -GO_Biological_Process_2018 negative regulation of response to DNA damage stimulus (GO:2001021) 1/28 0.3186459522993792 1.0 0 0 2.6260504201680672 3.0033473213137447 CLU -GO_Biological_Process_2018 regulation of receptor internalization (GO:0002090) 1/28 0.3186459522993792 1.0 0 0 2.6260504201680672 3.0033473213137447 SFRP4 -GO_Biological_Process_2018 positive regulation of protein tyrosine kinase activity (GO:0061098) 1/28 0.3186459522993792 1.0 0 0 2.6260504201680672 3.0033473213137447 GAS6 -GO_Biological_Process_2018 positive regulation of extrinsic apoptotic signaling pathway (GO:2001238) 1/28 0.3186459522993792 1.0 0 0 2.6260504201680672 3.0033473213137447 CAV1 -GO_Biological_Process_2018 regulation of lipid biosynthetic process (GO:0046890) 1/28 0.3186459522993792 1.0 0 0 2.6260504201680672 3.0033473213137447 SORBS1 -GO_Biological_Process_2018 antigen processing and presentation of peptide antigen via MHC class I (GO:0002474) 1/28 0.3186459522993792 1.0 0 0 2.6260504201680672 3.0033473213137447 SEC31A -GO_Biological_Process_2018 dopamine receptor signaling pathway (GO:0007212) 1/28 0.3186459522993792 1.0 0 0 2.6260504201680672 3.0033473213137447 FLNA -GO_Biological_Process_2018 positive regulation of lipid biosynthetic process (GO:0046889) 1/28 0.3186459522993792 1.0 0 0 2.6260504201680672 3.0033473213137447 SORBS1 -GO_Biological_Process_2018 regulation of cell morphogenesis (GO:0022604) 1/28 0.3186459522993792 1.0 0 0 2.6260504201680672 3.0033473213137447 SPARC -GO_Biological_Process_2018 collagen fibril organization (GO:0030199) 1/29 0.3279253435879597 1.0 0 0 2.5354969574036508 2.8270012868094043 COL14A1 -GO_Biological_Process_2018 superoxide metabolic process (GO:0006801) 1/29 0.3279253435879597 1.0 0 0 2.5354969574036508 2.8270012868094043 SOD1 -GO_Biological_Process_2018 determination of bilateral symmetry (GO:0009855) 1/29 0.3279253435879597 1.0 0 0 2.5354969574036508 2.8270012868094043 PKD2 -GO_Biological_Process_2018 cardiac muscle cell action potential (GO:0086001) 1/29 0.3279253435879597 1.0 0 0 2.5354969574036508 2.8270012868094043 CACNA2D1 -GO_Biological_Process_2018 regulation of vasoconstriction (GO:0019229) 1/29 0.3279253435879597 1.0 0 0 2.5354969574036508 2.8270012868094043 CAV1 -GO_Biological_Process_2018 vacuolar transport (GO:0007034) 1/29 0.3279253435879597 1.0 0 0 2.5354969574036508 2.8270012868094043 VPS13A -GO_Biological_Process_2018 negative regulation of epithelial cell apoptotic process (GO:1904036) 1/29 0.3279253435879597 1.0 0 0 2.5354969574036508 2.8270012868094043 GAS6 -GO_Biological_Process_2018 B cell proliferation (GO:0042100) 1/29 0.3279253435879597 1.0 0 0 2.5354969574036508 2.8270012868094043 MEF2C -GO_Biological_Process_2018 regulation of activated T cell proliferation (GO:0046006) 1/29 0.3279253435879597 1.0 0 0 2.5354969574036508 2.8270012868094043 IGFBP2 -GO_Biological_Process_2018 semaphorin-plexin signaling pathway (GO:0071526) 1/29 0.3279253435879597 1.0 0 0 2.5354969574036508 2.8270012868094043 FLNA -GO_Biological_Process_2018 amino acid transmembrane transport (GO:0003333) 1/29 0.3279253435879597 1.0 0 0 2.5354969574036508 2.8270012868094043 SLC38A2 -GO_Biological_Process_2018 regulation of translation (GO:0006417) 4/213 0.3293544387635163 1.0 0 0 1.3808340237503454 1.5335829696788656 APP;IGFBP5;FAM129A;HSPB1 -GO_Biological_Process_2018 positive regulation of protein ubiquitination involved in ubiquitin-dependent protein catabolic process (GO:2000060) 2/87 0.3318775022231565 1.0 0 0 1.6903313049357676 1.8644174228514108 CLU;SKP1 -GO_Biological_Process_2018 organic substance transport (GO:0071702) 2/87 0.3318775022231565 1.0 0 0 1.6903313049357676 1.8644174228514108 CAV2;CAV1 -GO_Biological_Process_2018 regulation of MAP kinase activity (GO:0043405) 2/87 0.3318775022231565 1.0 0 0 1.6903313049357676 1.8644174228514108 PDGFRB;FGFR1 -GO_Biological_Process_2018 negative regulation of gene expression (GO:0010629) 10/618 0.3329071254836514 1.0 0 0 1.1897963068722637 1.3086471187341904 EID1;APP;MEF2C;ZEB1;IGFBP5;ATP8B1;ID4;NR2F2;GAS6;DKK3 -GO_Biological_Process_2018 regulation of stem cell differentiation (GO:2000736) 2/88 0.3368460304531131 1.0 0 0 1.6711229946524069 1.8183979544803304 PDGFRA;JAG1 -GO_Biological_Process_2018 regulation of proteasomal ubiquitin-dependent protein catabolic process (GO:0032434) 2/88 0.3368460304531131 1.0 0 0 1.6711229946524069 1.8183979544803304 CLU;OGT -GO_Biological_Process_2018 regulation of protein targeting to mitochondrion (GO:1903214) 2/88 0.3368460304531131 1.0 0 0 1.6711229946524069 1.8183979544803304 LEPROT;TOMM7 -GO_Biological_Process_2018 regulation of actin cytoskeleton reorganization (GO:2000249) 1/30 0.33707881654032523 1.0 0 0 2.4509803921568634 2.665290438726649 PDGFRA -GO_Biological_Process_2018 negative regulation of cation channel activity (GO:2001258) 1/30 0.33707881654032523 1.0 0 0 2.4509803921568634 2.665290438726649 PKD2 -GO_Biological_Process_2018 hippo signaling (GO:0035329) 1/30 0.33707881654032523 1.0 0 0 2.4509803921568634 2.665290438726649 TJP1 -GO_Biological_Process_2018 positive regulation of cyclin-dependent protein serine/threonine kinase activity (GO:0045737) 1/30 0.33707881654032523 1.0 0 0 2.4509803921568634 2.665290438726649 PKD2 -GO_Biological_Process_2018 bile acid and bile salt transport (GO:0015721) 1/30 0.33707881654032523 1.0 0 0 2.4509803921568634 2.665290438726649 ATP8B1 -GO_Biological_Process_2018 activation of protein kinase B activity (GO:0032148) 1/30 0.33707881654032523 1.0 0 0 2.4509803921568634 2.665290438726649 GAS6 -GO_Biological_Process_2018 cyclic purine nucleotide metabolic process (GO:0052652) 1/30 0.33707881654032523 1.0 0 0 2.4509803921568634 2.665290438726649 AQP1 -GO_Biological_Process_2018 fatty-acyl-CoA biosynthetic process (GO:0046949) 1/30 0.33707881654032523 1.0 0 0 2.4509803921568634 2.665290438726649 HSD17B12 -GO_Biological_Process_2018 regulation of cation transmembrane transport (GO:1904062) 1/30 0.33707881654032523 1.0 0 0 2.4509803921568634 2.665290438726649 SLMAP -GO_Biological_Process_2018 ATP-dependent chromatin remodeling (GO:0043044) 1/30 0.33707881654032523 1.0 0 0 2.4509803921568634 2.665290438726649 SMARCA5 -GO_Biological_Process_2018 regulation of blood coagulation (GO:0030193) 1/30 0.33707881654032523 1.0 0 0 2.4509803921568634 2.665290438726649 CAV1 -GO_Biological_Process_2018 positive regulation of B cell proliferation (GO:0030890) 1/30 0.33707881654032523 1.0 0 0 2.4509803921568634 2.665290438726649 MEF2C -GO_Biological_Process_2018 CENP-A containing nucleosome assembly (GO:0034080) 1/30 0.33707881654032523 1.0 0 0 2.4509803921568634 2.665290438726649 SMARCA5 -GO_Biological_Process_2018 CENP-A containing chromatin organization (GO:0061641) 1/30 0.33707881654032523 1.0 0 0 2.4509803921568634 2.665290438726649 SMARCA5 -GO_Biological_Process_2018 regulation of sodium ion transmembrane transport (GO:1902305) 1/30 0.33707881654032523 1.0 0 0 2.4509803921568634 2.665290438726649 SLMAP -GO_Biological_Process_2018 negative regulation of cell morphogenesis involved in differentiation (GO:0010771) 1/30 0.33707881654032523 1.0 0 0 2.4509803921568634 2.665290438726649 ACTN4 -GO_Biological_Process_2018 SRP-dependent cotranslational protein targeting to membrane (GO:0006614) 2/89 0.34180352222373084 1.0 0 0 1.6523463317911435 1.7738255178910634 RPL35A;RPS23 -GO_Biological_Process_2018 regulation of neuron apoptotic process (GO:0043523) 2/89 0.34180352222373084 1.0 0 0 1.6523463317911435 1.7738255178910634 MEF2C;SOD1 -GO_Biological_Process_2018 negative regulation of cell-cell adhesion (GO:0022408) 1/31 0.3461080735716709 1.0 0 0 2.3719165085389 2.5166133807702407 JAG1 -GO_Biological_Process_2018 negative regulation of blood vessel endothelial cell migration (GO:0043537) 1/31 0.3461080735716709 1.0 0 0 2.3719165085389 2.5166133807702407 MEF2C -GO_Biological_Process_2018 negative regulation of tumor necrosis factor superfamily cytokine production (GO:1903556) 1/31 0.3461080735716709 1.0 0 0 2.3719165085389 2.5166133807702407 GAS6 -GO_Biological_Process_2018 positive regulation of release of sequestered calcium ion into cytosol (GO:0051281) 1/31 0.3461080735716709 1.0 0 0 2.3719165085389 2.5166133807702407 PKD2 -GO_Biological_Process_2018 renal water homeostasis (GO:0003091) 1/31 0.3461080735716709 1.0 0 0 2.3719165085389 2.5166133807702407 AQP1 -GO_Biological_Process_2018 regulation of protein export from nucleus (GO:0046825) 1/31 0.3461080735716709 1.0 0 0 2.3719165085389 2.5166133807702407 GAS6 -GO_Biological_Process_2018 positive regulation of viral genome replication (GO:0045070) 1/31 0.3461080735716709 1.0 0 0 2.3719165085389 2.5166133807702407 NUCKS1 -GO_Biological_Process_2018 positive regulation of interleukin-1 beta production (GO:0032731) 1/31 0.3461080735716709 1.0 0 0 2.3719165085389 2.5166133807702407 HSPB1 -GO_Biological_Process_2018 positive regulation of insulin secretion (GO:0032024) 1/31 0.3461080735716709 1.0 0 0 2.3719165085389 2.5166133807702407 ANO1 -GO_Biological_Process_2018 termination of RNA polymerase I transcription (GO:0006363) 1/31 0.3461080735716709 1.0 0 0 2.3719165085389 2.5166133807702407 CAVIN1 -GO_Biological_Process_2018 sarcomere organization (GO:0045214) 1/31 0.3461080735716709 1.0 0 0 2.3719165085389 2.5166133807702407 TPM1 -GO_Biological_Process_2018 liver development (GO:0001889) 1/31 0.3461080735716709 1.0 0 0 2.3719165085389 2.5166133807702407 PKD2 -GO_Biological_Process_2018 positive regulation of bone mineralization (GO:0030501) 1/31 0.3461080735716709 1.0 0 0 2.3719165085389 2.5166133807702407 MEF2C -GO_Biological_Process_2018 protein destabilization (GO:0031648) 1/31 0.3461080735716709 1.0 0 0 2.3719165085389 2.5166133807702407 GSN -GO_Biological_Process_2018 positive regulation of response to external stimulus (GO:0032103) 2/90 0.3467493599469019 1.0 0 0 1.6339869281045754 1.7306422641375756 PDGFRB;IL33 -GO_Biological_Process_2018 protein tetramerization (GO:0051262) 2/90 0.3467493599469019 1.0 0 0 1.6339869281045754 1.7306422641375756 PKD2;GLS -GO_Biological_Process_2018 regulation of Ras protein signal transduction (GO:0046578) 2/90 0.3467493599469019 1.0 0 0 1.6339869281045754 1.7306422641375756 EPS8;OGT -GO_Biological_Process_2018 regulation of JNK cascade (GO:0046328) 2/90 0.3467493599469019 1.0 0 0 1.6339869281045754 1.7306422641375756 APP;CTGF -GO_Biological_Process_2018 positive regulation of cellular protein metabolic process (GO:0032270) 2/91 0.3516829446320383 1.0 0 0 1.6160310277957342 1.6887932041986902 FAM129A;OGT -GO_Biological_Process_2018 protein-DNA complex assembly (GO:0065004) 2/91 0.3516829446320383 1.0 0 0 1.6160310277957342 1.6887932041986902 ATRX;SMARCA5 -GO_Biological_Process_2018 dendrite morphogenesis (GO:0048813) 1/32 0.3550147942307571 1.0 0 0 2.297794117647059 2.3795859753805084 MEF2A -GO_Biological_Process_2018 positive regulation of calcium ion transport into cytosol (GO:0010524) 1/32 0.3550147942307571 1.0 0 0 2.297794117647059 2.3795859753805084 CAV1 -GO_Biological_Process_2018 chromatin remodeling at centromere (GO:0031055) 1/32 0.3550147942307571 1.0 0 0 2.297794117647059 2.3795859753805084 SMARCA5 -GO_Biological_Process_2018 negative regulation of BMP signaling pathway (GO:0030514) 1/32 0.3550147942307571 1.0 0 0 2.297794117647059 2.3795859753805084 SKIL -GO_Biological_Process_2018 positive regulation of tumor necrosis factor superfamily cytokine production (GO:1903557) 1/32 0.3550147942307571 1.0 0 0 2.297794117647059 2.3795859753805084 CLU -GO_Biological_Process_2018 positive regulation of TOR signaling (GO:0032008) 1/32 0.3550147942307571 1.0 0 0 2.297794117647059 2.3795859753805084 GAS6 -GO_Biological_Process_2018 regulation of cell-cell adhesion (GO:0022407) 1/32 0.3550147942307571 1.0 0 0 2.297794117647059 2.3795859753805084 JAG1 -GO_Biological_Process_2018 positive regulation of telomere maintenance (GO:0032206) 1/32 0.3550147942307571 1.0 0 0 2.297794117647059 2.3795859753805084 ATRX -GO_Biological_Process_2018 aminoglycan catabolic process (GO:0006026) 1/32 0.3550147942307571 1.0 0 0 2.297794117647059 2.3795859753805084 HSPG2 -GO_Biological_Process_2018 positive regulation of cellular metabolic process (GO:0031325) 2/92 0.3566036957192216 1.0 0 0 1.59846547314578 1.6482260387468903 PDGFRB;SFRP4 -GO_Biological_Process_2018 regulation of cysteine-type endopeptidase activity involved in apoptotic process (GO:0043281) 2/93 0.3615110504528937 1.0 0 0 1.5812776723592663 1.60889100176486 GAS6;AQP1 -GO_Biological_Process_2018 cotranslational protein targeting to membrane (GO:0006613) 2/93 0.3615110504528937 1.0 0 0 1.5812776723592663 1.60889100176486 RPL35A;RPS23 -GO_Biological_Process_2018 proteasome-mediated ubiquitin-dependent protein catabolic process (GO:0043161) 5/291 0.3627345637512383 1.0 0 0 1.2633919547200323 1.2811854931414095 PCNP;RNF11;TBL1XR1;DNAJC10;SKP1 -GO_Biological_Process_2018 positive regulation of cell-cell adhesion (GO:0022409) 1/33 0.3638006353842425 1.0 0 0 2.2281639928698755 2.2530063867622725 ANK3 -GO_Biological_Process_2018 positive regulation of cytokine-mediated signaling pathway (GO:0001961) 1/33 0.3638006353842425 1.0 0 0 2.2281639928698755 2.2530063867622725 GAS6 -GO_Biological_Process_2018 chemical synaptic transmission, postsynaptic (GO:0099565) 1/33 0.3638006353842425 1.0 0 0 2.2281639928698755 2.2530063867622725 MEF2C -GO_Biological_Process_2018 negative regulation of neurogenesis (GO:0050768) 1/33 0.3638006353842425 1.0 0 0 2.2281639928698755 2.2530063867622725 ID4 -GO_Biological_Process_2018 regulation of T cell differentiation (GO:0045580) 1/33 0.3638006353842425 1.0 0 0 2.2281639928698755 2.2530063867622725 SOD1 -GO_Biological_Process_2018 entry into host cell (GO:0030260) 1/33 0.3638006353842425 1.0 0 0 2.2281639928698755 2.2530063867622725 GAS6 -GO_Biological_Process_2018 negative regulation of mitochondrion organization (GO:0010823) 1/33 0.3638006353842425 1.0 0 0 2.2281639928698755 2.2530063867622725 CLU -GO_Biological_Process_2018 stem cell differentiation (GO:0048863) 1/33 0.3638006353842425 1.0 0 0 2.2281639928698755 2.2530063867622725 MEF2C -GO_Biological_Process_2018 regulation of cell-substrate adhesion (GO:0010810) 1/33 0.3638006353842425 1.0 0 0 2.2281639928698755 2.2530063867622725 NPNT -GO_Biological_Process_2018 response to exogenous dsRNA (GO:0043330) 1/33 0.3638006353842425 1.0 0 0 2.2281639928698755 2.2530063867622725 CAV1 -GO_Biological_Process_2018 regulation of postsynaptic membrane potential (GO:0060078) 1/33 0.3638006353842425 1.0 0 0 2.2281639928698755 2.2530063867622725 MEF2C -GO_Biological_Process_2018 transcription initiation from RNA polymerase I promoter (GO:0006361) 1/33 0.3638006353842425 1.0 0 0 2.2281639928698755 2.2530063867622725 CAVIN1 -GO_Biological_Process_2018 positive regulation of biomineral tissue development (GO:0070169) 1/33 0.3638006353842425 1.0 0 0 2.2281639928698755 2.2530063867622725 MEF2C -GO_Biological_Process_2018 transcription from RNA polymerase I promoter (GO:0006360) 1/33 0.3638006353842425 1.0 0 0 2.2281639928698755 2.2530063867622725 CAVIN1 -GO_Biological_Process_2018 positive regulation of nucleocytoplasmic transport (GO:0046824) 1/33 0.3638006353842425 1.0 0 0 2.2281639928698755 2.2530063867622725 GAS6 -GO_Biological_Process_2018 positive regulation of organelle organization (GO:0010638) 1/33 0.3638006353842425 1.0 0 0 2.2281639928698755 2.2530063867622725 ATL3 -GO_Biological_Process_2018 skin development (GO:0043588) 2/94 0.3664044637645348 1.0 0 0 1.5644555694618274 1.570740712598409 CLIC4;JAG1 -GO_Biological_Process_2018 regulation of heart contraction (GO:0008016) 2/95 0.37128340769847057 1.0 0 0 1.547987616099071 1.5337300400111975 PLN;TPM1 -GO_Biological_Process_2018 embryonic limb morphogenesis (GO:0030326) 1/34 0.3724672315695393 1.0 0 0 2.1626297577854667 2.1358265873115294 MBNL1 -GO_Biological_Process_2018 apoptotic mitochondrial changes (GO:0008637) 1/34 0.3724672315695393 1.0 0 0 2.1626297577854667 2.1358265873115294 CLU -GO_Biological_Process_2018 negative regulation of DNA binding (GO:0043392) 1/34 0.3724672315695393 1.0 0 0 2.1626297577854667 2.1358265873115294 ID4 -GO_Biological_Process_2018 positive regulation of exocytosis (GO:0045921) 1/34 0.3724672315695393 1.0 0 0 2.1626297577854667 2.1358265873115294 CLASP2 -GO_Biological_Process_2018 positive regulation of DNA binding (GO:0043388) 1/34 0.3724672315695393 1.0 0 0 2.1626297577854667 2.1358265873115294 FOXC1 -GO_Biological_Process_2018 positive regulation of cytokinesis (GO:0032467) 1/34 0.3724672315695393 1.0 0 0 2.1626297577854667 2.1358265873115294 SVIL -GO_Biological_Process_2018 ventricular cardiac muscle tissue morphogenesis (GO:0055010) 1/34 0.3724672315695393 1.0 0 0 2.1626297577854667 2.1358265873115294 TPM1 -GO_Biological_Process_2018 regulation of axon extension (GO:0030516) 1/34 0.3724672315695393 1.0 0 0 2.1626297577854667 2.1358265873115294 CTTN -GO_Biological_Process_2018 positive regulation of chemokine production (GO:0032722) 1/34 0.3724672315695393 1.0 0 0 2.1626297577854667 2.1358265873115294 IL33 -GO_Biological_Process_2018 positive regulation of protein metabolic process (GO:0051247) 2/96 0.3761473711485361 1.0 0 0 1.531862745098039 1.497815974017252 APP;CLU -GO_Biological_Process_2018 protein targeting to ER (GO:0045047) 2/97 0.3809958594842488 1.0 0 0 1.516070345664039 1.4629575066857154 RPL35A;RPS23 -GO_Biological_Process_2018 antigen processing and presentation of exogenous peptide antigen via MHC class II (GO:0019886) 2/97 0.3809958594842488 1.0 0 0 1.516070345664039 1.4629575066857154 KLC1;SEC31A -GO_Biological_Process_2018 antigen processing and presentation of exogenous peptide antigen (GO:0002478) 2/97 0.3809958594842488 1.0 0 0 1.516070345664039 1.4629575066857154 KLC1;SEC31A -GO_Biological_Process_2018 positive regulation of peptide secretion (GO:0002793) 1/35 0.3810161954023504 1.0 0 0 2.100840336134454 2.0271289855906343 MYH10 -GO_Biological_Process_2018 regulation of endothelial cell apoptotic process (GO:2000351) 1/35 0.3810161954023504 1.0 0 0 2.100840336134454 2.0271289855906343 GAS6 -GO_Biological_Process_2018 positive regulation of DNA replication (GO:0045740) 1/35 0.3810161954023504 1.0 0 0 2.100840336134454 2.0271289855906343 PDGFRA -GO_Biological_Process_2018 apical junction assembly (GO:0043297) 1/35 0.3810161954023504 1.0 0 0 2.100840336134454 2.0271289855906343 VCL -GO_Biological_Process_2018 regulation of mitochondrial membrane potential (GO:0051881) 1/35 0.3810161954023504 1.0 0 0 2.100840336134454 2.0271289855906343 SOD1 -GO_Biological_Process_2018 membrane protein proteolysis (GO:0033619) 1/35 0.3810161954023504 1.0 0 0 2.100840336134454 2.0271289855906343 MYH9 -GO_Biological_Process_2018 carboxylic acid transmembrane transport (GO:1905039) 1/35 0.3810161954023504 1.0 0 0 2.100840336134454 2.0271289855906343 SLC38A2 -GO_Biological_Process_2018 negative regulation of tumor necrosis factor production (GO:0032720) 1/35 0.3810161954023504 1.0 0 0 2.100840336134454 2.0271289855906343 GAS6 -GO_Biological_Process_2018 regulation of cardiac muscle contraction (GO:0055117) 1/35 0.3810161954023504 1.0 0 0 2.100840336134454 2.0271289855906343 PLN -GO_Biological_Process_2018 protein kinase B signaling (GO:0043491) 1/35 0.3810161954023504 1.0 0 0 2.100840336134454 2.0271289855906343 GAS6 -GO_Biological_Process_2018 antigen processing and presentation of peptide antigen via MHC class II (GO:0002495) 2/98 0.38582839416898707 1.0 0 0 1.5006002400960383 1.429115520800836 KLC1;SEC31A -GO_Biological_Process_2018 histone modification (GO:0016570) 2/98 0.38582839416898707 1.0 0 0 1.5006002400960383 1.429115520800836 TBL1XR1;MORF4L2 -GO_Biological_Process_2018 response to UV (GO:0009411) 2/98 0.38582839416898707 1.0 0 0 1.5006002400960383 1.429115520800836 MFAP4;AQP1 -GO_Biological_Process_2018 histone exchange (GO:0043486) 1/36 0.38944911767313006 1.0 0 0 2.042483660130719 1.9261071429541925 SMARCA5 -GO_Biological_Process_2018 organic hydroxy compound transport (GO:0015850) 1/36 0.38944911767313006 1.0 0 0 2.042483660130719 1.9261071429541925 ATP8B1 -GO_Biological_Process_2018 regulation of nitric-oxide synthase activity (GO:0050999) 1/36 0.38944911767313006 1.0 0 0 2.042483660130719 1.9261071429541925 CAV1 -GO_Biological_Process_2018 lymphocyte proliferation (GO:0046651) 1/36 0.38944911767313006 1.0 0 0 2.042483660130719 1.9261071429541925 MEF2C -GO_Biological_Process_2018 positive regulation of signaling (GO:0023056) 1/36 0.38944911767313006 1.0 0 0 2.042483660130719 1.9261071429541925 ILK -GO_Biological_Process_2018 sodium ion transmembrane transport (GO:0035725) 1/36 0.38944911767313006 1.0 0 0 2.042483660130719 1.9261071429541925 PKD2 -GO_Biological_Process_2018 centromere complex assembly (GO:0034508) 1/36 0.38944911767313006 1.0 0 0 2.042483660130719 1.9261071429541925 SMARCA5 -GO_Biological_Process_2018 epithelial cell migration (GO:0010631) 1/36 0.38944911767313006 1.0 0 0 2.042483660130719 1.9261071429541925 CDH13 -GO_Biological_Process_2018 acylglycerol metabolic process (GO:0006639) 1/36 0.38944911767313006 1.0 0 0 2.042483660130719 1.9261071429541925 CAV1 -GO_Biological_Process_2018 cardiac muscle contraction (GO:0060048) 1/36 0.38944911767313006 1.0 0 0 2.042483660130719 1.9261071429541925 TPM1 -GO_Biological_Process_2018 cardiac muscle tissue morphogenesis (GO:0055008) 1/36 0.38944911767313006 1.0 0 0 2.042483660130719 1.9261071429541925 TPM1 -GO_Biological_Process_2018 negative regulation of blood coagulation (GO:0030195) 1/36 0.38944911767313006 1.0 0 0 2.042483660130719 1.9261071429541925 PDGFRA -GO_Biological_Process_2018 regulation of mitotic cell cycle (GO:0007346) 3/165 0.3897580286189413 1.0 0 0 1.3368983957219251 1.2596646682476662 APP;FOXC1;CAV2 -GO_Biological_Process_2018 regulation of inflammatory response (GO:0050727) 3/166 0.3934267237882432 1.0 0 0 1.3288447909284196 1.239626743019271 IL33;PTGIS;NOV -GO_Biological_Process_2018 cellular response to transforming growth factor beta stimulus (GO:0071560) 2/100 0.3954437670071447 1.0 0 0 1.4705882352941178 1.364333358695922 MEF2C;LTBP2 -GO_Biological_Process_2018 negative regulation of cellular macromolecule biosynthetic process (GO:2000113) 8/512 0.3956547416817075 1.0 0 0 1.1488970588235294 1.0652726475903609 EID1;ZEB1;IGFBP5;ATP8B1;ID4;NR2F2;GAS6;DKK3 -GO_Biological_Process_2018 mitochondrion organization (GO:0007005) 3/167 0.3970897953470558 1.0 0 0 1.3208876364917224 1.2199623623426896 MEF2A;CAV2;TOMM7 -GO_Biological_Process_2018 epithelial to mesenchymal transition (GO:0001837) 1/37 0.3977675676481275 1.0 0 0 1.987281399046105 1.8320497718138693 S100A4 -GO_Biological_Process_2018 regulation of dendrite development (GO:0050773) 1/37 0.3977675676481275 1.0 0 0 1.987281399046105 1.8320497718138693 MEF2C -GO_Biological_Process_2018 regulation of peptidyl-threonine phosphorylation (GO:0010799) 1/37 0.3977675676481275 1.0 0 0 1.987281399046105 1.8320497718138693 APP -GO_Biological_Process_2018 regulation of carbohydrate catabolic process (GO:0043470) 1/37 0.3977675676481275 1.0 0 0 1.987281399046105 1.8320497718138693 OGT -GO_Biological_Process_2018 response to steroid hormone (GO:0048545) 1/37 0.3977675676481275 1.0 0 0 1.987281399046105 1.8320497718138693 CAV1 -GO_Biological_Process_2018 negative regulation of nucleic acid-templated transcription (GO:1903507) 7/444 0.3999421295830078 1.0 0 0 1.1592474827768946 1.0623754518883424 EID1;ZEB1;ATP8B1;ID4;NR2F2;GAS6;DKK3 -GO_Biological_Process_2018 regulation of protein ubiquitination (GO:0031396) 2/101 0.4002257256164923 1.0 0 0 1.45602795573675 1.3333234959205245 CAV1;OGT -GO_Biological_Process_2018 proteasomal protein catabolic process (GO:0010498) 4/237 0.4032556459916933 1.0 0 0 1.2410027302060065 1.1270595195526294 PCNP;RNF11;TBL1XR1;SKP1 -GO_Biological_Process_2018 organelle assembly (GO:0070925) 6/376 0.4042599779338396 1.0 0 0 1.1733416770963705 1.062692152198882 GSN;SEPT7;LMOD1;FLNA;SYNPO;RPS23 -GO_Biological_Process_2018 positive regulation of neuron death (GO:1901216) 1/38 0.4059730937858025 1.0 0 0 1.9349845201238391 1.7443273859133042 CLU -GO_Biological_Process_2018 regulation of ossification (GO:0030278) 1/38 0.4059730937858025 1.0 0 0 1.9349845201238391 1.7443273859133042 MEF2C -GO_Biological_Process_2018 signal transduction by p53 class mediator (GO:0072331) 1/39 0.4140672232539661 1.0 0 0 1.885369532428356 1.6623811148729974 ATRX -GO_Biological_Process_2018 regulation of alternative mRNA splicing, via spliceosome (GO:0000381) 1/39 0.4140672232539661 1.0 0 0 1.885369532428356 1.6623811148729974 MBNL1 -GO_Biological_Process_2018 positive regulation of phagocytosis (GO:0050766) 1/39 0.4140672232539661 1.0 0 0 1.885369532428356 1.6623811148729974 GAS6 -GO_Biological_Process_2018 regulation of RNA metabolic process (GO:0051252) 1/39 0.4140672232539661 1.0 0 0 1.885369532428356 1.6623811148729974 MBNL1 -GO_Biological_Process_2018 regulation of coenzyme metabolic process (GO:0051196) 1/39 0.4140672232539661 1.0 0 0 1.885369532428356 1.6623811148729974 OGT -GO_Biological_Process_2018 positive regulation of protein kinase activity (GO:0045860) 3/172 0.41531182208515066 1.0 0 0 1.2824897400820794 1.1269566463447165 GAS6;CLU;CYR61 -GO_Biological_Process_2018 negative regulation of transport (GO:0051051) 1/40 0.4220514629710593 1.0 0 0 1.8382352941176472 1.5857132761269408 CRYAB -GO_Biological_Process_2018 negative regulation of epidermal growth factor receptor signaling pathway (GO:0042059) 1/40 0.4220514629710593 1.0 0 0 1.8382352941176472 1.5857132761269408 ITGA1 -GO_Biological_Process_2018 positive regulation of epithelial to mesenchymal transition (GO:0010718) 1/40 0.4220514629710593 1.0 0 0 1.8382352941176472 1.5857132761269408 FOXC1 -GO_Biological_Process_2018 regulation of protein dephosphorylation (GO:0035304) 1/40 0.4220514629710593 1.0 0 0 1.8382352941176472 1.5857132761269408 PPP1R14A -GO_Biological_Process_2018 sensory organ development (GO:0007423) 1/40 0.4220514629710593 1.0 0 0 1.8382352941176472 1.5857132761269408 FOXC1 -GO_Biological_Process_2018 ameboidal-type cell migration (GO:0001667) 1/40 0.4220514629710593 1.0 0 0 1.8382352941176472 1.5857132761269408 NOV -GO_Biological_Process_2018 positive regulation of mitotic nuclear division (GO:0045840) 1/40 0.4220514629710593 1.0 0 0 1.8382352941176472 1.5857132761269408 PDGFRB -GO_Biological_Process_2018 negative regulation of cytokine secretion (GO:0050710) 1/40 0.4220514629710593 1.0 0 0 1.8382352941176472 1.5857132761269408 GAS6 -GO_Biological_Process_2018 regulation of release of cytochrome c from mitochondria (GO:0090199) 1/40 0.4220514629710593 1.0 0 0 1.8382352941176472 1.5857132761269408 CLU -GO_Biological_Process_2018 negative regulation of catabolic process (GO:0009895) 1/40 0.4220514629710593 1.0 0 0 1.8382352941176472 1.5857132761269408 FLNA -GO_Biological_Process_2018 regulation of cholesterol biosynthetic process (GO:0045540) 1/40 0.4220514629710593 1.0 0 0 1.8382352941176472 1.5857132761269408 SOD1 -GO_Biological_Process_2018 regulation of intracellular transport (GO:0032386) 1/40 0.4220514629710593 1.0 0 0 1.8382352941176472 1.5857132761269408 CRYAB -GO_Biological_Process_2018 positive regulation of cytokine biosynthetic process (GO:0042108) 1/40 0.4220514629710593 1.0 0 0 1.8382352941176472 1.5857132761269408 HSPB1 -GO_Biological_Process_2018 JAK-STAT cascade (GO:0007259) 1/40 0.4220514629710593 1.0 0 0 1.8382352941176472 1.5857132761269408 PKD2 -GO_Biological_Process_2018 response to endoplasmic reticulum stress (GO:0034976) 2/106 0.42386196903111656 1.0 0 0 1.3873473917869037 1.1908260564501991 DNAJC10;FAM129A -GO_Biological_Process_2018 protein autophosphorylation (GO:0046777) 3/175 0.4261615371680269 1.0 0 0 1.2605042016806725 1.0751304319651245 PDGFRB;PDGFRA;FGFR1 -GO_Biological_Process_2018 phosphorylation (GO:0016310) 6/386 0.428443250329284 1.0 0 0 1.142944224321853 0.9687560816286774 APP;IGFBP3;STK38L;ILK;GAS6;FGFR1 -GO_Biological_Process_2018 viral life cycle (GO:0019058) 2/107 0.428531881130898 1.0 0 0 1.3743815283122591 1.1646373584473049 NFIA;GAS6 -GO_Biological_Process_2018 negative regulation of response to external stimulus (GO:0032102) 2/107 0.428531881130898 1.0 0 0 1.3743815283122591 1.1646373584473049 PTGIS;NOV -GO_Biological_Process_2018 regulation of phagocytosis (GO:0050764) 1/41 0.4299272993468652 1.0 0 0 1.793400286944046 1.5138794043670196 GAS6 -GO_Biological_Process_2018 peptide transport (GO:0015833) 1/41 0.4299272993468652 1.0 0 0 1.793400286944046 1.5138794043670196 MYH9 -GO_Biological_Process_2018 positive regulation of cell division (GO:0051781) 1/41 0.4299272993468652 1.0 0 0 1.793400286944046 1.5138794043670196 SVIL -GO_Biological_Process_2018 positive regulation of viral transcription (GO:0050434) 1/41 0.4299272993468652 1.0 0 0 1.793400286944046 1.5138794043670196 NUCKS1 -GO_Biological_Process_2018 regulation of cytokine production (GO:0001817) 2/108 0.43318186814686666 1.0 0 0 1.3616557734204793 1.1391579797765845 IL33;SOD1 -GO_Biological_Process_2018 positive regulation of MAP kinase activity (GO:0043406) 3/177 0.4333559979168851 1.0 0 0 1.2462612163509472 1.0421182984586832 PDGFRB;FGFR1;SOD1 -GO_Biological_Process_2018 regulation of interleukin-6 production (GO:0032675) 1/42 0.4376961988764984 1.0 0 0 1.7507002801120448 1.4464814760772309 GAS6 -GO_Biological_Process_2018 determination of left/right symmetry (GO:0007368) 1/42 0.4376961988764984 1.0 0 0 1.7507002801120448 1.4464814760772309 PKD2 -GO_Biological_Process_2018 negative regulation of protein secretion (GO:0050709) 1/42 0.4376961988764984 1.0 0 0 1.7507002801120448 1.4464814760772309 NOV -GO_Biological_Process_2018 regulation of ion transmembrane transporter activity (GO:0032412) 1/42 0.4376961988764984 1.0 0 0 1.7507002801120448 1.4464814760772309 FHL1 -GO_Biological_Process_2018 cellular response to retinoic acid (GO:0071300) 1/42 0.4376961988764984 1.0 0 0 1.7507002801120448 1.4464814760772309 AQP1 -GO_Biological_Process_2018 viral gene expression (GO:0019080) 2/110 0.4424207888154111 1.0 0 0 1.3368983957219251 1.090232404926217 RPL35A;RPS23 -GO_Biological_Process_2018 positive regulation of establishment of protein localization to mitochondrion (GO:1903749) 2/110 0.4424207888154111 1.0 0 0 1.3368983957219251 1.090232404926217 LEPROT;TOMM7 -GO_Biological_Process_2018 cellular response to insulin stimulus (GO:0032869) 2/110 0.4424207888154111 1.0 0 0 1.3368983957219251 1.090232404926217 CAV2;SORBS1 -GO_Biological_Process_2018 cellular response to peptide hormone stimulus (GO:0071375) 2/110 0.4424207888154111 1.0 0 0 1.3368983957219251 1.090232404926217 CAV1;SORBS1 -GO_Biological_Process_2018 regulation of B cell proliferation (GO:0030888) 1/43 0.4453596082378105 1.0 0 0 1.7099863201094392 1.3831621319427378 MEF2C -GO_Biological_Process_2018 negative regulation of ERBB signaling pathway (GO:1901185) 1/43 0.4453596082378105 1.0 0 0 1.7099863201094392 1.3831621319427378 ITGA1 -GO_Biological_Process_2018 membrane depolarization (GO:0051899) 1/43 0.4453596082378105 1.0 0 0 1.7099863201094392 1.3831621319427378 CAV1 -GO_Biological_Process_2018 protein transmembrane import into intracellular organelle (GO:0044743) 1/43 0.4453596082378105 1.0 0 0 1.7099863201094392 1.3831621319427378 TOMM7 -GO_Biological_Process_2018 response to unfolded protein (GO:0006986) 1/43 0.4453596082378105 1.0 0 0 1.7099863201094392 1.3831621319427378 HSPB1 -GO_Biological_Process_2018 regulation of ATP metabolic process (GO:1903578) 1/43 0.4453596082378105 1.0 0 0 1.7099863201094392 1.3831621319427378 OGT -GO_Biological_Process_2018 regulation of interferon-gamma production (GO:0032649) 1/43 0.4453596082378105 1.0 0 0 1.7099863201094392 1.3831621319427378 GAS6 -GO_Biological_Process_2018 nuclear-transcribed mRNA catabolic process, nonsense-mediated decay (GO:0000184) 2/112 0.4515762971991973 1.0 0 0 1.3130252100840336 1.043869399103068 RPL35A;RPS23 -GO_Biological_Process_2018 sprouting angiogenesis (GO:0002040) 1/44 0.4529189545669325 1.0 0 0 1.6711229946524069 1.3235997288533732 CDH13 -GO_Biological_Process_2018 lymphocyte chemotaxis (GO:0048247) 1/44 0.4529189545669325 1.0 0 0 1.6711229946524069 1.3235997288533732 GAS6 -GO_Biological_Process_2018 regulation of dendrite morphogenesis (GO:0048814) 1/44 0.4529189545669325 1.0 0 0 1.6711229946524069 1.3235997288533732 PDLIM5 -GO_Biological_Process_2018 regulation of T cell activation (GO:0050863) 1/44 0.4529189545669325 1.0 0 0 1.6711229946524069 1.3235997288533732 GSN -GO_Biological_Process_2018 carboxylic acid biosynthetic process (GO:0046394) 1/44 0.4529189545669325 1.0 0 0 1.6711229946524069 1.3235997288533732 GLS -GO_Biological_Process_2018 metaphase plate congression (GO:0051310) 1/44 0.4529189545669325 1.0 0 0 1.6711229946524069 1.3235997288533732 GEM -GO_Biological_Process_2018 positive regulation of viral process (GO:0048524) 1/44 0.4529189545669325 1.0 0 0 1.6711229946524069 1.3235997288533732 CAV2 -GO_Biological_Process_2018 viral transcription (GO:0019083) 2/113 0.4561220632116787 1.0 0 0 1.3014055179593962 1.0215965939700191 RPL35A;RPS23 -GO_Biological_Process_2018 regulation of organelle organization (GO:0033043) 2/113 0.4561220632116787 1.0 0 0 1.3014055179593962 1.0215965939700191 CLIC4;NEXN -GO_Biological_Process_2018 hexose metabolic process (GO:0019318) 1/45 0.460375645861729 1.0 0 0 1.6339869281045754 1.2675040871149124 MAN2A1 -GO_Biological_Process_2018 protein polymerization (GO:0051258) 1/45 0.460375645861729 1.0 0 0 1.6339869281045754 1.2675040871149124 GSN -GO_Biological_Process_2018 nicotinamide nucleotide metabolic process (GO:0046496) 1/45 0.460375645861729 1.0 0 0 1.6339869281045754 1.2675040871149124 FMO2 -GO_Biological_Process_2018 nucleus organization (GO:0006997) 1/45 0.460375645861729 1.0 0 0 1.6339869281045754 1.2675040871149124 SYNE1 -GO_Biological_Process_2018 regulation of synaptic transmission, glutamatergic (GO:0051966) 1/45 0.460375645861729 1.0 0 0 1.6339869281045754 1.2675040871149124 MEF2C -GO_Biological_Process_2018 protein complex subunit organization (GO:0071822) 1/45 0.460375645861729 1.0 0 0 1.6339869281045754 1.2675040871149124 COL14A1 -GO_Biological_Process_2018 negative regulation of ossification (GO:0030279) 1/45 0.460375645861729 1.0 0 0 1.6339869281045754 1.2675040871149124 MEF2C -GO_Biological_Process_2018 peptidyl-lysine modification (GO:0018205) 2/115 0.4651483016825341 1.0 0 0 1.278772378516624 0.9787710944911516 SENP6;UBA2 -GO_Biological_Process_2018 DNA-templated transcription, initiation (GO:0006352) 3/186 0.4652988538826531 1.0 0 0 1.1859582542694498 0.9073474659794925 CAVIN1;SMARCA5;CTGF -GO_Biological_Process_2018 protein complex disassembly (GO:0043241) 1/46 0.4677310711370527 1.0 0 0 1.59846547314578 1.2146128237733471 CAV1 -GO_Biological_Process_2018 interleukin-12-mediated signaling pathway (GO:0035722) 1/46 0.4677310711370527 1.0 0 0 1.59846547314578 1.2146128237733471 SOD1 -GO_Biological_Process_2018 cellular response to interleukin-12 (GO:0071349) 1/46 0.4677310711370527 1.0 0 0 1.59846547314578 1.2146128237733471 SOD1 -GO_Biological_Process_2018 regulation of generation of precursor metabolites and energy (GO:0043467) 1/46 0.4677310711370527 1.0 0 0 1.59846547314578 1.2146128237733471 OGT -GO_Biological_Process_2018 mitotic spindle assembly (GO:0090307) 1/46 0.4677310711370527 1.0 0 0 1.59846547314578 1.2146128237733471 FLNA -GO_Biological_Process_2018 icosanoid metabolic process (GO:0006690) 1/46 0.4677310711370527 1.0 0 0 1.59846547314578 1.2146128237733471 PTGIS -GO_Biological_Process_2018 aminoglycan biosynthetic process (GO:0006023) 1/46 0.4677310711370527 1.0 0 0 1.59846547314578 1.2146128237733471 HSPG2 -GO_Biological_Process_2018 regulation of DNA metabolic process (GO:0051052) 1/46 0.4677310711370527 1.0 0 0 1.59846547314578 1.2146128237733471 NUCKS1 -GO_Biological_Process_2018 positive regulation of peptidyl-tyrosine phosphorylation (GO:0050731) 2/116 0.4696282803180857 1.0 0 0 1.2677484787018256 0.9581817826307044 IL6ST;GAS6 -GO_Biological_Process_2018 response to glucose (GO:0009749) 1/47 0.4749866005080773 1.0 0 0 1.5644555694618274 1.1646881802285192 GAS6 -GO_Biological_Process_2018 negative regulation of I-kappaB kinase/NF-kappaB signaling (GO:0043124) 1/47 0.4749866005080773 1.0 0 0 1.5644555694618274 1.1646881802285192 NOV -GO_Biological_Process_2018 regulation of intrinsic apoptotic signaling pathway (GO:2001242) 1/47 0.4749866005080773 1.0 0 0 1.5644555694618274 1.1646881802285192 CAV1 -GO_Biological_Process_2018 positive regulation of gene expression, epigenetic (GO:0045815) 1/47 0.4749866005080773 1.0 0 0 1.5644555694618274 1.1646881802285192 SMARCA5 -GO_Biological_Process_2018 heterophilic cell-cell adhesion via plasma membrane cell adhesion molecules (GO:0007157) 1/47 0.4749866005080773 1.0 0 0 1.5644555694618274 1.1646881802285192 ALCAM -GO_Biological_Process_2018 cellular response to heat (GO:0034605) 1/47 0.4749866005080773 1.0 0 0 1.5644555694618274 1.1646881802285192 ANO1 -GO_Biological_Process_2018 positive regulation of pathway-restricted SMAD protein phosphorylation (GO:0010862) 1/47 0.4749866005080773 1.0 0 0 1.5644555694618274 1.1646881802285192 RBPMS -GO_Biological_Process_2018 protein modification by small protein removal (GO:0070646) 4/261 0.4755949581493664 1.0 0 0 1.1268875366238449 0.8374901005784442 SENP6;IL33;USP34;OGT -GO_Biological_Process_2018 positive regulation of protein serine/threonine kinase activity (GO:0071902) 2/118 0.4785208013085293 1.0 0 0 1.2462612163509472 0.9185638049910918 PDGFRB;FGFR1 -GO_Biological_Process_2018 negative regulation of mitotic cell cycle (GO:0045930) 1/48 0.4821435858580175 1.0 0 0 1.531862745098039 1.1175142667321398 FOXC1 -GO_Biological_Process_2018 regulation of cell cycle arrest (GO:0071156) 1/48 0.4821435858580175 1.0 0 0 1.531862745098039 1.1175142667321398 PKD2 -GO_Biological_Process_2018 interstrand cross-link repair (GO:0036297) 1/48 0.4821435858580175 1.0 0 0 1.531862745098039 1.1175142667321398 NUCKS1 -GO_Biological_Process_2018 cellular macromolecule catabolic process (GO:0044265) 1/48 0.4821435858580175 1.0 0 0 1.531862745098039 1.1175142667321398 MGEA5 -GO_Biological_Process_2018 amino acid transport (GO:0006865) 1/48 0.4821435858580175 1.0 0 0 1.531862745098039 1.1175142667321398 SLC38A2 -GO_Biological_Process_2018 cellular response to mechanical stimulus (GO:0071260) 1/48 0.4821435858580175 1.0 0 0 1.531862745098039 1.1175142667321398 AQP1 -GO_Biological_Process_2018 vesicle-mediated transport (GO:0016192) 6/410 0.4857274998476561 1.0 0 0 1.0760401721664277 0.7770166917875452 APP;SORT1;CAV2;USO1;KALRN;RHOB -GO_Biological_Process_2018 cilium organization (GO:0044782) 3/192 0.48615201252777 1.0 0 0 1.1488970588235294 0.8286235305711063 SEPT7;GSN;FLNA -GO_Biological_Process_2018 positive regulation of GTPase activity (GO:0043547) 3/192 0.48615201252777 1.0 0 0 1.1488970588235294 0.8286235305711063 NET1;ITGB1;CAV2 -GO_Biological_Process_2018 enzyme linked receptor protein signaling pathway (GO:0007167) 2/120 0.4873219866293012 1.0 0 0 1.2254901960784317 0.8809193760563604 IL6ST;PTPRG -GO_Biological_Process_2018 glutathione metabolic process (GO:0006749) 1/49 0.4892033606322606 1.0 0 0 1.5006002400960383 1.0728946661821388 SOD1 -GO_Biological_Process_2018 regulation of reactive oxygen species metabolic process (GO:2000377) 1/49 0.4892033606322606 1.0 0 0 1.5006002400960383 1.0728946661821388 PDGFRB -GO_Biological_Process_2018 protein autoubiquitination (GO:0051865) 1/49 0.4892033606322606 1.0 0 0 1.5006002400960383 1.0728946661821388 RNF11 -GO_Biological_Process_2018 establishment of protein localization to mitochondrion (GO:0072655) 1/49 0.4892033606322606 1.0 0 0 1.5006002400960383 1.0728946661821388 TOMM7 -GO_Biological_Process_2018 regulation of cellular macromolecule biosynthetic process (GO:2000112) 9/631 0.4898832652098852 1.0 0 0 1.048755476834157 0.7483794810673605 APP;FOXC1;MEF2C;EFEMP1;NFIA;ATRX;APBB2;NUCKS1;ZNF532 -GO_Biological_Process_2018 cellular response to tumor necrosis factor (GO:0071356) 3/194 0.4930165858787749 1.0 0 0 1.1370527592480293 0.8041378821435905 ILK;TNFRSF11B;NPNT -GO_Biological_Process_2018 regulation of bone mineralization (GO:0030500) 1/50 0.4961672405115914 1.0 0 0 1.4705882352941178 1.0306503391739699 MEF2C -GO_Biological_Process_2018 regulation of cellular protein metabolic process (GO:0032268) 1/50 0.4961672405115914 1.0 0 0 1.4705882352941178 1.0306503391739699 APP -GO_Biological_Process_2018 myeloid leukocyte differentiation (GO:0002573) 1/50 0.4961672405115914 1.0 0 0 1.4705882352941178 1.0306503391739699 MYH9 -GO_Biological_Process_2018 tumor necrosis factor-mediated signaling pathway (GO:0033209) 2/123 0.5003491227816947 1.0 0 0 1.1956001912960308 0.8278923704645779 ILK;TNFRSF11B -GO_Biological_Process_2018 cellular calcium ion homeostasis (GO:0006874) 2/123 0.5003491227816947 1.0 0 0 1.1956001912960308 0.8278923704645779 PLN;CAV1 -GO_Biological_Process_2018 regulation of exocytosis (GO:0017157) 1/51 0.5030365232259699 1.0 0 0 1.441753171856978 0.9906177922883744 CLASP2 -GO_Biological_Process_2018 carboxylic acid transport (GO:0046942) 1/51 0.5030365232259699 1.0 0 0 1.441753171856978 0.9906177922883744 SLC38A2 -GO_Biological_Process_2018 arachidonic acid metabolic process (GO:0019369) 1/51 0.5030365232259699 1.0 0 0 1.441753171856978 0.9906177922883744 PTGIS -GO_Biological_Process_2018 dephosphorylation (GO:0016311) 2/124 0.5046442309253042 1.0 0 0 1.1859582542694498 0.8110787371868524 PPP1CB;PPP1R12A -GO_Biological_Process_2018 negative regulation of macromolecule metabolic process (GO:0010605) 2/124 0.5046442309253042 1.0 0 0 1.1859582542694498 0.8110787371868524 APP;MEF2C -GO_Biological_Process_2018 protein dephosphorylation (GO:0006470) 2/125 0.5089154386376918 1.0 0 0 1.1764705882352942 0.7946745983242032 PPP1CB;PPP1R12A -GO_Biological_Process_2018 positive regulation of proteolysis (GO:0045862) 1/52 0.5098124890611783 1.0 0 0 1.4140271493212666 0.9526474680117316 OGT -GO_Biological_Process_2018 chemokine-mediated signaling pathway (GO:0070098) 1/52 0.5098124890611783 1.0 0 0 1.4140271493212666 0.9526474680117316 FOXC1 -GO_Biological_Process_2018 B cell differentiation (GO:0030183) 1/52 0.5098124890611783 1.0 0 0 1.4140271493212666 0.9526474680117316 ITGB1 -GO_Biological_Process_2018 triglyceride metabolic process (GO:0006641) 1/52 0.5098124890611783 1.0 0 0 1.4140271493212666 0.9526474680117316 CAV1 -GO_Biological_Process_2018 regulation of intracellular signal transduction (GO:1902531) 6/422 0.5137207147182432 1.0 0 0 1.0454418734318371 0.6963432371528571 NET1;HSPB1;A2M;KALRN;PJA2;RHOB -GO_Biological_Process_2018 monocarboxylic acid transport (GO:0015718) 1/53 0.5164964012357725 1.0 0 0 1.3873473917869037 0.9166023280305878 ATP8B1 -GO_Biological_Process_2018 positive regulation of tyrosine phosphorylation of STAT protein (GO:0042531) 1/53 0.5164964012357725 1.0 0 0 1.3873473917869037 0.9166023280305878 IL6ST -GO_Biological_Process_2018 non-canonical Wnt signaling pathway (GO:0035567) 2/127 0.5173855760137304 1.0 0 0 1.157943492357573 0.7630464189938134 SFRP4;FRZB -GO_Biological_Process_2018 rRNA processing (GO:0006364) 3/202 0.5200103803955571 1.0 0 0 1.0920209668025629 0.7140796141235729 SBDS;RPL35A;RPS23 -GO_Biological_Process_2018 positive regulation of mitochondrion organization (GO:0010822) 2/128 0.5215842383045128 1.0 0 0 1.1488970588235294 0.747799272519236 LEPROT;TOMM7 -GO_Biological_Process_2018 positive regulation of NF-kappaB transcription factor activity (GO:0051092) 2/128 0.5215842383045128 1.0 0 0 1.1488970588235294 0.747799272519236 APP;CLU -GO_Biological_Process_2018 protein targeting to mitochondrion (GO:0006626) 1/54 0.5230895056202313 1.0 0 0 1.3616557734204793 0.8823566049926231 TOMM7 -GO_Biological_Process_2018 action potential (GO:0001508) 1/54 0.5230895056202313 1.0 0 0 1.3616557734204793 0.8823566049926231 ANK3 -GO_Biological_Process_2018 positive regulation of immune response (GO:0050778) 1/54 0.5230895056202313 1.0 0 0 1.3616557734204793 0.8823566049926231 IL6ST -GO_Biological_Process_2018 membrane fusion (GO:0061025) 1/54 0.5230895056202313 1.0 0 0 1.3616557734204793 0.8823566049926231 USO1 -GO_Biological_Process_2018 regulation of axonogenesis (GO:0050770) 1/55 0.5295930315435577 1.0 0 0 1.3368983957219251 0.8497946957319057 CTTN -GO_Biological_Process_2018 regulation of RNA splicing (GO:0043484) 1/55 0.5295930315435577 1.0 0 0 1.3368983957219251 0.8497946957319057 MBNL1 -GO_Biological_Process_2018 positive regulation of DNA biosynthetic process (GO:2000573) 1/55 0.5295930315435577 1.0 0 0 1.3368983957219251 0.8497946957319057 PDGFRB -GO_Biological_Process_2018 negative regulation of cellular response to growth factor stimulus (GO:0090288) 1/55 0.5295930315435577 1.0 0 0 1.3368983957219251 0.8497946957319057 SKIL -GO_Biological_Process_2018 Notch signaling pathway (GO:0007219) 1/55 0.5295930315435577 1.0 0 0 1.3368983957219251 0.8497946957319057 JAG1 -GO_Biological_Process_2018 protein ubiquitination (GO:0016567) 7/506 0.5351128253140287 1.0 0 0 1.017205301092769 0.6360357563906271 PCNP;RNF11;EPAS1;LMO7;FBXO32;PJA2;SKP1 -GO_Biological_Process_2018 chromosome organization (GO:0051276) 1/56 0.5360081916524269 1.0 0 0 1.3130252100840336 0.8188101826345664 GEM -GO_Biological_Process_2018 regulation of secretion by cell (GO:1903530) 1/56 0.5360081916524269 1.0 0 0 1.3130252100840336 0.8188101826345664 MEF2C -GO_Biological_Process_2018 ribosome assembly (GO:0042255) 1/56 0.5360081916524269 1.0 0 0 1.3130252100840336 0.8188101826345664 SBDS -GO_Biological_Process_2018 cellular defense response (GO:0006968) 1/56 0.5360081916524269 1.0 0 0 1.3130252100840336 0.8188101826345664 ITGB1 -GO_Biological_Process_2018 positive regulation of protein transport (GO:0051222) 1/56 0.5360081916524269 1.0 0 0 1.3130252100840336 0.8188101826345664 MYH10 -GO_Biological_Process_2018 protein import (GO:0017038) 1/56 0.5360081916524269 1.0 0 0 1.3130252100840336 0.8188101826345664 CLU -GO_Biological_Process_2018 regulation of glycolytic process (GO:0006110) 1/57 0.5423361821352921 1.0 0 0 1.2899896800825594 0.7893049633428438 OGT -GO_Biological_Process_2018 regulation of Rho protein signal transduction (GO:0035023) 1/57 0.5423361821352921 1.0 0 0 1.2899896800825594 0.7893049633428438 EPS8 -GO_Biological_Process_2018 I-kappaB kinase/NF-kappaB signaling (GO:0007249) 1/57 0.5423361821352921 1.0 0 0 1.2899896800825594 0.7893049633428438 ROCK1 -GO_Biological_Process_2018 regulation of neurogenesis (GO:0050767) 1/57 0.5423361821352921 1.0 0 0 1.2899896800825594 0.7893049633428438 ROCK1 -GO_Biological_Process_2018 response to retinoic acid (GO:0032526) 1/57 0.5423361821352921 1.0 0 0 1.2899896800825594 0.7893049633428438 AQP1 -GO_Biological_Process_2018 ubiquitin-dependent ERAD pathway (GO:0030433) 1/58 0.5485781831956474 1.0 0 0 1.2677484787018256 0.761188475423958 DNAJC10 -GO_Biological_Process_2018 cholesterol homeostasis (GO:0042632) 1/58 0.5485781831956474 1.0 0 0 1.2677484787018256 0.761188475423958 CAV1 -GO_Biological_Process_2018 positive regulation of cell morphogenesis involved in differentiation (GO:0010770) 1/58 0.5485781831956474 1.0 0 0 1.2677484787018256 0.761188475423958 FLNA -GO_Biological_Process_2018 sterol homeostasis (GO:0055092) 1/58 0.5485781831956474 1.0 0 0 1.2677484787018256 0.761188475423958 CAV1 -GO_Biological_Process_2018 sodium ion transport (GO:0006814) 1/58 0.5485781831956474 1.0 0 0 1.2677484787018256 0.761188475423958 PKD2 -GO_Biological_Process_2018 regulation of extrinsic apoptotic signaling pathway (GO:2001236) 1/59 0.5547353591513081 1.0 0 0 1.2462612163509472 0.7343770056249409 CAV1 -GO_Biological_Process_2018 regulation of pathway-restricted SMAD protein phosphorylation (GO:0060393) 1/59 0.5547353591513081 1.0 0 0 1.2462612163509472 0.7343770056249409 RBPMS -GO_Biological_Process_2018 regulation of mRNA splicing, via spliceosome (GO:0048024) 1/60 0.5608088583395024 1.0 0 0 1.2254901960784317 0.7087930729027963 MBNL1 -GO_Biological_Process_2018 response to cytokine (GO:0034097) 2/138 0.5622105006840508 1.0 0 0 1.0656436487638534 0.6136817378941394 TIMP2;IL6ST -GO_Biological_Process_2018 regulation of primary metabolic process (GO:0080090) 2/139 0.5661352920303011 1.0 0 0 1.05797714769361 0.6019066837567296 TBL1XR1;CTGF -GO_Biological_Process_2018 potassium ion transport (GO:0006813) 1/61 0.5667998139490054 1.0 0 0 1.205400192864031 0.6843648739316724 AQP1 -GO_Biological_Process_2018 regulation of tyrosine phosphorylation of STAT protein (GO:0042509) 1/61 0.5667998139490054 1.0 0 0 1.205400192864031 0.6843648739316724 IL6ST -GO_Biological_Process_2018 positive regulation of leukocyte chemotaxis (GO:0002690) 1/61 0.5667998139490054 1.0 0 0 1.205400192864031 0.6843648739316724 GAS6 -GO_Biological_Process_2018 regulation of cellular ketone metabolic process (GO:0010565) 1/61 0.5667998139490054 1.0 0 0 1.205400192864031 0.6843648739316724 CAV1 -GO_Biological_Process_2018 negative regulation of viral life cycle (GO:1903901) 1/61 0.5667998139490054 1.0 0 0 1.205400192864031 0.6843648739316724 GSN -GO_Biological_Process_2018 striated muscle contraction (GO:0006941) 1/61 0.5667998139490054 1.0 0 0 1.205400192864031 0.6843648739316724 TPM1 -GO_Biological_Process_2018 negative regulation of MAP kinase activity (GO:0043407) 1/61 0.5667998139490054 1.0 0 0 1.205400192864031 0.6843648739316724 CAV1 -GO_Biological_Process_2018 inorganic cation transmembrane transport (GO:0098662) 2/140 0.5700347817918674 1.0 0 0 1.050420168067227 0.5903969530753582 PKD2;GAS6 -GO_Biological_Process_2018 diterpenoid metabolic process (GO:0016101) 1/62 0.572709343685847 1.0 0 0 1.1859582542694498 0.6610257881479962 HSPG2 -GO_Biological_Process_2018 cellular protein catabolic process (GO:0044257) 1/62 0.572709343685847 1.0 0 0 1.1859582542694498 0.6610257881479962 TINAGL1 -GO_Biological_Process_2018 stress-activated MAPK cascade (GO:0051403) 1/62 0.572709343685847 1.0 0 0 1.1859582542694498 0.6610257881479962 SKP1 -GO_Biological_Process_2018 regulation of T cell proliferation (GO:0042129) 1/62 0.572709343685847 1.0 0 0 1.1859582542694498 0.6610257881479962 IL6ST -GO_Biological_Process_2018 regulation of translational initiation (GO:0006446) 1/62 0.572709343685847 1.0 0 0 1.1859582542694498 0.6610257881479962 HSPB1 -GO_Biological_Process_2018 ion transmembrane transport (GO:0034220) 3/219 0.5746713311634556 1.0 0 0 1.0072522159548751 0.5579744153764982 ANO1;GJA1;ATP8B1 -GO_Biological_Process_2018 positive regulation of cytokine production (GO:0001819) 3/220 0.5777645318888124 1.0 0 0 1.002673796791444 0.5500556927694067 IL33;IL6ST;SOD1 -GO_Biological_Process_2018 adenylate cyclase-inhibiting G-protein coupled receptor signaling pathway (GO:0007193) 1/63 0.5785385502430619 1.0 0 0 1.1671335200746966 0.6387139312464435 FLNA -GO_Biological_Process_2018 positive regulation of cell projection organization (GO:0031346) 1/63 0.5785385502430619 1.0 0 0 1.1671335200746966 0.6387139312464435 NPTN -GO_Biological_Process_2018 ribosomal large subunit biogenesis (GO:0042273) 1/63 0.5785385502430619 1.0 0 0 1.1671335200746966 0.6387139312464435 RPL35A -GO_Biological_Process_2018 positive regulation of JAK-STAT cascade (GO:0046427) 1/64 0.5842885213635548 1.0 0 0 1.1488970588235294 0.617371754046922 IL6ST -GO_Biological_Process_2018 glycerophospholipid metabolic process (GO:0006650) 1/64 0.5842885213635548 1.0 0 0 1.1488970588235294 0.617371754046922 PDGFRB -GO_Biological_Process_2018 negative regulation of neuron differentiation (GO:0045665) 1/64 0.5842885213635548 1.0 0 0 1.1488970588235294 0.617371754046922 ID4 -GO_Biological_Process_2018 transmembrane receptor protein serine/threonine kinase signaling pathway (GO:0007178) 2/144 0.5853790072913251 1.0 0 0 1.0212418300653594 0.5468706758777364 LTBP2;FSTL1 -GO_Biological_Process_2018 regulation of nucleic acid-templated transcription (GO:1903506) 8/607 0.5858174194847008 1.0 0 0 0.9690861517588916 0.5182160178298529 FOXC1;MEF2C;EFEMP1;NFIA;ATRX;APBB2;ACTN4;ZNF532 -GO_Biological_Process_2018 peptidyl-serine phosphorylation (GO:0018105) 2/145 0.5891514998830811 1.0 0 0 1.0141987829614605 0.5365840902470336 STK38L;GAS6 -GO_Biological_Process_2018 positive regulation of protein modification by small protein conjugation or removal (GO:1903322) 1/65 0.589960330158279 1.0 0 0 1.1312217194570138 0.5969456802746016 CAV1 -GO_Biological_Process_2018 negative regulation of apoptotic signaling pathway (GO:2001234) 1/65 0.589960330158279 1.0 0 0 1.1312217194570138 0.5969456802746016 CLU -GO_Biological_Process_2018 cellular response to light stimulus (GO:0071482) 1/66 0.5955550352393493 1.0 0 0 1.1140819964349378 0.5773857797673376 AQP1 -GO_Biological_Process_2018 ribosome biogenesis (GO:0042254) 3/226 0.5960259817631259 1.0 0 0 0.9760541384695472 0.5050797299303753 SBDS;RPL35A;RPS23 -GO_Biological_Process_2018 regulation of transcription, DNA-templated (GO:0006355) 21/1598 0.5982297354403318 1.0 0 0 0.9662813811381874 0.4964564594726873 FOXC1;MEF2C;EPAS1;ATP8B1;ATRX;SMARCA5;ILK;NUCKS1;NR2F2;DKK3;EID1;ZEB1;EFEMP1;NFIA;TBL1XR1;SUB1;ID4;APBB2;GAS6;ZNF532;OGT -GO_Biological_Process_2018 ncRNA processing (GO:0034470) 3/227 0.5990193817864249 1.0 0 0 0.9717543405027209 0.4979865164076934 SBDS;RPL35A;RPS23 -GO_Biological_Process_2018 negative regulation of protein serine/threonine kinase activity (GO:0071901) 1/67 0.6010736810122704 1.0 0 0 1.0974539069359088 0.5586454725671469 NR2F2 -GO_Biological_Process_2018 regulation of TOR signaling (GO:0032006) 1/67 0.6010736810122704 1.0 0 0 1.0974539069359088 0.5586454725671469 GAS6 -GO_Biological_Process_2018 extrinsic apoptotic signaling pathway (GO:0097191) 1/67 0.6010736810122704 1.0 0 0 1.0974539069359088 0.5586454725671469 SORT1 -GO_Biological_Process_2018 regulation of cyclin-dependent protein serine/threonine kinase activity (GO:0000079) 1/67 0.6010736810122704 1.0 0 0 1.0974539069359088 0.5586454725671469 NR2F2 -GO_Biological_Process_2018 positive regulation of autophagy (GO:0010508) 1/67 0.6010736810122704 1.0 0 0 1.0974539069359088 0.5586454725671469 ROCK1 -GO_Biological_Process_2018 regulation of G2/M transition of mitotic cell cycle (GO:0010389) 2/149 0.6039868267439543 1.0 0 0 0.9869719699960522 0.4976341209156434 FHL1;SKP1 -GO_Biological_Process_2018 protein sumoylation (GO:0016925) 1/68 0.6065172977739856 1.0 0 0 1.0813148788927336 0.5406812611971594 UBA2 -GO_Biological_Process_2018 positive regulation of T cell activation (GO:0050870) 1/68 0.6065172977739856 1.0 0 0 1.0813148788927336 0.5406812611971594 IL6ST -GO_Biological_Process_2018 cellular response to interleukin-1 (GO:0071347) 2/150 0.6076319947656166 1.0 0 0 0.9803921568627452 0.4884175019940708 PTGIS;SKP1 -GO_Biological_Process_2018 regulation of endocytosis (GO:0030100) 1/69 0.611886901684063 1.0 0 0 1.0656436487638534 0.5234524879768573 CDH13 -GO_Biological_Process_2018 response to organic cyclic compound (GO:0014070) 1/69 0.611886901684063 1.0 0 0 1.0656436487638534 0.5234524879768573 IGFBP5 -GO_Biological_Process_2018 stress-activated protein kinase signaling cascade (GO:0031098) 1/70 0.6171834956057707 1.0 0 0 1.050420168067227 0.5069211130315818 SKP1 -GO_Biological_Process_2018 spermatogenesis (GO:0007283) 2/153 0.6184148636939507 1.0 0 0 0.9611687812379854 0.4619336277106629 CCNI;SOD1 -GO_Biological_Process_2018 male gamete generation (GO:0048232) 2/154 0.6219583405211832 1.0 0 0 0.9549274255156608 0.4534780033804816 CCNI;SOD1 -GO_Biological_Process_2018 regulation of cell communication (GO:0010646) 1/71 0.6224080684802494 1.0 0 0 1.035625517812759 0.4910515148150744 GRIA2 -GO_Biological_Process_2018 recombinational repair (GO:0000725) 1/71 0.6224080684802494 1.0 0 0 1.035625517812759 0.4910515148150744 NUCKS1 -GO_Biological_Process_2018 histone acetylation (GO:0016573) 1/71 0.6224080684802494 1.0 0 0 1.035625517812759 0.4910515148150744 MORF4L2 -GO_Biological_Process_2018 organelle membrane fusion (GO:0090174) 1/71 0.6224080684802494 1.0 0 0 1.035625517812759 0.4910515148150744 CAV2 -GO_Biological_Process_2018 response to lipopolysaccharide (GO:0032496) 2/155 0.6254764540891268 1.0 0 0 0.94876660341556 0.4452007524918621 MEF2C;TNFRSF11B -GO_Biological_Process_2018 SCF-dependent proteasomal ubiquitin-dependent protein catabolic process (GO:0031146) 1/72 0.6275615960808578 1.0 0 0 1.0212418300653594 0.47581030607501 SKP1 -GO_Biological_Process_2018 lymphocyte differentiation (GO:0030098) 1/73 0.6326450410245487 1.0 0 0 1.0072522159548751 0.4611661671999042 ITGB1 -GO_Biological_Process_2018 positive regulation of proteasomal protein catabolic process (GO:1901800) 1/73 0.6326450410245487 1.0 0 0 1.0072522159548751 0.4611661671999042 CLU -GO_Biological_Process_2018 regulation of gene expression, epigenetic (GO:0040029) 1/74 0.6376593528396856 1.0 0 0 0.9936406995230526 0.44708969390594056 SMARCA5 -GO_Biological_Process_2018 glucose homeostasis (GO:0042593) 1/74 0.6376593528396856 1.0 0 0 0.9936406995230526 0.44708969390594056 NUCKS1 -GO_Biological_Process_2018 mRNA 3'-end processing (GO:0031124) 1/74 0.6376593528396856 1.0 0 0 0.9936406995230526 0.44708969390594056 APP -GO_Biological_Process_2018 regulation of G-protein coupled receptor protein signaling pathway (GO:0008277) 1/74 0.6376593528396856 1.0 0 0 0.9936406995230526 0.44708969390594056 RGS5 -GO_Biological_Process_2018 retinoid metabolic process (GO:0001523) 1/75 0.642605468326459 1.0 0 0 0.9803921568627452 0.43355325749716417 HSPG2 -GO_Biological_Process_2018 purine ribonucleotide biosynthetic process (GO:0009152) 1/75 0.642605468326459 1.0 0 0 0.9803921568627452 0.43355325749716417 AQP1 -GO_Biological_Process_2018 double-strand break repair via homologous recombination (GO:0000724) 1/76 0.6474843116692787 1.0 0 0 0.9674922600619196 0.4205308773066643 NUCKS1 -GO_Biological_Process_2018 regulation of hematopoietic stem cell differentiation (GO:1902036) 1/76 0.6474843116692787 1.0 0 0 0.9674922600619196 0.4205308773066643 FOXC1 -GO_Biological_Process_2018 nucleic acid-templated transcription (GO:0097659) 1/76 0.6474843116692787 1.0 0 0 0.9674922600619196 0.4205308773066643 MEF2A -GO_Biological_Process_2018 positive regulation of ubiquitin-protein ligase activity involved in regulation of mitotic cell cycle transition (GO:0051437) 1/76 0.6474843116692787 1.0 0 0 0.9674922600619196 0.4205308773066643 SKP1 -GO_Biological_Process_2018 hemopoiesis (GO:0030097) 1/76 0.6474843116692787 1.0 0 0 0.9674922600619196 0.4205308773066643 JAG1 -GO_Biological_Process_2018 response to interleukin-1 (GO:0070555) 1/76 0.6474843116692787 1.0 0 0 0.9674922600619196 0.4205308773066643 PTGIS -GO_Biological_Process_2018 water-soluble vitamin metabolic process (GO:0006767) 1/77 0.6522967943032942 1.0 0 0 0.9549274255156608 0.4079981041287729 CYB5R3 -GO_Biological_Process_2018 ERAD pathway (GO:0036503) 1/77 0.6522967943032942 1.0 0 0 0.9549274255156608 0.4079981041287729 DNAJC10 -GO_Biological_Process_2018 regulation of hematopoietic progenitor cell differentiation (GO:1901532) 1/77 0.6522967943032942 1.0 0 0 0.9549274255156608 0.4079981041287729 FOXC1 -GO_Biological_Process_2018 regulation of cytokinesis (GO:0032465) 1/77 0.6522967943032942 1.0 0 0 0.9549274255156608 0.4079981041287729 SVIL -GO_Biological_Process_2018 sensory perception of mechanical stimulus (GO:0050954) 1/78 0.6570438157555529 1.0 0 0 0.942684766214178 0.395931911834019 SOD1 -GO_Biological_Process_2018 positive regulation of translation (GO:0045727) 1/78 0.6570438157555529 1.0 0 0 0.942684766214178 0.395931911834019 FAM129A -GO_Biological_Process_2018 regulation of cellular response to heat (GO:1900034) 1/78 0.6570438157555529 1.0 0 0 0.942684766214178 0.395931911834019 CRYAB -GO_Biological_Process_2018 fibroblast growth factor receptor signaling pathway (GO:0008543) 1/78 0.6570438157555529 1.0 0 0 0.942684766214178 0.395931911834019 FGFR1 -GO_Biological_Process_2018 regulation of transcription from RNA polymerase II promoter in response to hypoxia (GO:0061418) 1/78 0.6570438157555529 1.0 0 0 0.942684766214178 0.395931911834019 EPAS1 -GO_Biological_Process_2018 visual perception (GO:0007601) 1/78 0.6570438157555529 1.0 0 0 0.942684766214178 0.395931911834019 EFEMP1 -GO_Biological_Process_2018 retrograde vesicle-mediated transport, Golgi to ER (GO:0006890) 1/80 0.6663450117331924 1.0 0 0 0.9191176470588236 0.37311370123204185 KLC1 -GO_Biological_Process_2018 spindle assembly (GO:0051225) 1/80 0.6663450117331924 1.0 0 0 0.9191176470588236 0.37311370123204185 FLNA -GO_Biological_Process_2018 NIK/NF-kappaB signaling (GO:0038061) 1/80 0.6663450117331924 1.0 0 0 0.9191176470588236 0.37311370123204185 SKP1 -GO_Biological_Process_2018 negative regulation of MAPK cascade (GO:0043409) 1/80 0.6663450117331924 1.0 0 0 0.9191176470588236 0.37311370123204185 CAV1 -GO_Biological_Process_2018 epidermis development (GO:0008544) 1/81 0.6709009247529266 1.0 0 0 0.9077705156136529 0.3623219009055435 CTGF -GO_Biological_Process_2018 BMP signaling pathway (GO:0030509) 1/81 0.6709009247529266 1.0 0 0 0.9077705156136529 0.3623219009055435 FSTL1 -GO_Biological_Process_2018 sensory perception of light stimulus (GO:0050953) 1/81 0.6709009247529266 1.0 0 0 0.9077705156136529 0.3623219009055435 EFEMP1 -GO_Biological_Process_2018 sensory perception of sound (GO:0007605) 1/81 0.6709009247529266 1.0 0 0 0.9077705156136529 0.3623219009055435 SOD1 -GO_Biological_Process_2018 protein catabolic process (GO:0030163) 1/81 0.6709009247529266 1.0 0 0 0.9077705156136529 0.3623219009055435 MGEA5 -GO_Biological_Process_2018 peptidyl-serine modification (GO:0018209) 2/170 0.6752346421370338 1.0 0 0 0.8650519031141869 0.3397015835068759 STK38L;GAS6 -GO_Biological_Process_2018 DNA damage response, signal transduction by p53 class mediator (GO:0030330) 1/82 0.6753948540366097 1.0 0 0 0.896700143472023 0.3519169567737805 ATRX -GO_Biological_Process_2018 negative regulation of NF-kappaB transcription factor activity (GO:0032088) 1/82 0.6753948540366097 1.0 0 0 0.896700143472023 0.3519169567737805 PTGIS -GO_Biological_Process_2018 positive regulation of cell cycle (GO:0045787) 1/82 0.6753948540366097 1.0 0 0 0.896700143472023 0.3519169567737805 APP -GO_Biological_Process_2018 positive regulation of ubiquitin protein ligase activity (GO:1904668) 1/82 0.6753948540366097 1.0 0 0 0.896700143472023 0.3519169567737805 SKP1 -GO_Biological_Process_2018 positive regulation of protein secretion (GO:0050714) 1/82 0.6753948540366097 1.0 0 0 0.896700143472023 0.3519169567737805 MYH10 -GO_Biological_Process_2018 positive regulation of cell cycle arrest (GO:0071158) 1/82 0.6753948540366097 1.0 0 0 0.896700143472023 0.3519169567737805 PKD2 -GO_Biological_Process_2018 protein deubiquitination (GO:0016579) 3/257 0.6819448524078062 1.0 0 0 0.8583199816891738 0.3285704560370907 IL33;USP34;OGT -GO_Biological_Process_2018 protein modification process (GO:0036211) 1/84 0.6842001109883779 1.0 0 0 0.8753501400560224 0.3321996185419881 CPE -GO_Biological_Process_2018 cellular response to molecule of bacterial origin (GO:0071219) 1/84 0.6842001109883779 1.0 0 0 0.8753501400560224 0.3321996185419881 MEF2C -GO_Biological_Process_2018 ubiquitin-dependent protein catabolic process (GO:0006511) 4/341 0.6844475697884622 1.0 0 0 0.8625150940141453 0.327016761731792 PCNP;RNF11;TBL1XR1;SKP1 -GO_Biological_Process_2018 nuclear-transcribed mRNA catabolic process (GO:0000956) 2/174 0.6875646586543727 1.0 0 0 0.8451656524678838 0.31659855046646496 RPL35A;RPS23 -GO_Biological_Process_2018 peptide biosynthetic process (GO:0043043) 2/174 0.6875646586543727 1.0 0 0 0.8451656524678838 0.31659855046646496 RPL35A;RPS23 -GO_Biological_Process_2018 regulation of peptidyl-tyrosine phosphorylation (GO:0050730) 1/85 0.6885130852350059 1.0 0 0 0.8650519031141869 0.3228554978972173 APP -GO_Biological_Process_2018 cellular response to BMP stimulus (GO:0071773) 1/87 0.6969637583750068 1.0 0 0 0.8451656524678838 0.305123281073371 FSTL1 -GO_Biological_Process_2018 calcium ion homeostasis (GO:0055074) 1/87 0.6969637583750068 1.0 0 0 0.8451656524678838 0.305123281073371 CAV1 -GO_Biological_Process_2018 intracellular protein transport (GO:0006886) 4/347 0.6975103690751886 1.0 0 0 0.8476012883539583 0.3053381075940812 CTTN;USO1;LTBP2;SEC31A -GO_Biological_Process_2018 cellular response to lipid (GO:0071396) 2/178 0.6995088661531909 1.0 0 0 0.8261731658955718 0.29525513051077035 MEF2C;AQP1 -GO_Biological_Process_2018 negative regulation of translation (GO:0017148) 1/88 0.7011030381419441 1.0 0 0 0.8355614973262033 0.29670823478061903 IGFBP5 -GO_Biological_Process_2018 negative regulation of cellular protein metabolic process (GO:0032269) 1/89 0.7051859826565431 1.0 0 0 0.8261731658955718 0.28857708672649723 IGFBP5 -GO_Biological_Process_2018 regulation of cell cycle process (GO:0010564) 1/90 0.7092133558638481 1.0 0 0 0.8169934640522877 0.2807180332222872 CAV2 -GO_Biological_Process_2018 regulation of cytokine-mediated signaling pathway (GO:0001959) 1/90 0.7092133558638481 1.0 0 0 0.8169934640522877 0.2807180332222872 GAS6 -GO_Biological_Process_2018 regulation of protein catabolic process (GO:0042176) 1/91 0.7131859114263601 1.0 0 0 0.8080155138978671 0.2731198668531718 FLNA -GO_Biological_Process_2018 cellular response to acid chemical (GO:0071229) 1/91 0.7131859114263601 1.0 0 0 0.8080155138978671 0.2731198668531718 AQP1 -GO_Biological_Process_2018 cellular response to lipopolysaccharide (GO:0071222) 1/91 0.7131859114263601 1.0 0 0 0.8080155138978671 0.2731198668531718 MEF2C -GO_Biological_Process_2018 phospholipase C-activating G-protein coupled receptor signaling pathway (GO:0007200) 1/91 0.7131859114263601 1.0 0 0 0.8080155138978671 0.2731198668531718 ANO1 -GO_Biological_Process_2018 regulation of transcription from RNA polymerase II promoter in response to stress (GO:0043618) 1/93 0.7209695331604391 1.0 0 0 0.7906388361796333 0.2586641357966182 EPAS1 -GO_Biological_Process_2018 proteolysis involved in cellular protein catabolic process (GO:0051603) 1/93 0.7209695331604391 1.0 0 0 0.7906388361796333 0.2586641357966182 TINAGL1 -GO_Biological_Process_2018 cellular response to fibroblast growth factor stimulus (GO:0044344) 1/94 0.7247820561533257 1.0 0 0 0.7822277847309137 0.2517868284751854 FGFR1 -GO_Biological_Process_2018 vesicle fusion (GO:0006906) 1/94 0.7247820561533257 1.0 0 0 0.7822277847309137 0.2517868284751854 CAV2 -GO_Biological_Process_2018 interleukin-1-mediated signaling pathway (GO:0070498) 1/95 0.728542675466188 1.0 0 0 0.7739938080495357 0.2451308631193555 SKP1 -GO_Biological_Process_2018 protein complex assembly (GO:0006461) 2/189 0.7304161565902056 1.0 0 0 0.7780890133831311 0.24442952828457104 USO1;SEC31A -GO_Biological_Process_2018 protein localization to nucleus (GO:0034504) 1/96 0.7322520950675093 1.0 0 0 0.7659313725490197 0.2386875246181585 SKP1 -GO_Biological_Process_2018 cellular metal ion homeostasis (GO:0006875) 1/96 0.7322520950675093 1.0 0 0 0.7659313725490197 0.2386875246181585 CAV1 -GO_Biological_Process_2018 positive regulation of neuron projection development (GO:0010976) 1/97 0.7359110095360712 1.0 0 0 0.7580351728320195 0.2324485131291728 NPTN -GO_Biological_Process_2018 response to tumor necrosis factor (GO:0034612) 1/97 0.7359110095360712 1.0 0 0 0.7580351728320195 0.2324485131291728 NPNT -GO_Biological_Process_2018 protein localization to organelle (GO:0033365) 1/97 0.7359110095360712 1.0 0 0 0.7580351728320195 0.2324485131291728 TMEM30A -GO_Biological_Process_2018 cellular macromolecule biosynthetic process (GO:0034645) 4/367 0.7382433116679898 1.0 0 0 0.8014104824491104 0.2432135102665796 MEF2A;RPL35A;RPS23;PTMS -GO_Biological_Process_2018 regulation of insulin secretion (GO:0050796) 1/98 0.7395201040137492 1.0 0 0 0.7503001200480192 0.2264059208026544 NOV -GO_Biological_Process_2018 response to molecule of bacterial origin (GO:0002237) 1/98 0.7395201040137492 1.0 0 0 0.7503001200480192 0.2264059208026544 TNFRSF11B -GO_Biological_Process_2018 RNA processing (GO:0006396) 2/193 0.7409699662369972 1.0 0 0 0.7619628162145688 0.228432784137104 MBNL1;RBPMS -GO_Biological_Process_2018 protein polyubiquitination (GO:0000209) 3/283 0.7429589013653309 1.0 0 0 0.7794637289544795 0.2315900152582747 LMO7;FBXO32;SKP1 -GO_Biological_Process_2018 cellular divalent inorganic cation homeostasis (GO:0072503) 1/101 0.7500551806596405 1.0 0 0 0.7280139778683751 0.20938300884200392 CAV1 -GO_Biological_Process_2018 metal ion transport (GO:0030001) 1/101 0.7500551806596405 1.0 0 0 0.7280139778683751 0.20938300884200392 AQP1 -GO_Biological_Process_2018 intrinsic apoptotic signaling pathway (GO:0097193) 1/101 0.7500551806596405 1.0 0 0 0.7280139778683751 0.20938300884200392 DNAJC10 -GO_Biological_Process_2018 cilium assembly (GO:0060271) 3/288 0.7535639353206565 1.0 0 0 0.7659313725490197 0.21671370511101049 GSN;SEPT7;FLNA -GO_Biological_Process_2018 ion transport (GO:0006811) 3/289 0.7556424034888479 1.0 0 0 0.7632810909831059 0.21386145885853086 ANO1;GJA1;ATP8B1 -GO_Biological_Process_2018 regulation of cellular response to stress (GO:0080135) 1/104 0.7601656674623802 1.0 0 0 0.7070135746606335 0.1938764747974973 CRYAB -GO_Biological_Process_2018 B cell receptor signaling pathway (GO:0050853) 1/104 0.7601656674623802 1.0 0 0 0.7070135746606335 0.1938764747974973 MEF2C -GO_Biological_Process_2018 monovalent inorganic cation transport (GO:0015672) 1/104 0.7601656674623802 1.0 0 0 0.7070135746606335 0.1938764747974973 AQP1 -GO_Biological_Process_2018 regulation of autophagy (GO:0010506) 2/203 0.7658223382509833 1.0 0 0 0.7244277021153289 0.19328098416450445 ROCK1;HSPB1 -GO_Biological_Process_2018 DNA-templated transcription, termination (GO:0006353) 1/106 0.7666785575604318 1.0 0 0 0.6936736958934517 0.1843005382851343 CAVIN1 -GO_Biological_Process_2018 positive regulation of cellular catabolic process (GO:0031331) 1/106 0.7666785575604318 1.0 0 0 0.6936736958934517 0.1843005382851343 ROCK1 -GO_Biological_Process_2018 positive regulation of cellular component organization (GO:0051130) 1/106 0.7666785575604318 1.0 0 0 0.6936736958934517 0.1843005382851343 ADD1 -GO_Biological_Process_2018 RNA splicing (GO:0008380) 1/106 0.7666785575604318 1.0 0 0 0.6936736958934517 0.1843005382851343 MBNL1 -GO_Biological_Process_2018 positive regulation of epithelial cell proliferation (GO:0050679) 1/107 0.769868615791715 1.0 0 0 0.6871907641561297 0.17972471655792546 CDH13 -GO_Biological_Process_2018 regulation of protein secretion (GO:0050708) 1/107 0.769868615791715 1.0 0 0 0.6871907641561297 0.17972471655792546 MYH10 -GO_Biological_Process_2018 positive regulation of cysteine-type endopeptidase activity involved in apoptotic process (GO:0043280) 1/109 0.7761189484878155 1.0 0 0 0.6745817593092284 0.1709724004397922 GSN -GO_Biological_Process_2018 macroautophagy (GO:0016236) 1/110 0.7791803947062851 1.0 0 0 0.6684491978609626 0.16678655599481193 TOMM7 -GO_Biological_Process_2018 negative regulation of cytokine production (GO:0001818) 1/110 0.7791803947062851 1.0 0 0 0.6684491978609626 0.16678655599481193 GAS6 -GO_Biological_Process_2018 phosphatidylinositol metabolic process (GO:0046488) 1/112 0.7851787171223406 1.0 0 0 0.6565126050420168 0.15877358323759158 PDGFRB -GO_Biological_Process_2018 positive regulation of developmental process (GO:0051094) 1/113 0.7881167181818356 1.0 0 0 0.6507027589796981 0.15493823566682394 CTGF -GO_Biological_Process_2018 positive regulation of sequence-specific DNA binding transcription factor activity (GO:0051091) 2/215 0.7928956863683884 1.0 0 0 0.6839945280437757 0.1587302387421054 APP;CLU -GO_Biological_Process_2018 regulation of cell cycle (GO:0051726) 2/215 0.7928956863683884 1.0 0 0 0.6839945280437757 0.1587302387421054 ID4;RHOB -GO_Biological_Process_2018 activation of MAPK activity (GO:0000187) 1/117 0.7994737594784006 1.0 0 0 0.6284565108094521 0.14064955279787705 SOD1 -GO_Biological_Process_2018 positive regulation of B cell activation (GO:0050871) 1/119 0.8049227342907571 1.0 0 0 0.6178942165101334 0.13408859887399344 MEF2C -GO_Biological_Process_2018 regulation of neuron projection development (GO:0010975) 1/119 0.8049227342907571 1.0 0 0 0.6178942165101334 0.13408859887399344 NPTN -GO_Biological_Process_2018 brain development (GO:0007420) 1/119 0.8049227342907571 1.0 0 0 0.6178942165101334 0.13408859887399344 HSPG2 -GO_Biological_Process_2018 DNA replication (GO:0006260) 1/120 0.8075916433552928 1.0 0 0 0.6127450980392157 0.13094285545440004 PTMS -GO_Biological_Process_2018 stimulatory C-type lectin receptor signaling pathway (GO:0002223) 1/121 0.8102241702309049 1.0 0 0 0.6076810889645115 0.12788303125505296 SKP1 -GO_Biological_Process_2018 gene expression (GO:0010467) 4/411 0.8130785369671377 1.0 0 0 0.7156147130385001 0.14808041553195286 MEF2A;RBPMS;RPL35A;RPS23 -GO_Biological_Process_2018 positive regulation of nucleic acid-templated transcription (GO:1903508) 5/502 0.8160515311277788 1.0 0 0 0.7323646590110148 0.14887345846033356 FOXC1;MEF2C;TBL1XR1;ILK;NR2F2 -GO_Biological_Process_2018 innate immune response activating cell surface receptor signaling pathway (GO:0002220) 1/124 0.8179083665160062 1.0 0 0 0.5929791271347249 0.11919175167713965 SKP1 -GO_Biological_Process_2018 translation (GO:0006412) 2/232 0.8265210479788829 1.0 0 0 0.6338742393509128 0.12077199267633675 RPL35A;RPS23 -GO_Biological_Process_2018 regulation of mRNA stability (GO:0043488) 1/128 0.8276738370645137 1.0 0 0 0.5744485294117647 0.10864896529822443 HSPB1 -GO_Biological_Process_2018 activation of protein kinase activity (GO:0032147) 2/233 0.8283379005219922 1.0 0 0 0.6311537490532694 0.11886778302579877 GAS6;SOD1 -GO_Biological_Process_2018 cellular response to organic substance (GO:0071310) 1/133 0.8391500426860937 1.0 0 0 0.5528527200353825 0.09695143374596307 IGFBP5 -GO_Biological_Process_2018 Fc-gamma receptor signaling pathway involved in phagocytosis (GO:0038096) 1/133 0.8391500426860937 1.0 0 0 0.5528527200353825 0.09695143374596307 NCKAP1 -GO_Biological_Process_2018 Fc-gamma receptor signaling pathway (GO:0038094) 1/134 0.8413522235586169 1.0 0 0 0.5487269534679544 0.09478977804928307 NCKAP1 -GO_Biological_Process_2018 mitochondrial transport (GO:0006839) 1/135 0.8435243635592125 1.0 0 0 0.5446623093681918 0.09268327534075144 TOMM7 -GO_Biological_Process_2018 Fc receptor mediated stimulatory signaling pathway (GO:0002431) 1/135 0.8435243635592125 1.0 0 0 0.5446623093681918 0.09268327534075144 NCKAP1 -GO_Biological_Process_2018 regulation of vesicle-mediated transport (GO:0060627) 1/138 0.8498645936205809 1.0 0 0 0.5328218243819267 0.08667851865434359 CDH13 -GO_Biological_Process_2018 response to lipid (GO:0033993) 1/140 0.8539485487913296 1.0 0 0 0.5252100840336135 0.08292244449866276 TNFRSF11B -GO_Biological_Process_2018 double-strand break repair (GO:0006302) 1/141 0.8559488268447879 1.0 0 0 0.5214851898206091 0.08111425028721937 NUCKS1 -GO_Biological_Process_2018 modification-dependent protein catabolic process (GO:0019941) 1/141 0.8559488268447879 1.0 0 0 0.5214851898206091 0.08111425028721937 RNF11 -GO_Biological_Process_2018 inflammatory response (GO:0006954) 2/252 0.8597534526581452 1.0 0 0 0.5835667600373483 0.08818254773088402 TNFRSF11B;HSPG2 -GO_Biological_Process_2018 transcription, DNA-templated (GO:0006351) 3/356 0.8654876335492986 1.0 0 0 0.6196298744216788 0.08951309031126016 MEF2A;EPAS1;SMARCA5 -GO_Biological_Process_2018 antigen receptor-mediated signaling pathway (GO:0050851) 2/257 0.8671018799133118 1.0 0 0 0.5722133211261159 0.08159693325397775 MEF2C;SKP1 -GO_Biological_Process_2018 ribonucleoprotein complex assembly (GO:0022618) 1/156 0.8828764178483237 1.0 0 0 0.471342383107089 0.058715142020059036 RPS23 -GO_Biological_Process_2018 transcription initiation from RNA polymerase II promoter (GO:0006367) 1/160 0.8891675678668584 1.0 0 0 0.4595588235294118 0.05398417782231872 CTGF -GO_Biological_Process_2018 negative regulation of intracellular signal transduction (GO:1902532) 1/161 0.8906870193578725 1.0 0 0 0.4567044208987943 0.05286910037419463 CAV1 -GO_Biological_Process_2018 T cell receptor signaling pathway (GO:0050852) 1/163 0.8936639375626885 1.0 0 0 0.4511006856730422 0.05071521257966539 SKP1 -GO_Biological_Process_2018 organonitrogen compound biosynthetic process (GO:1901566) 1/181 0.9170711480640944 1.0 0 0 0.4062398440038999 0.035168273431629364 GLS -GO_Biological_Process_2018 Fc-epsilon receptor signaling pathway (GO:0038095) 1/182 0.9182092534534368 1.0 0 0 0.4040077569489335 0.0344739695373152 SKP1 -GO_Biological_Process_2018 Fc receptor signaling pathway (GO:0038093) 1/183 0.9193317958930208 1.0 0 0 0.4018000642880103 0.0337946727905075 SKP1 -GO_Biological_Process_2018 regulation of mitotic cell cycle phase transition (GO:1901990) 1/184 0.9204389873504912 1.0 0 0 0.3996163682864449 0.03313002021266934 SKP1 -GO_Biological_Process_2018 RNA metabolic process (GO:0016070) 1/191 0.9277767165111692 1.0 0 0 0.3849707422235911 0.028859016921114308 RBPMS -GO_Biological_Process_2018 DNA metabolic process (GO:0006259) 2/314 0.9291851503230178 1.0 0 0 0.4683402023229674 0.034398304306578316 ATRX;PTMS -GO_Biological_Process_2018 RNA splicing, via transesterification reactions with bulged adenosine as nucleophile (GO:0000377) 1/236 0.9612585983132644 1.0 0 0 0.3115653040877368 0.012310510117464809 RBPMS -GO_Biological_Process_2018 anterograde trans-synaptic signaling (GO:0098916) 1/240 0.9633477066441892 1.0 0 0 0.30637254901960786 0.011440216379652238 GRIA2 -GO_Biological_Process_2018 regulation of immune response (GO:0050776) 1/251 0.9685317667808274 1.0 0 0 0.29294586360440594 0.009366650074053854 ITGB1 -GO_Biological_Process_2018 mRNA splicing, via spliceosome (GO:0000398) 1/261 0.972607425814958 1.0 0 0 0.2817218841559612 0.007824753782749298 RBPMS -GO_Biological_Process_2018 mRNA processing (GO:0006397) 1/283 0.9798163042418012 1.0 0 0 0.25982124298482645 0.005297799194212961 RBPMS -GO_Biological_Process_2018 DNA repair (GO:0006281) 1/288 0.9811705804792644 1.0 0 0 0.25531045751633985 0.004853183785819382 NUCKS1 -GO_Biological_Process_2018 chemical synaptic transmission (GO:0007268) 1/289 0.9814303682447606 1.0 0 0 0.25442703032770203 0.004769034196431285 GRIA2 -GO_Biological_Process_2018 transcription from RNA polymerase II promoter (GO:0006366) 2/485 0.9905530532624968 1.0 0 0 0.3032140691328078 0.0028780631217281702 EPAS1;CTGF diff --git a/session_topology/lab/assests/Enrichr/KEGG_2019_Human.human.enrichr.reports.png b/session_topology/lab/assests/Enrichr/KEGG_2019_Human.human.enrichr.reports.png deleted file mode 100644 index 033c64e49ee72f4e1c37a0c884de139fa30f31bc..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 172077 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z#X`E<4Bm4;A08xc~qF diff --git a/session_topology/lab/assests/Enrichr/KEGG_2019_Human.human.enrichr.reports.txt b/session_topology/lab/assests/Enrichr/KEGG_2019_Human.human.enrichr.reports.txt deleted file mode 100644 index 772c02a0..00000000 --- a/session_topology/lab/assests/Enrichr/KEGG_2019_Human.human.enrichr.reports.txt +++ /dev/null @@ -1,161 +0,0 @@ -Gene_set Term Overlap P-value Adjusted P-value Old P-value Old Adjusted P-value Odds Ratio Combined Score Genes -KEGG_2019_Human Focal adhesion 24/199 3.939728600559394e-16 1.2134364089722933e-13 0 0 8.867868755542418 314.5455186231823 PDGFRB;ITGB1;PDGFRA;PPP1R12A;VWF;ROCK1;CAV2;LAMB2;ACTN1;CAV1;ITGA1;ILK;ACTN4;MYLK;PPP1CB;COL4A2;ITGA11;COL6A1;ITGA8;FLNA;TLN1;PPP1R12B;MYL9;VCL -KEGG_2019_Human Regulation of actin cytoskeleton 22/214 1.9515304281886292e-13 3.005356859410489e-11 0 0 7.559098405717427 221.21695670780895 PDGFRB;ITGB1;NCKAP1;PDGFRA;GSN;PPP1R12A;ROCK1;ACTN1;ITGA1;ACTN4;MYLK;ENAH;PPP1CB;ITGA11;ITGA8;MYH9;PPP1R12B;MYL9;MYH10;VCL;PFN2;FGFR1 -KEGG_2019_Human Vascular smooth muscle contraction 12/132 2.5785931141018904e-07 2.6473555971446075e-05 0 0 6.6844919786096275 101.40943653321591 PPP1CB;ACTA2;PPP1R14A;GUCY1A1;PPP1R12A;ROCK1;CALD1;MYH11;PPP1R12B;MYL9;PRKG1;MYLK -KEGG_2019_Human Proteoglycans in cancer 14/201 6.976767796890097e-07 5.372111203605374e-05 0 0 5.121451565700908 72.59918741181114 ITGB1;PPP1R12A;ROCK1;CAV2;CAV1;MMP2;ANK3;HSPG2;PPP1CB;CTTN;COL21A1;FLNA;PPP1R12B;FGFR1 -KEGG_2019_Human ECM-receptor interaction 9/82 1.7176918986317643e-06 0.00010580982095571667 0 0 8.070301291248207 107.12944923733204 ITGB1;VWF;COL4A2;LAMB2;ITGA1;COL6A1;ITGA11;ITGA8;HSPG2 -KEGG_2019_Human cGMP-PKG signaling pathway 11/166 1.764061630668101e-05 0.0009055516370762916 0 0 4.8724309000708725 53.33024994229728 PPP1CB;MEF2A;GUCY1A1;MEF2C;PPP1R12A;PLN;ROCK1;PDE5A;MYL9;PRKG1;MYLK -KEGG_2019_Human Tight junction 11/170 2.203180951587685e-05 0.0009693996186985814 0 0 4.757785467128028 51.0178442387101 ITGB1;TJP1;CTTN;ROCK1;ACTN1;MYH9;MYH11;ACTN4;SYNPO;MYL9;MYH10 -KEGG_2019_Human Dilated cardiomyopathy (DCM) 8/91 3.384381037514927e-05 0.0013029866994432471 0 0 6.464124111182937 66.54010620443874 ITGB1;PLN;TPM2;CACNA2D1;ITGA1;TPM1;ITGA11;ITGA8 -KEGG_2019_Human Platelet activation 9/124 5.064224878919744e-05 0.0017330902918969787 0 0 5.336812144212526 52.784937952859615 PPP1CB;ITGB1;GUCY1A1;PPP1R12A;VWF;ROCK1;TLN1;PRKG1;MYLK -KEGG_2019_Human Adherens junction 7/72 5.519901974359909e-05 0.0017001298081028519 0 0 7.1486928104575185 70.08982592090406 TJP1;ACTN1;LMO7;ACTN4;SORBS1;VCL;FGFR1 -KEGG_2019_Human Hypertrophic cardiomyopathy (HCM) 7/85 0.00015919873510733808 0.0044575645830054675 0 0 6.055363321799308 52.956315405915696 ITGB1;TPM2;CACNA2D1;ITGA1;TPM1;ITGA11;ITGA8 -KEGG_2019_Human Salmonella infection 7/86 0.00017131999525673466 0.0043972132115895225 0 0 5.984952120383038 51.90136972497077 TJP1;ROCK1;MYH9;FLNA;KLC1;MYH10;PFN2 -KEGG_2019_Human Oxytocin signaling pathway 9/153 0.00025273978613641045 0.005987988779231878 0 0 4.3252595155709335 35.826773840027336 PPP1CB;GUCY1A1;MEF2C;PPP1R12A;ROCK1;CACNA2D1;PPP1R12B;MYL9;MYLK -KEGG_2019_Human Arrhythmogenic right ventricular cardiomyopathy (ARVC) 6/72 0.0004412950967941586 0.009708492129471488 0 0 6.127450980392156 47.33944088586219 ITGB1;GJA1;CACNA2D1;ITGA1;ITGA11;ITGA8 -KEGG_2019_Human Bacterial invasion of epithelial cells 6/74 0.0005115434938667711 0.010503693074064369 0 0 5.9618441971383165 45.17932001703107 ITGB1;CTTN;CAV2;CAV1;ILK;VCL -KEGG_2019_Human Leukocyte transendothelial migration 7/112 0.0008567714507825956 0.016492850427564966 0 0 4.595588235294118 32.45560367708864 ITGB1;ROCK1;ACTN1;MMP2;ACTN4;MYL9;VCL -KEGG_2019_Human Gap junction 6/88 0.0012778472605185756 0.023151585661160073 0 0 5.01336898395722 33.40196412749211 PDGFRB;TJP1;PDGFRA;GJA1;GUCY1A1;PRKG1 -KEGG_2019_Human Protein digestion and absorption 6/90 0.00143540785691625 0.02456142332945584 0 0 4.901960784313727 32.0897365162063 COL4A2;COL14A1;ELN;COL6A1;COL21A1;SLC38A2 -KEGG_2019_Human Shigellosis 5/65 0.0019044895061039286 0.03087277725684263 0 0 5.656108597285066 35.42726969521417 ITGB1;CTTN;ROCK1;VCL;PFN2 -KEGG_2019_Human Amoebiasis 6/96 0.0019977995166266713 0.03076611255605074 0 0 4.595588235294118 28.56483890541851 COL4A2;LAMB2;ACTN1;HSPB1;ACTN4;VCL -KEGG_2019_Human Complement and coagulation cascades 5/79 0.004457245629719468 0.06537293590255219 0 0 4.6537602382725245 25.191847894350172 VWF;SERPING1;A2M;CLU;CD55 -KEGG_2019_Human TGF-beta signaling pathway 5/90 0.007708626496768672 0.1079207709547614 0 0 4.084967320261438 19.875062307850172 ROCK1;ID4;FMOD;LTBP1;SKP1 -KEGG_2019_Human PI3K-Akt signaling pathway 11/354 0.009451277993651972 0.12656494008890468 0 0 2.284812229976737 10.650892821777227 PDGFRB;ITGB1;PDGFRA;COL4A2;VWF;LAMB2;ITGA11;COL6A1;ITGA1;ITGA8;FGFR1 -KEGG_2019_Human Central carbon metabolism in cancer 4/65 0.011841808719011663 0.1519698785606497 0 0 4.524886877828054 20.072936188974584 PDGFRB;PDGFRA;GLS;FGFR1 -KEGG_2019_Human African trypanosomiasis 3/37 0.01374657822559041 0.16935784373927384 0 0 5.9618441971383165 25.558219448490423 HBB;HBA2;HBA1 -KEGG_2019_Human Human papillomavirus infection 10/330 0.015291853418803257 0.181149648191977 0 0 2.2281639928698755 9.314694849991763 PDGFRB;ITGB1;JAG1;COL4A2;VWF;LAMB2;ITGA11;COL6A1;ITGA1;ITGA8 -KEGG_2019_Human Malaria 3/49 0.02895489837874372 0.3303003222464098 0 0 4.5018007202881165 15.945449675590188 HBB;HBA2;HBA1 -KEGG_2019_Human Other types of O-glycan biosynthesis 2/22 0.03559647586046295 0.3915612344650925 0 0 6.6844919786096275 22.296180740025918 EOGT;OGT -KEGG_2019_Human Proximal tubule bicarbonate reclamation 2/23 0.03864356360448186 0.41042129621311774 0 0 6.3938618925831205 20.801630742336886 GLS;AQP1 -KEGG_2019_Human Pathogenic Escherichia coli infection 3/55 0.038875077459333496 0.3991174619158239 0 0 4.0106951871657746 13.024339235140411 ITGB1;CTTN;ROCK1 -KEGG_2019_Human Apelin signaling pathway 5/137 0.039449647163154085 0.3919513331048858 0 0 2.683555173894375 8.675209788616414 MEF2A;ACTA2;MEF2C;JAG1;MYLK -KEGG_2019_Human Fluid shear stress and atherosclerosis 5/139 0.041561873732611 0.4000330346763809 0 0 2.6449428692340247 8.412431307572852 MEF2A;MEF2C;CAV2;CAV1;MMP2 -KEGG_2019_Human Signaling pathways regulating pluripotency of stem cells 5/139 0.041561873732611 0.3879108215043693 0 0 2.6449428692340247 8.412431307572852 PCGF5;ID4;IL6ST;SKIL;FGFR1 -KEGG_2019_Human Prostate cancer 4/97 0.04344296444589789 0.3935421485098985 0 0 3.032140691328078 9.509722144863476 PDGFRB;PDGFRA;ZEB1;FGFR1 -KEGG_2019_Human Long-term depression 3/60 0.04829123688356071 0.4249628845753343 0 0 3.6764705882352944 11.141563109435227 GRIA2;GUCY1A1;PRKG1 -KEGG_2019_Human Adrenergic signaling in cardiomyocytes 5/145 0.04830280702957706 0.413257349030826 0 0 2.5354969574036508 7.683229217692497 PPP1CB;PLN;TPM2;CACNA2D1;TPM1 -KEGG_2019_Human Pathways in cancer 12/530 0.05944815698888802 0.4948657392588517 0 0 1.664816870144284 4.699196432886532 PDGFRB;ITGB1;PDGFRA;JAG1;COL4A2;ROCK1;EPAS1;LAMB2;MMP2;IL6ST;SKP1;FGFR1 -KEGG_2019_Human Rap1 signaling pathway 6/206 0.0629454020198865 0.5101890479506591 0 0 2.1416333523700746 5.9226604003579135 PDGFRB;ITGB1;PDGFRA;TLN1;PFN2;FGFR1 -KEGG_2019_Human D-Glutamine and D-glutamate metabolism 1/5 0.06618155629411791 0.5226645984253414 0 0 14.705882352941178 39.93166853950034 GLS -KEGG_2019_Human cAMP signaling pathway 6/212 0.070209829121254 0.5406156842336557 0 0 2.081021087680355 5.527747561971588 PPP1CB;GRIA2;PPP1R12A;PLN;ROCK1;MYL9 -KEGG_2019_Human Melanoma 3/72 0.07491587537354344 0.5627826735378385 0 0 3.063725490196078 7.9393059334104645 PDGFRB;PDGFRA;FGFR1 -KEGG_2019_Human Tyrosine metabolism 2/36 0.08586386087529987 0.6296683130855324 0 0 4.084967320261438 10.028563113135698 ADH1B;ADH5 -KEGG_2019_Human Cardiac muscle contraction 3/78 0.09019591084545324 0.6460544311720835 0 0 2.828054298642533 6.803651547649332 TPM2;CACNA2D1;TPM1 -KEGG_2019_Human Axon guidance 5/181 0.10145294164382992 0.7101705915068095 0 0 2.0311992200194995 4.647709248322914 ITGB1;ENAH;ROCK1;ILK;MYL9 -KEGG_2019_Human MAPK signaling pathway 7/295 0.10894023592569868 0.7456353925581153 0 0 1.7447657028913262 3.8680685168659816 PDGFRB;PDGFRA;MEF2C;CACNA2D1;FLNA;HSPB1;FGFR1 -KEGG_2019_Human Fatty acid degradation 2/44 0.1203076012637693 0.8055378519400205 0 0 3.3422459893048133 7.077885936155263 ADH1B;ADH5 -KEGG_2019_Human Small cell lung cancer 3/93 0.13331901412497088 0.8736650287338518 0 0 2.3719165085389 4.779436480958584 ITGB1;COL4A2;LAMB2 -KEGG_2019_Human Cell adhesion molecules (CAMs) 4/145 0.13592443578946695 0.8721817963157464 0 0 2.028397565922921 4.047984111267192 ITGB1;VCAN;ALCAM;ITGA8 -KEGG_2019_Human Circadian entrainment 3/97 0.14584412407234534 0.916734494169028 0 0 2.2741055184960586 4.3781463105314575 GRIA2;GUCY1A1;PRKG1 -KEGG_2019_Human Amyotrophic lateral sclerosis (ALS) 2/51 0.15270294406402393 0.9406501354343874 0 0 2.883506343713956 5.418860400492679 GRIA2;SOD1 -KEGG_2019_Human Parathyroid hormone synthesis, secretion and action 3/106 0.17531828927573342 1.0 0 0 2.081021087680355 3.623374364325077 MEF2A;MEF2C;FGFR1 -KEGG_2019_Human JAK-STAT signaling pathway 4/162 0.17954242332578133 1.0 0 0 1.8155410312273057 3.1179080561552 PDGFRB;PDGFRA;FHL1;IL6ST -KEGG_2019_Human Drug metabolism 3/108 0.18208248755349327 1.0 0 0 2.042483660130719 3.4789531582238045 ADH1B;FMO2;ADH5 -KEGG_2019_Human Protein processing in endoplasmic reticulum 4/165 0.1876850542876477 1.0 0 0 1.7825311942959003 2.9821567987378486 DNAJC10;CRYAB;SKP1;SEC31A -KEGG_2019_Human Viral myocarditis 2/59 0.19146176778668986 1.0 0 0 2.4925224327018944 4.120306920029571 CAV1;CD55 -KEGG_2019_Human Glutamatergic synapse 3/114 0.2027731706243024 1.0 0 0 1.9349845201238391 3.0875915456052483 GRIA2;SLC38A2;GLS -KEGG_2019_Human MicroRNAs in cancer 6/299 0.22316530994588504 1.0 0 0 1.475506590596105 2.2130274669637133 PDGFRB;PDGFRA;ZEB1;ROCK1;TPM1;GLS -KEGG_2019_Human Long-term potentiation 2/67 0.23132414856873426 1.0 0 0 2.1949078138718177 3.2132030542691368 PPP1CB;GRIA2 -KEGG_2019_Human Retinol metabolism 2/67 0.23132414856873426 1.0 0 0 2.1949078138718177 3.2132030542691368 ADH1B;ADH5 -KEGG_2019_Human Amphetamine addiction 2/68 0.2363510906537297 1.0 0 0 2.1626297577854667 3.119456979202147 PPP1CB;GRIA2 -KEGG_2019_Human Glycolysis / Gluconeogenesis 2/68 0.2363510906537297 1.0 0 0 2.1626297577854667 3.119456979202147 ADH1B;ADH5 -KEGG_2019_Human Renin secretion 2/69 0.2413839501392483 1.0 0 0 2.131287297527707 3.0293402794340234 GUCY1A1;AQP1 -KEGG_2019_Human Arginine biosynthesis 1/21 0.2500165320035174 1.0 0 0 3.5014005602240896 4.8537403196504965 GLS -KEGG_2019_Human Calcium signaling pathway 4/188 0.25349779726341404 1.0 0 0 1.5644555694618274 2.1470590508211016 PDGFRB;PDGFRA;PLN;MYLK -KEGG_2019_Human Relaxin signaling pathway 3/130 0.2601790295419775 1.0 0 0 1.6968325791855206 2.2845904577447924 ACTA2;COL4A2;MMP2 -KEGG_2019_Human Metabolism of xenobiotics by cytochrome P450 2/74 0.2665999687415824 1.0 0 0 1.987281399046105 2.627197910345616 ADH1B;ADH5 -KEGG_2019_Human PPAR signaling pathway 2/74 0.2665999687415824 1.0 0 0 1.987281399046105 2.627197910345616 ILK;SORBS1 -KEGG_2019_Human Glioma 2/75 0.2716464914501763 1.0 0 0 1.9607843137254903 2.555399454446498 PDGFRB;PDGFRA -KEGG_2019_Human Pertussis 2/76 0.2766919018489793 1.0 0 0 1.9349845201238391 2.4861661368358834 ITGB1;SERPING1 -KEGG_2019_Human Viral carcinogenesis 4/201 0.2926016121500216 1.0 0 0 1.4632718759145449 1.798278139699968 GSN;ACTN1;ACTN4;IL6ST -KEGG_2019_Human Chemical carcinogenesis 2/82 0.3068922933208021 1.0 0 0 1.793400286944046 2.1184692055970427 ADH1B;ADH5 -KEGG_2019_Human Biosynthesis of unsaturated fatty acids 1/27 0.3092389169797937 1.0 0 0 2.7233115468409586 3.196190377820267 HSD17B12 -KEGG_2019_Human Fatty acid elongation 1/27 0.3092389169797937 1.0 0 0 2.7233115468409586 3.196190377820267 HSD17B12 -KEGG_2019_Human GABAergic synapse 2/89 0.34180352222373084 1.0 0 0 1.6523463317911435 1.7738255178910634 SLC38A2;GLS -KEGG_2019_Human Circadian rhythm 1/31 0.3461080735716709 1.0 0 0 2.3719165085389 2.5166133807702407 SKP1 -KEGG_2019_Human Salivary secretion 2/90 0.3467493599469019 1.0 0 0 1.6339869281045754 1.7306422641375756 GUCY1A1;PRKG1 -KEGG_2019_Human Wnt signaling pathway 3/158 0.3639425768910762 1.0 0 0 1.3961280714817574 1.4111492641249497 SFRP4;TBL1XR1;SKP1 -KEGG_2019_Human Hematopoietic cell lineage 2/97 0.3809958594842488 1.0 0 0 1.516070345664039 1.4629575066857154 ITGA1;CD55 -KEGG_2019_Human Alanine, aspartate and glutamate metabolism 1/35 0.3810161954023504 1.0 0 0 2.100840336134454 2.0271289855906343 GLS -KEGG_2019_Human Prion diseases 1/35 0.3810161954023504 1.0 0 0 2.100840336134454 2.0271289855906343 SOD1 -KEGG_2019_Human Choline metabolism in cancer 2/99 0.3906445124515504 1.0 0 0 1.4854426619132504 1.3962526854769095 PDGFRB;PDGFRA -KEGG_2019_Human AGE-RAGE signaling pathway in diabetic complications 2/100 0.3954437670071447 1.0 0 0 1.4705882352941178 1.364333358695922 COL4A2;MMP2 -KEGG_2019_Human Longevity regulating pathway 2/102 0.4049899708664891 1.0 0 0 1.441753171856978 1.303190564410082 CRYAB;SOD1 -KEGG_2019_Human Nicotine addiction 1/40 0.4220514629710593 1.0 0 0 1.8382352941176472 1.5857132761269408 GRIA2 -KEGG_2019_Human Bladder cancer 1/41 0.4299272993468652 1.0 0 0 1.793400286944046 1.5138794043670196 MMP2 -KEGG_2019_Human Insulin resistance 2/108 0.43318186814686666 1.0 0 0 1.3616557734204793 1.1391579797765845 PPP1CB;OGT -KEGG_2019_Human Type I diabetes mellitus 1/43 0.4453596082378105 1.0 0 0 1.7099863201094392 1.3831621319427378 CPE -KEGG_2019_Human Toxoplasmosis 2/113 0.4561220632116787 1.0 0 0 1.3014055179593962 1.0215965939700191 ITGB1;LAMB2 -KEGG_2019_Human Transcriptional misregulation in cancer 3/186 0.4652988538826531 1.0 0 0 1.1859582542694498 0.9073474659794925 MEF2C;ZEB1;IGFBP3 -KEGG_2019_Human Thyroid hormone signaling pathway 2/116 0.4696282803180857 1.0 0 0 1.2677484787018256 0.9581817826307044 PLN;RCAN2 -KEGG_2019_Human Amino sugar and nucleotide sugar metabolism 1/48 0.4821435858580175 1.0 0 0 1.531862745098039 1.1175142667321398 CYB5R3 -KEGG_2019_Human Notch signaling pathway 1/48 0.4821435858580175 1.0 0 0 1.531862745098039 1.1175142667321398 JAG1 -KEGG_2019_Human Cocaine addiction 1/49 0.4892033606322606 1.0 0 0 1.5006002400960383 1.0728946661821388 GRIA2 -KEGG_2019_Human Vibrio cholerae infection 1/50 0.4961672405115914 1.0 0 0 1.4705882352941178 1.0306503391739699 TJP1 -KEGG_2019_Human Cholesterol metabolism 1/50 0.4961672405115914 1.0 0 0 1.4705882352941178 1.0306503391739699 SORT1 -KEGG_2019_Human N-Glycan biosynthesis 1/50 0.4961672405115914 1.0 0 0 1.4705882352941178 1.0306503391739699 MAN2A1 -KEGG_2019_Human Mineral absorption 1/51 0.5030365232259699 1.0 0 0 1.441753171856978 0.9906177922883744 CYBRD1 -KEGG_2019_Human Oocyte meiosis 2/125 0.5089154386376918 1.0 0 0 1.1764705882352942 0.7946745983242032 PPP1CB;SKP1 -KEGG_2019_Human Purine metabolism 2/129 0.5257584645947387 1.0 0 0 1.1399908800729597 0.7329153721972049 GUCY1A1;PDE5A -KEGG_2019_Human Regulation of lipolysis in adipocytes 1/55 0.5295930315435577 1.0 0 0 1.3368983957219251 0.8497946957319057 PRKG1 -KEGG_2019_Human Dopaminergic synapse 2/131 0.5340331611962786 1.0 0 0 1.122586439155815 0.7041954898198746 PPP1CB;GRIA2 -KEGG_2019_Human Systemic lupus erythematosus 2/133 0.5422088441197871 1.0 0 0 1.1057054400707649 0.6768067564191083 ACTN1;ACTN4 -KEGG_2019_Human Endometrial cancer 1/58 0.5485781831956474 1.0 0 0 1.2677484787018256 0.761188475423958 ILK -KEGG_2019_Human VEGF signaling pathway 1/59 0.5547353591513081 1.0 0 0 1.2462612163509472 0.7343770056249409 HSPB1 -KEGG_2019_Human Ubiquitin mediated proteolysis 2/137 0.5582604536697457 1.0 0 0 1.0734220695577499 0.6257295648902023 UBA2;SKP1 -KEGG_2019_Human Insulin signaling pathway 2/137 0.5582604536697457 1.0 0 0 1.0734220695577499 0.6257295648902023 PPP1CB;SORBS1 -KEGG_2019_Human Steroid hormone biosynthesis 1/60 0.5608088583395024 1.0 0 0 1.2254901960784317 0.7087930729027963 HSD17B12 -KEGG_2019_Human Arachidonic acid metabolism 1/63 0.5785385502430619 1.0 0 0 1.1671335200746966 0.6387139312464435 PTGIS -KEGG_2019_Human Cytosolic DNA-sensing pathway 1/63 0.5785385502430619 1.0 0 0 1.1671335200746966 0.6387139312464435 IL33 -KEGG_2019_Human Mitophagy 1/65 0.589960330158279 1.0 0 0 1.1312217194570138 0.5969456802746016 TOMM7 -KEGG_2019_Human Breast cancer 2/147 0.5966201002647564 1.0 0 0 1.0004001600640255 0.5166813886655346 JAG1;FGFR1 -KEGG_2019_Human Phospholipase D signaling pathway 2/148 0.6003161973829434 1.0 0 0 0.9936406995230526 0.507053623749191 PDGFRB;PDGFRA -KEGG_2019_Human Epithelial cell signaling in Helicobacter pylori infection 1/68 0.6065172977739856 1.0 0 0 1.0813148788927336 0.5406812611971594 TJP1 -KEGG_2019_Human Renal cell carcinoma 1/69 0.611886901684063 1.0 0 0 1.0656436487638534 0.5234524879768573 EPAS1 -KEGG_2019_Human Ras signaling pathway 3/232 0.6137683033988813 1.0 0 0 0.9508113590263692 0.4641269441679554 PDGFRB;PDGFRA;FGFR1 -KEGG_2019_Human Ribosome 2/153 0.6184148636939507 1.0 0 0 0.9611687812379854 0.4619336277106629 RPL35A;RPS23 -KEGG_2019_Human Bile secretion 1/72 0.6275615960808578 1.0 0 0 1.0212418300653594 0.47581030607501 AQP1 -KEGG_2019_Human p53 signaling pathway 1/72 0.6275615960808578 1.0 0 0 1.0212418300653594 0.47581030607501 IGFBP3 -KEGG_2019_Human Leishmaniasis 1/74 0.6376593528396856 1.0 0 0 0.9936406995230526 0.44708969390594056 ITGB1 -KEGG_2019_Human Gastric acid secretion 1/75 0.642605468326459 1.0 0 0 0.9803921568627452 0.43355325749716417 MYLK -KEGG_2019_Human Cellular senescence 2/160 0.6426878178425024 1.0 0 0 0.9191176470588236 0.4063384020594419 PPP1CB;IGFBP3 -KEGG_2019_Human Endocytosis 3/244 0.6476602989696862 1.0 0 0 0.9040501446480232 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0.20938300884200392 SBDS -KEGG_2019_Human Cytokine-cytokine receptor interaction 3/294 0.7658244651942859 1.0 0 0 0.7503001200480192 0.2001817926260468 IL33;TNFRSF11B;IL6ST -KEGG_2019_Human Th17 cell differentiation 1/107 0.769868615791715 1.0 0 0 0.6871907641561297 0.17972471655792546 IL6ST -KEGG_2019_Human TNF signaling pathway 1/110 0.7791803947062851 1.0 0 0 0.6684491978609626 0.16678655599481193 JAG1 -KEGG_2019_Human Serotonergic synapse 1/113 0.7881167181818356 1.0 0 0 0.6507027589796981 0.15493823566682394 APP -KEGG_2019_Human Sphingolipid signaling pathway 1/119 0.8049227342907571 1.0 0 0 0.6178942165101334 0.13408859887399344 ROCK1 -KEGG_2019_Human Neurotrophin signaling pathway 1/119 0.8049227342907571 1.0 0 0 0.6178942165101334 0.13408859887399344 SORT1 -KEGG_2019_Human Human cytomegalovirus infection 2/225 0.8133138001460511 1.0 0 0 0.6535947712418301 0.1350576901080161 PDGFRA;ROCK1 -KEGG_2019_Human Lysosome 1/123 0.8153820476764102 1.0 0 0 0.5978000956480154 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1/160 0.8891675678668584 1.0 0 0 0.4595588235294118 0.05398417782231872 PPP1CB -KEGG_2019_Human Necroptosis 1/162 0.8921857150883906 1.0 0 0 0.4538852578068264 0.051779669254340105 IL33 -KEGG_2019_Human Hepatitis B 1/163 0.8936639375626885 1.0 0 0 0.4511006856730422 0.05071521257966539 HSPG2 -KEGG_2019_Human Alzheimer disease 1/171 0.904785339493249 1.0 0 0 0.4299965600275198 0.04302440546407457 APP -KEGG_2019_Human Influenza A 1/171 0.904785339493249 1.0 0 0 0.4299965600275198 0.04302440546407457 IL33 -KEGG_2019_Human NOD-like receptor signaling pathway 1/178 0.9135612891775724 1.0 0 0 0.4130865829477859 0.0373450151680992 ANTXR1 -KEGG_2019_Human Alcoholism 1/180 0.9159172646241148 1.0 0 0 0.40849673202614384 0.03587795786359597 PPP1CB -KEGG_2019_Human Kaposi sarcoma-associated herpesvirus infection 1/186 0.9226081510752836 1.0 0 0 0.39531941808981663 0.03184324517757378 IL6ST -KEGG_2019_Human Chemokine signaling pathway 1/190 0.9267712817794176 1.0 0 0 0.38699690402476783 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zf4by!eM);O8!sOx%reO?=d0HLyF8eZ^54rdfvOZLdBvG_?Qajv0{p9uaxha%qo8G?TgnVJ6on@azG{-in>tdBoqN=}CPJp0)k OG}JTEEx&sE?*9QeYA3z` diff --git a/session_topology/lab/assests/Enrichr/OMIM_Disease.human.enrichr.reports.txt b/session_topology/lab/assests/Enrichr/OMIM_Disease.human.enrichr.reports.txt deleted file mode 100644 index 9d4cc690..00000000 --- a/session_topology/lab/assests/Enrichr/OMIM_Disease.human.enrichr.reports.txt +++ /dev/null @@ -1,27 +0,0 @@ -Gene_set Term Overlap P-value Adjusted P-value Old P-value Old Adjusted P-value Odds Ratio Combined Score Genes -OMIM_Disease blood 4/36 0.0013995103571758866 0.1259559321458298 0 0 8.169934640522875 53.68981085302527 RGS5;SLC14A1;CD55;AQP1 -OMIM_Disease macular degeneration 3/18 0.0017450224885294321 0.07852601198382445 0 0 12.254901960784313 77.83073328433055 HTRA1;HMCN1;FBLN5 -OMIM_Disease myopathy 4/44 0.002962716383514205 0.08888149150542614 0 0 6.6844919786096275 38.9147642669757 TPM2;FHL1;COL6A1;CRYAB -OMIM_Disease alzheimer disease 3/26 0.005131974899755907 0.11546943524450792 0 0 8.484162895927602 44.7307527418186 APP;APBB2;A2M -OMIM_Disease anemia 4/61 0.009519000335164543 0.1713420060329618 0 0 4.821600771456124 22.44197416828791 HBB;RPL35A;HBA2;HBA1 -OMIM_Disease cardiomyopathy, dilated 3/33 0.010043658740843656 0.15065488111265485 0 0 6.6844919786096275 30.75410303932754 PLN;TPM1;VCL -OMIM_Disease cardiomyopathy 3/42 0.019329898502524718 0.2485272664610321 0 0 5.2521008403361344 20.725326872638654 PLN;TPM1;VCL -OMIM_Disease aneurysm 2/18 0.024427820184259687 0.27481297707292146 0 0 8.169934640522875 30.32706392727768 ACTA2;MYH11 -OMIM_Disease hypertension 2/29 0.05882299169862056 0.5882299169862056 0 0 5.0709939148073016 14.367253981853178 PTGIS;ADD1 -OMIM_Disease pancreatic cancer 1/11 0.1398634329517421 1.0 0 0 6.6844919786096275 13.148989380870011 PALLD -OMIM_Disease dementia 1/12 0.15156771036528016 1.0 0 0 6.127450980392156 11.560801597876258 APP -OMIM_Disease anomalies 1/13 0.1631133000996947 1.0 0 0 5.656108597285066 10.256279564503453 FOXC1 -OMIM_Disease corneal dystrophy 1/14 0.17450234575219342 1.0 0 0 5.2521008403361344 9.169207430273257 ZEB1 -OMIM_Disease hypogonadism 1/15 0.185736962021256 1.0 0 0 4.901960784313727 8.252077396325646 FGFR1 -OMIM_Disease glaucoma 1/16 0.19681923528001455 1.0 0 0 4.595588235294118 7.469988781692437 FOXC1 -OMIM_Disease cardiomyopathy, hypertrophic 1/17 0.20775122366844606 1.0 0 0 4.3252595155709335 6.796773162470168 TPM1 -OMIM_Disease lateral sclerosis 1/19 0.2291724408383681 1.0 0 0 3.8699690402476783 5.701550085299294 SOD1 -OMIM_Disease leukemia 2/78 0.28677581014083325 1.0 0 0 1.885369532428356 2.354929332227962 PDGFRB;LPP -OMIM_Disease charcot-marie-tooth disease 1/27 0.3092389169797937 1.0 0 0 2.7233115468409586 3.196190377820267 HSPB1 -OMIM_Disease muscular dystrophy 1/33 0.3638006353842425 1.0 0 0 2.2281639928698755 2.2530063867622725 COL6A1 -OMIM_Disease neuropathy 1/35 0.3810161954023504 1.0 0 0 2.100840336134454 2.0271289855906343 HSPB1 -OMIM_Disease spinocerebellar ataxia 1/37 0.3977675676481275 1.0 0 0 1.987281399046105 1.8320497718138693 SYNE1 -OMIM_Disease deafness 2/111 0.4470091141530259 1.0 0 0 1.3248542660307367 1.0667412493009387 JAG1;MYH9 -OMIM_Disease cataract 1/47 0.4749866005080773 1.0 0 0 1.5644555694618274 1.1646881802285192 CRYAB -OMIM_Disease ataxia 1/60 0.5608088583395024 1.0 0 0 1.2254901960784317 0.7087930729027963 SYNE1 -OMIM_Disease mental retardation 1/114 0.7910146830110117 1.0 0 0 0.6449948400412797 0.15121178327775464 ATRX diff --git a/session_topology/lab/assests/Enrichr/gseapy.enrichr..log b/session_topology/lab/assests/Enrichr/gseapy.enrichr..log deleted file mode 100644 index a72a54f0..00000000 --- a/session_topology/lab/assests/Enrichr/gseapy.enrichr..log +++ /dev/null @@ -1,1959 +0,0 @@ -LINE 226 : 2020-10-01 18:27:15,648 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:27:15,864 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:27:15,868 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:27:16,338 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:27:16,453 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:27:16,453 [DEBUG ] Start Enrichr using library: GO_Biological_Process_2018 -LINE 370 : 2020-10-01 18:27:16,453 [INFO ] Analysis name: , Enrichr Library: GO_Biological_Process_2018 -LINE 226 : 2020-10-01 18:27:16,457 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:27:16,674 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:27:16,678 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:27:19,245 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:27:20,254 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:27:20,470 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210182&filename=GO_Biological_Process_2018..reports&backgroundType=GO_Biological_Process_2018 HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:27:20,475 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:27:21,893 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210182&filename=GO_Biological_Process_2018..reports&backgroundType=GO_Biological_Process_2018 HTTP/1.1" 200 None -LINE 378 : 2020-10-01 18:27:22,468 [INFO ] Save file of enrichment results: Job Id:b1151e731e474f5fd2801afcf8062dd7 -LINE 387 : 2020-10-01 18:27:22,499 [WARNING ] Warning: No enrich terms using library GO_Biological_Process_2018 when cutoff = 0.05 -LINE 388 : 2020-10-01 18:27:22,500 [INFO ] Done. - -LINE 226 : 2020-10-01 18:27:22,507 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:27:22,723 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:27:22,729 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:27:23,198 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:27:23,313 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:27:23,314 [DEBUG ] Start Enrichr using library: KEGG_2019_Human -LINE 370 : 2020-10-01 18:27:23,314 [INFO ] Analysis name: , Enrichr Library: KEGG_2019_Human -LINE 226 : 2020-10-01 18:27:23,317 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:27:23,533 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:27:23,539 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:27:25,207 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:27:26,217 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:27:26,433 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210183&filename=KEGG_2019_Human..reports&backgroundType=KEGG_2019_Human HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:27:26,439 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:27:26,928 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210183&filename=KEGG_2019_Human..reports&backgroundType=KEGG_2019_Human HTTP/1.1" 200 None -LINE 378 : 2020-10-01 18:27:27,046 [INFO ] Save file of enrichment results: Job Id:e120d373841dac2ff70265a5bbaf602e -LINE 387 : 2020-10-01 18:27:27,051 [WARNING ] Warning: No enrich terms using library KEGG_2019_Human when cutoff = 0.05 -LINE 388 : 2020-10-01 18:27:27,052 [INFO ] Done. - -LINE 226 : 2020-10-01 18:27:27,061 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:27:27,277 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:27:27,282 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:27:27,752 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:27:27,866 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:27:27,867 [DEBUG ] Start Enrichr using library: OMIM_Disease -LINE 370 : 2020-10-01 18:27:27,867 [INFO ] Analysis name: , Enrichr Library: OMIM_Disease -LINE 226 : 2020-10-01 18:27:27,870 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:27:28,086 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:27:28,091 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:27:29,771 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:27:30,782 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:27:30,998 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210184&filename=OMIM_Disease..reports&backgroundType=OMIM_Disease HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:27:31,005 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:27:31,513 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210184&filename=OMIM_Disease..reports&backgroundType=OMIM_Disease HTTP/1.1" 200 4629 -LINE 378 : 2020-10-01 18:27:31,520 [INFO ] Save file of enrichment results: Job Id:b275b5918fa42f7e9a134c3845e069dc -LINE 387 : 2020-10-01 18:27:31,523 [WARNING ] Warning: No enrich terms using library OMIM_Disease when cutoff = 0.05 -LINE 388 : 2020-10-01 18:27:31,524 [INFO ] Done. - -LINE 226 : 2020-10-01 18:27:31,534 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:27:31,751 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:27:31,756 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:27:32,226 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:27:32,340 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:27:32,341 [DEBUG ] Start Enrichr using library: GO_Biological_Process_2018 -LINE 370 : 2020-10-01 18:27:32,341 [INFO ] Analysis name: , Enrichr Library: GO_Biological_Process_2018 -LINE 226 : 2020-10-01 18:27:32,344 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:27:32,561 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:27:32,566 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:27:34,232 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:27:35,239 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:27:35,455 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210185&filename=GO_Biological_Process_2018..reports&backgroundType=GO_Biological_Process_2018 HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:27:35,458 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:27:36,458 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210185&filename=GO_Biological_Process_2018..reports&backgroundType=GO_Biological_Process_2018 HTTP/1.1" 200 None -LINE 378 : 2020-10-01 18:27:37,018 [INFO ] Save file of enrichment results: Job Id:fc3500185c80b8c1172f16c3355d68c5 -LINE 1334: 2020-10-01 18:27:37,114 [DEBUG ] findfont: Matching sans\-serif:style=normal:variant=normal:weight=bold:stretch=normal:size=24.0. -LINE 1346: 2020-10-01 18:27:37,151 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,152 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,152 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,152 [DEBUG ] findfont: score() = 0.33499999999999996 -LINE 1346: 2020-10-01 18:27:37,152 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,152 [DEBUG ] findfont: score() = 0.05 -LINE 1346: 2020-10-01 18:27:37,152 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,152 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,152 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,152 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,152 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,152 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,152 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,152 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,152 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,152 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,152 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,152 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,153 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,153 [DEBUG ] findfont: score() = 1.335 -LINE 1346: 2020-10-01 18:27:37,153 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,153 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,153 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,153 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,153 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,153 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,153 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,153 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,153 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,153 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,153 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,153 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,153 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,153 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,153 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,153 [DEBUG ] findfont: score() = 1.05 -LINE 1346: 2020-10-01 18:27:37,153 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,154 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,154 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,154 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,154 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,154 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,154 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,154 [DEBUG ] findfont: score() = 10.535 -LINE 1346: 2020-10-01 18:27:37,154 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,154 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,154 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,154 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,154 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,154 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,154 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,154 [DEBUG ] findfont: score() = 10.44 -LINE 1346: 2020-10-01 18:27:37,154 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,154 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,154 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,154 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,154 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,154 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,155 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,155 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,155 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,155 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,155 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,155 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,155 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,155 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,155 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,155 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,155 [DEBUG ] findfont: score() = 11.535 -LINE 1346: 2020-10-01 18:27:37,155 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,155 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,155 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,155 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,155 [DEBUG ] findfont: score() = 10.145 -LINE 1346: 2020-10-01 18:27:37,155 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,155 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,155 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,155 [DEBUG ] findfont: score() = 10.145 -LINE 1346: 2020-10-01 18:27:37,155 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,156 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,156 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,156 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,156 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,156 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,156 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,156 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,156 [DEBUG ] findfont: score() = 10.44 -LINE 1346: 2020-10-01 18:27:37,156 [DEBUG ] findfont: score() = 7.607727272727273 -LINE 1346: 2020-10-01 18:27:37,156 [DEBUG ] findfont: score() = 7.698636363636363 -LINE 1346: 2020-10-01 18:27:37,156 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,156 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,156 [DEBUG ] findfont: score() = 10.145 -LINE 1346: 2020-10-01 18:27:37,156 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,156 [DEBUG ] findfont: score() = 10.43 -LINE 1346: 2020-10-01 18:27:37,156 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,156 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,156 [DEBUG ] findfont: score() = 11.525 -LINE 1346: 2020-10-01 18:27:37,156 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,156 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,156 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,157 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,157 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,157 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,157 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,157 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,157 [DEBUG ] findfont: score() = 10.344999999999999 -LINE 1346: 2020-10-01 18:27:37,157 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,157 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,157 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,157 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,157 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,157 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,157 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,157 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,157 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,157 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,157 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,157 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,157 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,157 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,157 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,158 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,158 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,158 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,158 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,158 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,158 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,158 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,158 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,158 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,158 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,158 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,158 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,158 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,158 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,158 [DEBUG ] findfont: score() = 10.43 -LINE 1346: 2020-10-01 18:27:37,158 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,158 [DEBUG ] findfont: score() = 11.43 -LINE 1346: 2020-10-01 18:27:37,158 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,158 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,158 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,158 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,158 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,159 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,159 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,159 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,159 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,159 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,159 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,159 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,159 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,159 [DEBUG ] findfont: score() = 10.44 -LINE 1346: 2020-10-01 18:27:37,159 [DEBUG ] findfont: score() = 10.62 -LINE 1346: 2020-10-01 18:27:37,159 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,159 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,159 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,159 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,159 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,159 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,159 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,159 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,159 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,159 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,160 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,160 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,160 [DEBUG ] findfont: score() = 10.25 -LINE 1346: 2020-10-01 18:27:37,160 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,160 [DEBUG ] findfont: score() = 10.25 -LINE 1346: 2020-10-01 18:27:37,160 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,160 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,160 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,160 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,160 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,160 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,160 [DEBUG ] findfont: score() = 11.24 -LINE 1346: 2020-10-01 18:27:37,160 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,160 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,160 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,160 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,160 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,160 [DEBUG ] findfont: score() = 10.344999999999999 -LINE 1346: 2020-10-01 18:27:37,160 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,160 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,161 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,161 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,161 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,161 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,161 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,161 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,161 [DEBUG ] findfont: score() = 11.24 -LINE 1346: 2020-10-01 18:27:37,161 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,161 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,161 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,161 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,161 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,161 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,161 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,161 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,161 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,161 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,161 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,161 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,161 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,161 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,162 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,162 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,162 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,162 [DEBUG ] findfont: score() = 10.71025 -LINE 1346: 2020-10-01 18:27:37,162 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,162 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,162 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,162 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,162 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,162 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,162 [DEBUG ] findfont: score() = 10.43 -LINE 1346: 2020-10-01 18:27:37,162 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,162 [DEBUG ] findfont: score() = 10.62 -LINE 1346: 2020-10-01 18:27:37,162 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,162 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,162 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,162 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,162 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,162 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,162 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,162 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,162 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,163 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,163 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,163 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,163 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,163 [DEBUG ] findfont: score() = 10.344999999999999 -LINE 1346: 2020-10-01 18:27:37,163 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,163 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,163 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,163 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,163 [DEBUG ] findfont: score() = 10.31125 -LINE 1346: 2020-10-01 18:27:37,163 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,163 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,163 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,163 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,163 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,163 [DEBUG ] findfont: score() = 10.535 -LINE 1346: 2020-10-01 18:27:37,163 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,163 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,163 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,163 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,163 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,164 [DEBUG ] findfont: score() = 10.43 -LINE 1346: 2020-10-01 18:27:37,164 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,164 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,164 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,164 [DEBUG ] findfont: score() = 10.344999999999999 -LINE 1346: 2020-10-01 18:27:37,164 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,164 [DEBUG ] findfont: score() = 10.43 -LINE 1346: 2020-10-01 18:27:37,164 [DEBUG ] findfont: score() = 10.43 -LINE 1346: 2020-10-01 18:27:37,164 [DEBUG ] findfont: score() = 10.525 -LINE 1346: 2020-10-01 18:27:37,164 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,164 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,164 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,164 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,164 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,164 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,164 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,164 [DEBUG ] findfont: score() = 10.43 -LINE 1346: 2020-10-01 18:27:37,164 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,164 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,164 [DEBUG ] findfont: score() = 10.4775 -LINE 1346: 2020-10-01 18:27:37,164 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,165 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,165 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,165 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,165 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,165 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,165 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,165 [DEBUG ] findfont: score() = 10.715 -LINE 1346: 2020-10-01 18:27:37,165 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,165 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,165 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,165 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,165 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,165 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,165 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,165 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,165 [DEBUG ] findfont: score() = 3.6863636363636365 -LINE 1346: 2020-10-01 18:27:37,165 [DEBUG ] findfont: score() = 10.535 -LINE 1346: 2020-10-01 18:27:37,165 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,165 [DEBUG ] findfont: score() = 6.413636363636363 -LINE 1346: 2020-10-01 18:27:37,165 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,165 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,166 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,166 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,166 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,166 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,166 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,166 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,166 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,166 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,166 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,166 [DEBUG ] findfont: score() = 4.6863636363636365 -LINE 1346: 2020-10-01 18:27:37,166 [DEBUG ] findfont: score() = 10.25 -LINE 1346: 2020-10-01 18:27:37,166 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,166 [DEBUG ] findfont: score() = 4.971363636363637 -LINE 1346: 2020-10-01 18:27:37,166 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,166 [DEBUG ] findfont: score() = 11.43 -LINE 1346: 2020-10-01 18:27:37,166 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,166 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,166 [DEBUG ] findfont: score() = 2.967272727272727 -LINE 1346: 2020-10-01 18:27:37,166 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,166 [DEBUG ] findfont: score() = 6.698636363636363 -LINE 1346: 2020-10-01 18:27:37,166 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,167 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,167 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,167 [DEBUG ] findfont: score() = 10.535 -LINE 1346: 2020-10-01 18:27:37,167 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,167 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,167 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,167 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,167 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,167 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,167 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,167 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,167 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,167 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,167 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,167 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,167 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,167 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,167 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,167 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,167 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,167 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,168 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,168 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,168 [DEBUG ] findfont: score() = 3.9713636363636367 -LINE 1346: 2020-10-01 18:27:37,168 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,168 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,168 [DEBUG ] findfont: score() = 10.25 -LINE 1346: 2020-10-01 18:27:37,168 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,168 [DEBUG ] findfont: score() = 10.525 -LINE 1346: 2020-10-01 18:27:37,168 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,168 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,168 [DEBUG ] findfont: score() = 11.24 -LINE 1346: 2020-10-01 18:27:37,168 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,168 [DEBUG ] findfont: score() = 11.31125 -LINE 1346: 2020-10-01 18:27:37,168 [DEBUG ] findfont: score() = 11.43 -LINE 1346: 2020-10-01 18:27:37,168 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,168 [DEBUG ] findfont: score() = 10.629999999999999 -LINE 1346: 2020-10-01 18:27:37,168 [DEBUG ] findfont: score() = 11.145 -LINE 1346: 2020-10-01 18:27:37,168 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,168 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,168 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,169 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,169 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,169 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,169 [DEBUG ] findfont: score() = 10.535 -LINE 1346: 2020-10-01 18:27:37,169 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,169 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,169 [DEBUG ] findfont: score() = 10.344999999999999 -LINE 1346: 2020-10-01 18:27:37,169 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,169 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,169 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,169 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,169 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,169 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,169 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,169 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,169 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,169 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,169 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,169 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,169 [DEBUG ] findfont: score() = 10.25 -LINE 1346: 2020-10-01 18:27:37,169 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,169 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,170 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,170 [DEBUG ] findfont: score() = 10.344999999999999 -LINE 1346: 2020-10-01 18:27:37,170 [DEBUG ] findfont: score() = 10.535 -LINE 1346: 2020-10-01 18:27:37,170 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,170 [DEBUG ] findfont: score() = 7.413636363636363 -LINE 1346: 2020-10-01 18:27:37,170 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,170 [DEBUG ] findfont: score() = 0.33499999999999996 -LINE 1346: 2020-10-01 18:27:37,170 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,170 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,170 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,170 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,170 [DEBUG ] findfont: score() = 10.4775 -LINE 1346: 2020-10-01 18:27:37,170 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,170 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,170 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,170 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,170 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,170 [DEBUG ] findfont: score() = 11.25 -LINE 1346: 2020-10-01 18:27:37,170 [DEBUG ] findfont: score() = 10.43 -LINE 1346: 2020-10-01 18:27:37,170 [DEBUG ] findfont: score() = 10.43 -LINE 1366: 2020-10-01 18:27:37,170 [DEBUG ] findfont: Matching sans\-serif:style=normal:variant=normal:weight=bold:stretch=normal:size=24.0 to DejaVu Sans ('/Users/rui.benfeitas/miniconda3/envs/merged1/lib/python3.8/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Bold.ttf') with score of 0.050000. -LINE 1334: 2020-10-01 18:27:37,179 [DEBUG ] findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=16.0. -LINE 1346: 2020-10-01 18:27:37,179 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,179 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,179 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,179 [DEBUG ] findfont: score() = 0.05 -LINE 1346: 2020-10-01 18:27:37,179 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,179 [DEBUG ] findfont: score() = 0.33499999999999996 -LINE 1346: 2020-10-01 18:27:37,179 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,179 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,179 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,179 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,179 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,179 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,179 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,179 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,180 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,180 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,180 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,180 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,180 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,180 [DEBUG ] findfont: score() = 1.05 -LINE 1346: 2020-10-01 18:27:37,180 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,180 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,180 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,180 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,180 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,180 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,180 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,180 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,180 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,180 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,180 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,180 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,180 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,180 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,180 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,180 [DEBUG ] findfont: score() = 1.335 -LINE 1346: 2020-10-01 18:27:37,181 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,181 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,181 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,181 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,181 [DEBUG ] findfont: score() = 10.145 -LINE 1346: 2020-10-01 18:27:37,181 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,181 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,181 [DEBUG ] findfont: score() = 10.25 -LINE 1346: 2020-10-01 18:27:37,181 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,181 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,181 [DEBUG ] findfont: score() = 10.145 -LINE 1346: 2020-10-01 18:27:37,181 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,181 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,181 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,181 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,181 [DEBUG ] findfont: score() = 10.725 -LINE 1346: 2020-10-01 18:27:37,181 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,181 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,181 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,181 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,181 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,181 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,182 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,182 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,182 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,182 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,182 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,182 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,182 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,182 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,182 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,182 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,182 [DEBUG ] findfont: score() = 11.25 -LINE 1346: 2020-10-01 18:27:37,182 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,182 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,182 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,182 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,182 [DEBUG ] findfont: score() = 10.43 -LINE 1346: 2020-10-01 18:27:37,182 [DEBUG ] findfont: score() = 10.525 -LINE 1346: 2020-10-01 18:27:37,182 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,182 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,183 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,183 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,183 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,183 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,183 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,183 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,183 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,183 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,183 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,183 [DEBUG ] findfont: score() = 10.344999999999999 -LINE 1346: 2020-10-01 18:27:37,183 [DEBUG ] findfont: score() = 7.322727272727273 -LINE 1346: 2020-10-01 18:27:37,183 [DEBUG ] findfont: score() = 7.413636363636363 -LINE 1346: 2020-10-01 18:27:37,183 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,183 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,183 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,183 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,183 [DEBUG ] findfont: score() = 10.145 -LINE 1346: 2020-10-01 18:27:37,183 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,183 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,183 [DEBUG ] findfont: score() = 11.24 -LINE 1346: 2020-10-01 18:27:37,183 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,183 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,184 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,184 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,184 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,184 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,184 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,184 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,184 [DEBUG ] findfont: score() = 10.629999999999999 -LINE 1346: 2020-10-01 18:27:37,184 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,184 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,184 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,184 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,184 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,184 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,184 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,184 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,184 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,184 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,184 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,184 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,226 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,226 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,227 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,227 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,227 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,227 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,227 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,227 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,227 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,227 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,227 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,227 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,227 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,227 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,227 [DEBUG ] findfont: score() = 10.145 -LINE 1346: 2020-10-01 18:27:37,227 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,227 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,227 [DEBUG ] findfont: score() = 10.145 -LINE 1346: 2020-10-01 18:27:37,227 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,227 [DEBUG ] findfont: score() = 11.145 -LINE 1346: 2020-10-01 18:27:37,227 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,228 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,228 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,228 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,228 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,228 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,228 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,228 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,228 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,228 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,228 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,228 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,228 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,228 [DEBUG ] findfont: score() = 10.344999999999999 -LINE 1346: 2020-10-01 18:27:37,228 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,228 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,228 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,228 [DEBUG ] findfont: score() = 10.145 -LINE 1346: 2020-10-01 18:27:37,228 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,228 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,229 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,229 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,229 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,229 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,229 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,229 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,229 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,229 [DEBUG ] findfont: score() = 10.535 -LINE 1346: 2020-10-01 18:27:37,229 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,229 [DEBUG ] findfont: score() = 10.535 -LINE 1346: 2020-10-01 18:27:37,229 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,229 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,229 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,229 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,229 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,229 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,229 [DEBUG ] findfont: score() = 11.525 -LINE 1346: 2020-10-01 18:27:37,229 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,229 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,229 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,230 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,230 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,230 [DEBUG ] findfont: score() = 10.629999999999999 -LINE 1346: 2020-10-01 18:27:37,230 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,230 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,230 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,230 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,230 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,230 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,230 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,230 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,230 [DEBUG ] findfont: score() = 11.145 -LINE 1346: 2020-10-01 18:27:37,230 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,230 [DEBUG ] findfont: score() = 10.145 -LINE 1346: 2020-10-01 18:27:37,230 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,230 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,230 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,230 [DEBUG ] findfont: score() = 10.145 -LINE 1346: 2020-10-01 18:27:37,230 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,230 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,231 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,231 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,231 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,231 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,231 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,231 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,231 [DEBUG ] findfont: score() = 10.525 -LINE 1346: 2020-10-01 18:27:37,231 [DEBUG ] findfont: score() = 10.525 -LINE 1346: 2020-10-01 18:27:37,231 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,231 [DEBUG ] findfont: score() = 10.42525 -LINE 1346: 2020-10-01 18:27:37,231 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,231 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,231 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,231 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,231 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,231 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,231 [DEBUG ] findfont: score() = 10.145 -LINE 1346: 2020-10-01 18:27:37,231 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,231 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,231 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,232 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,232 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,232 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,232 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,232 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,232 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,232 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,232 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,232 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,232 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,232 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,232 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,232 [DEBUG ] findfont: score() = 10.44 -LINE 1346: 2020-10-01 18:27:37,232 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,232 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,232 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,232 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,232 [DEBUG ] findfont: score() = 10.07375 -LINE 1346: 2020-10-01 18:27:37,232 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,232 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,232 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,233 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,233 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,233 [DEBUG ] findfont: score() = 10.25 -LINE 1346: 2020-10-01 18:27:37,233 [DEBUG ] findfont: score() = 10.145 -LINE 1346: 2020-10-01 18:27:37,233 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,233 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,233 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,233 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,233 [DEBUG ] findfont: score() = 10.145 -LINE 1346: 2020-10-01 18:27:37,233 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,233 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,233 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,233 [DEBUG ] findfont: score() = 10.44 -LINE 1346: 2020-10-01 18:27:37,233 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,233 [DEBUG ] findfont: score() = 10.145 -LINE 1346: 2020-10-01 18:27:37,233 [DEBUG ] findfont: score() = 10.145 -LINE 1346: 2020-10-01 18:27:37,233 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,233 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,233 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,233 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,234 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,234 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,234 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,234 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,234 [DEBUG ] findfont: score() = 10.145 -LINE 1346: 2020-10-01 18:27:37,234 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,234 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,234 [DEBUG ] findfont: score() = 10.1925 -LINE 1346: 2020-10-01 18:27:37,234 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,234 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,234 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,234 [DEBUG ] findfont: score() = 10.145 -LINE 1346: 2020-10-01 18:27:37,234 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,234 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,234 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,234 [DEBUG ] findfont: score() = 10.43 -LINE 1346: 2020-10-01 18:27:37,234 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,234 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,234 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,234 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,235 [DEBUG ] findfont: score() = 10.525 -LINE 1346: 2020-10-01 18:27:37,235 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,235 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,235 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,235 [DEBUG ] findfont: score() = 3.9713636363636367 -LINE 1346: 2020-10-01 18:27:37,235 [DEBUG ] findfont: score() = 10.25 -LINE 1346: 2020-10-01 18:27:37,235 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,235 [DEBUG ] findfont: score() = 6.698636363636363 -LINE 1346: 2020-10-01 18:27:37,235 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,235 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,235 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,235 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,235 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,235 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,235 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,235 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,235 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,235 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,235 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,235 [DEBUG ] findfont: score() = 4.971363636363637 -LINE 1346: 2020-10-01 18:27:37,236 [DEBUG ] findfont: score() = 10.535 -LINE 1346: 2020-10-01 18:27:37,236 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,236 [DEBUG ] findfont: score() = 4.6863636363636365 -LINE 1346: 2020-10-01 18:27:37,236 [DEBUG ] findfont: score() = 10.525 -LINE 1346: 2020-10-01 18:27:37,236 [DEBUG ] findfont: score() = 11.145 -LINE 1346: 2020-10-01 18:27:37,236 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,236 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,236 [DEBUG ] findfont: score() = 2.872272727272727 -LINE 1346: 2020-10-01 18:27:37,236 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,236 [DEBUG ] findfont: score() = 6.413636363636363 -LINE 1346: 2020-10-01 18:27:37,236 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,236 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,236 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,236 [DEBUG ] findfont: score() = 10.25 -LINE 1346: 2020-10-01 18:27:37,236 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,236 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,236 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,236 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,237 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,237 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,237 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,237 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,237 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,237 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,237 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,237 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,237 [DEBUG ] findfont: score() = 10.525 -LINE 1346: 2020-10-01 18:27:37,237 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,237 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,237 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,237 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,237 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,237 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,237 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,237 [DEBUG ] findfont: score() = 3.6863636363636365 -LINE 1346: 2020-10-01 18:27:37,237 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,237 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,238 [DEBUG ] findfont: score() = 10.535 -LINE 1346: 2020-10-01 18:27:37,238 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,238 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,238 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,238 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,238 [DEBUG ] findfont: score() = 11.145 -LINE 1346: 2020-10-01 18:27:37,238 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,238 [DEBUG ] findfont: score() = 11.07375 -LINE 1346: 2020-10-01 18:27:37,238 [DEBUG ] findfont: score() = 11.145 -LINE 1346: 2020-10-01 18:27:37,238 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,238 [DEBUG ] findfont: score() = 10.344999999999999 -LINE 1346: 2020-10-01 18:27:37,238 [DEBUG ] findfont: score() = 11.24 -LINE 1346: 2020-10-01 18:27:37,238 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,238 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,238 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,238 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,238 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,238 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,238 [DEBUG ] findfont: score() = 10.25 -LINE 1346: 2020-10-01 18:27:37,238 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,239 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,239 [DEBUG ] findfont: score() = 10.44 -LINE 1346: 2020-10-01 18:27:37,239 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,239 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,239 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,239 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,239 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,239 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,239 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,239 [DEBUG ] findfont: score() = 10.525 -LINE 1346: 2020-10-01 18:27:37,239 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,239 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,239 [DEBUG ] findfont: score() = 10.525 -LINE 1346: 2020-10-01 18:27:37,239 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,239 [DEBUG ] findfont: score() = 10.535 -LINE 1346: 2020-10-01 18:27:37,239 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,239 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,239 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,239 [DEBUG ] findfont: score() = 10.629999999999999 -LINE 1346: 2020-10-01 18:27:37,239 [DEBUG ] findfont: score() = 10.25 -LINE 1346: 2020-10-01 18:27:37,239 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,240 [DEBUG ] findfont: score() = 7.698636363636363 -LINE 1346: 2020-10-01 18:27:37,240 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,240 [DEBUG ] findfont: score() = 0.05 -LINE 1346: 2020-10-01 18:27:37,240 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,240 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,240 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,240 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,240 [DEBUG ] findfont: score() = 10.1925 -LINE 1346: 2020-10-01 18:27:37,240 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,240 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,240 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,240 [DEBUG ] findfont: score() = 10.525 -LINE 1346: 2020-10-01 18:27:37,240 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,240 [DEBUG ] findfont: score() = 11.535 -LINE 1346: 2020-10-01 18:27:37,240 [DEBUG ] findfont: score() = 10.145 -LINE 1346: 2020-10-01 18:27:37,240 [DEBUG ] findfont: score() = 10.145 -LINE 1366: 2020-10-01 18:27:37,240 [DEBUG ] findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=16.0 to DejaVu Sans ('/Users/rui.benfeitas/miniconda3/envs/merged1/lib/python3.8/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000. -LINE 1334: 2020-10-01 18:27:37,249 [DEBUG ] findfont: Matching sans\-serif:style=normal:variant=normal:weight=bold:stretch=normal:size=16.0. -LINE 1346: 2020-10-01 18:27:37,250 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,250 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,250 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,250 [DEBUG ] findfont: score() = 0.33499999999999996 -LINE 1346: 2020-10-01 18:27:37,250 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,250 [DEBUG ] findfont: score() = 0.05 -LINE 1346: 2020-10-01 18:27:37,250 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,250 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,250 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,250 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,250 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,250 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,250 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,250 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,250 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,250 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,250 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,250 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,250 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,251 [DEBUG ] findfont: score() = 1.335 -LINE 1346: 2020-10-01 18:27:37,251 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,251 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,251 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,251 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,251 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,251 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,251 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,251 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,251 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,251 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,251 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,251 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,251 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,251 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,251 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,251 [DEBUG ] findfont: score() = 1.05 -LINE 1346: 2020-10-01 18:27:37,251 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,251 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,251 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,251 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,252 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,252 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,252 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,252 [DEBUG ] findfont: score() = 10.535 -LINE 1346: 2020-10-01 18:27:37,252 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,252 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,252 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,252 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,252 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,252 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,252 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,252 [DEBUG ] findfont: score() = 10.44 -LINE 1346: 2020-10-01 18:27:37,252 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,252 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,252 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,252 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,252 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,252 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,252 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,252 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,252 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,253 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,253 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,253 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,253 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,253 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,253 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,253 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,253 [DEBUG ] findfont: score() = 11.535 -LINE 1346: 2020-10-01 18:27:37,253 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,253 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,253 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,253 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,253 [DEBUG ] findfont: score() = 10.145 -LINE 1346: 2020-10-01 18:27:37,253 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,253 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,253 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,253 [DEBUG ] findfont: score() = 10.145 -LINE 1346: 2020-10-01 18:27:37,253 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,253 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,253 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,253 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,254 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,254 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,254 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,254 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,254 [DEBUG ] findfont: score() = 10.44 -LINE 1346: 2020-10-01 18:27:37,254 [DEBUG ] findfont: score() = 7.607727272727273 -LINE 1346: 2020-10-01 18:27:37,254 [DEBUG ] findfont: score() = 7.698636363636363 -LINE 1346: 2020-10-01 18:27:37,254 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,254 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,254 [DEBUG ] findfont: score() = 10.145 -LINE 1346: 2020-10-01 18:27:37,254 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,254 [DEBUG ] findfont: score() = 10.43 -LINE 1346: 2020-10-01 18:27:37,254 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,254 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,254 [DEBUG ] findfont: score() = 11.525 -LINE 1346: 2020-10-01 18:27:37,254 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,254 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,254 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,254 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,254 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,254 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,255 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,255 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,255 [DEBUG ] findfont: score() = 10.344999999999999 -LINE 1346: 2020-10-01 18:27:37,255 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,255 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,255 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,255 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,255 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,255 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,255 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,255 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,255 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,255 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,255 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,255 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,255 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,255 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,255 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,255 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,255 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,255 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,255 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,256 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,256 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,256 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,256 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,256 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,256 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,256 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,256 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,256 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,256 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,256 [DEBUG ] findfont: score() = 10.43 -LINE 1346: 2020-10-01 18:27:37,256 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,256 [DEBUG ] findfont: score() = 11.43 -LINE 1346: 2020-10-01 18:27:37,256 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,256 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,256 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,256 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,256 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,256 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,256 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,256 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,256 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,256 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,257 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,257 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,257 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,257 [DEBUG ] findfont: score() = 10.44 -LINE 1346: 2020-10-01 18:27:37,257 [DEBUG ] findfont: score() = 10.62 -LINE 1346: 2020-10-01 18:27:37,257 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,257 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,257 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,257 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,257 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,257 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,257 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,257 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,257 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,257 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,257 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,257 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,257 [DEBUG ] findfont: score() = 10.25 -LINE 1346: 2020-10-01 18:27:37,257 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,258 [DEBUG ] findfont: score() = 10.25 -LINE 1346: 2020-10-01 18:27:37,258 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,258 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,258 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,258 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,258 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,258 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,258 [DEBUG ] findfont: score() = 11.24 -LINE 1346: 2020-10-01 18:27:37,258 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,258 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,258 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,258 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,258 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,258 [DEBUG ] findfont: score() = 10.344999999999999 -LINE 1346: 2020-10-01 18:27:37,258 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,258 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,258 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,258 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,258 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,258 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,258 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,258 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,259 [DEBUG ] findfont: score() = 11.24 -LINE 1346: 2020-10-01 18:27:37,259 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,259 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,259 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,259 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,259 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,259 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,259 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,259 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,259 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,259 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,259 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,259 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,259 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,259 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,259 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,259 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,259 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,259 [DEBUG ] findfont: score() = 10.71025 -LINE 1346: 2020-10-01 18:27:37,259 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,259 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,259 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,260 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,260 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,260 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,260 [DEBUG ] findfont: score() = 10.43 -LINE 1346: 2020-10-01 18:27:37,260 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,260 [DEBUG ] findfont: score() = 10.62 -LINE 1346: 2020-10-01 18:27:37,260 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,260 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,260 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,260 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,260 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,260 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,260 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,260 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,260 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,260 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,260 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,260 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,260 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,260 [DEBUG ] findfont: score() = 10.344999999999999 -LINE 1346: 2020-10-01 18:27:37,260 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,261 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,261 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,261 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,261 [DEBUG ] findfont: score() = 10.31125 -LINE 1346: 2020-10-01 18:27:37,261 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,261 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,261 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,261 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,261 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,261 [DEBUG ] findfont: score() = 10.535 -LINE 1346: 2020-10-01 18:27:37,261 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,261 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,261 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,261 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,261 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,261 [DEBUG ] findfont: score() = 10.43 -LINE 1346: 2020-10-01 18:27:37,261 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,261 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,261 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,261 [DEBUG ] findfont: score() = 10.344999999999999 -LINE 1346: 2020-10-01 18:27:37,261 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,261 [DEBUG ] findfont: score() = 10.43 -LINE 1346: 2020-10-01 18:27:37,262 [DEBUG ] findfont: score() = 10.43 -LINE 1346: 2020-10-01 18:27:37,262 [DEBUG ] findfont: score() = 10.525 -LINE 1346: 2020-10-01 18:27:37,262 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,262 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,262 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,262 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,262 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,262 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,262 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,262 [DEBUG ] findfont: score() = 10.43 -LINE 1346: 2020-10-01 18:27:37,262 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,262 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,262 [DEBUG ] findfont: score() = 10.4775 -LINE 1346: 2020-10-01 18:27:37,262 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,262 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,262 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,262 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,262 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,262 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,262 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,262 [DEBUG ] findfont: score() = 10.715 -LINE 1346: 2020-10-01 18:27:37,262 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,263 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,263 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,263 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,263 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,263 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,263 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,263 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,263 [DEBUG ] findfont: score() = 3.6863636363636365 -LINE 1346: 2020-10-01 18:27:37,263 [DEBUG ] findfont: score() = 10.535 -LINE 1346: 2020-10-01 18:27:37,263 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,263 [DEBUG ] findfont: score() = 6.413636363636363 -LINE 1346: 2020-10-01 18:27:37,263 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,263 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,263 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,263 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,263 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,263 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,263 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,263 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,263 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,263 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,263 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,264 [DEBUG ] findfont: score() = 4.6863636363636365 -LINE 1346: 2020-10-01 18:27:37,264 [DEBUG ] findfont: score() = 10.25 -LINE 1346: 2020-10-01 18:27:37,264 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,264 [DEBUG ] findfont: score() = 4.971363636363637 -LINE 1346: 2020-10-01 18:27:37,264 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,264 [DEBUG ] findfont: score() = 11.43 -LINE 1346: 2020-10-01 18:27:37,264 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,264 [DEBUG ] findfont: score() = 11.05 -LINE 1346: 2020-10-01 18:27:37,264 [DEBUG ] findfont: score() = 2.967272727272727 -LINE 1346: 2020-10-01 18:27:37,264 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,264 [DEBUG ] findfont: score() = 6.698636363636363 -LINE 1346: 2020-10-01 18:27:37,264 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,264 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,264 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,264 [DEBUG ] findfont: score() = 10.535 -LINE 1346: 2020-10-01 18:27:37,264 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,264 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,264 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,264 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,264 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,264 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,264 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,265 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,265 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,265 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,265 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,265 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,265 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,265 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,265 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,265 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,265 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,265 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,265 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,265 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,265 [DEBUG ] findfont: score() = 3.9713636363636367 -LINE 1346: 2020-10-01 18:27:37,265 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,265 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,265 [DEBUG ] findfont: score() = 10.25 -LINE 1346: 2020-10-01 18:27:37,265 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,265 [DEBUG ] findfont: score() = 10.525 -LINE 1346: 2020-10-01 18:27:37,265 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,265 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,265 [DEBUG ] findfont: score() = 11.24 -LINE 1346: 2020-10-01 18:27:37,266 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,266 [DEBUG ] findfont: score() = 11.31125 -LINE 1346: 2020-10-01 18:27:37,266 [DEBUG ] findfont: score() = 11.43 -LINE 1346: 2020-10-01 18:27:37,266 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,266 [DEBUG ] findfont: score() = 10.629999999999999 -LINE 1346: 2020-10-01 18:27:37,266 [DEBUG ] findfont: score() = 11.145 -LINE 1346: 2020-10-01 18:27:37,266 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,266 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,266 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,266 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,266 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,266 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,266 [DEBUG ] findfont: score() = 10.535 -LINE 1346: 2020-10-01 18:27:37,266 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,266 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,266 [DEBUG ] findfont: score() = 10.344999999999999 -LINE 1346: 2020-10-01 18:27:37,266 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,266 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,266 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,266 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,266 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,266 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,266 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,267 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,267 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,267 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,267 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,267 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,267 [DEBUG ] findfont: score() = 10.25 -LINE 1346: 2020-10-01 18:27:37,267 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,267 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,267 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,267 [DEBUG ] findfont: score() = 10.344999999999999 -LINE 1346: 2020-10-01 18:27:37,267 [DEBUG ] findfont: score() = 10.535 -LINE 1346: 2020-10-01 18:27:37,267 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,267 [DEBUG ] findfont: score() = 7.413636363636363 -LINE 1346: 2020-10-01 18:27:37,267 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,267 [DEBUG ] findfont: score() = 0.33499999999999996 -LINE 1346: 2020-10-01 18:27:37,267 [DEBUG ] findfont: score() = 10.05 -LINE 1346: 2020-10-01 18:27:37,267 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,267 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,267 [DEBUG ] findfont: score() = 11.335 -LINE 1346: 2020-10-01 18:27:37,267 [DEBUG ] findfont: score() = 10.4775 -LINE 1346: 2020-10-01 18:27:37,267 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,267 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,268 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,268 [DEBUG ] findfont: score() = 10.24 -LINE 1346: 2020-10-01 18:27:37,268 [DEBUG ] findfont: score() = 10.335 -LINE 1346: 2020-10-01 18:27:37,268 [DEBUG ] findfont: score() = 11.25 -LINE 1346: 2020-10-01 18:27:37,268 [DEBUG ] findfont: score() = 10.43 -LINE 1346: 2020-10-01 18:27:37,268 [DEBUG ] findfont: score() = 10.43 -LINE 1366: 2020-10-01 18:27:37,268 [DEBUG ] findfont: Matching sans\-serif:style=normal:variant=normal:weight=bold:stretch=normal:size=16.0 to DejaVu Sans ('/Users/rui.benfeitas/miniconda3/envs/merged1/lib/python3.8/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Bold.ttf') with score of 0.050000. -LINE 388 : 2020-10-01 18:27:37,825 [INFO ] Done. - -LINE 226 : 2020-10-01 18:27:37,832 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:27:38,048 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:27:38,051 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:27:38,517 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:27:38,631 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:27:38,632 [DEBUG ] Start Enrichr using library: KEGG_2019_Human -LINE 370 : 2020-10-01 18:27:38,632 [INFO ] Analysis name: , Enrichr Library: KEGG_2019_Human -LINE 226 : 2020-10-01 18:27:38,635 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:27:38,851 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:27:38,854 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:27:39,981 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:27:40,989 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:27:41,205 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210186&filename=KEGG_2019_Human..reports&backgroundType=KEGG_2019_Human HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:27:41,209 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:27:41,699 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210186&filename=KEGG_2019_Human..reports&backgroundType=KEGG_2019_Human HTTP/1.1" 200 None -LINE 378 : 2020-10-01 18:27:41,817 [INFO ] Save file of enrichment results: Job Id:046463e0032514a522c2349bc3e4ed29 -LINE 388 : 2020-10-01 18:27:42,225 [INFO ] Done. - -LINE 226 : 2020-10-01 18:27:42,232 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:27:42,447 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:27:42,453 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:27:42,924 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:27:43,039 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:27:43,040 [DEBUG ] Start Enrichr using library: OMIM_Disease -LINE 370 : 2020-10-01 18:27:43,040 [INFO ] Analysis name: , Enrichr Library: OMIM_Disease -LINE 226 : 2020-10-01 18:27:43,044 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:27:43,260 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:27:43,266 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:27:44,399 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:27:45,412 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:27:45,628 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210187&filename=OMIM_Disease..reports&backgroundType=OMIM_Disease HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:27:45,634 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:27:46,117 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210187&filename=OMIM_Disease..reports&backgroundType=OMIM_Disease HTTP/1.1" 200 3482 -LINE 378 : 2020-10-01 18:27:46,125 [INFO ] Save file of enrichment results: Job Id:4781755539b6b282985adf3d0b1871ab -LINE 388 : 2020-10-01 18:27:46,334 [INFO ] Done. - -LINE 226 : 2020-10-01 18:27:46,344 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:27:46,561 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:27:46,566 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:27:47,023 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:27:47,135 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:27:47,136 [DEBUG ] Start Enrichr using library: GO_Biological_Process_2018 -LINE 370 : 2020-10-01 18:27:47,136 [INFO ] Analysis name: , Enrichr Library: GO_Biological_Process_2018 -LINE 226 : 2020-10-01 18:27:47,139 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:27:47,356 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:27:47,361 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:27:48,994 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:27:50,007 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:27:50,223 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210189&filename=GO_Biological_Process_2018..reports&backgroundType=GO_Biological_Process_2018 HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:27:50,226 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:27:51,232 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210189&filename=GO_Biological_Process_2018..reports&backgroundType=GO_Biological_Process_2018 HTTP/1.1" 200 None -LINE 378 : 2020-10-01 18:27:51,813 [INFO ] Save file of enrichment results: Job Id:767dc8a07e24445cd6f59b6054350dbf -LINE 388 : 2020-10-01 18:27:52,374 [INFO ] Done. - -LINE 226 : 2020-10-01 18:27:52,383 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:27:52,599 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:27:52,603 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:27:53,075 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:27:53,191 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:27:53,191 [DEBUG ] Start Enrichr using library: KEGG_2019_Human -LINE 370 : 2020-10-01 18:27:53,191 [INFO ] Analysis name: , Enrichr Library: KEGG_2019_Human -LINE 226 : 2020-10-01 18:27:53,195 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:27:53,411 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:27:53,418 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:27:54,511 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:27:55,521 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:27:55,736 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210191&filename=KEGG_2019_Human..reports&backgroundType=KEGG_2019_Human HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:27:55,740 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:27:56,214 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210191&filename=KEGG_2019_Human..reports&backgroundType=KEGG_2019_Human HTTP/1.1" 200 None -LINE 378 : 2020-10-01 18:27:56,329 [INFO ] Save file of enrichment results: Job Id:690b57ea27fa75952c3045492af701fd -LINE 388 : 2020-10-01 18:27:56,731 [INFO ] Done. - -LINE 226 : 2020-10-01 18:27:56,739 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:27:56,954 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:27:56,957 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:27:57,410 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:27:57,521 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:27:57,521 [DEBUG ] Start Enrichr using library: OMIM_Disease -LINE 370 : 2020-10-01 18:27:57,521 [INFO ] Analysis name: , Enrichr Library: OMIM_Disease -LINE 226 : 2020-10-01 18:27:57,524 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:27:57,741 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:27:57,745 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:27:58,812 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:27:59,819 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:00,034 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210193&filename=OMIM_Disease..reports&backgroundType=OMIM_Disease HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:00,038 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:00,533 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210193&filename=OMIM_Disease..reports&backgroundType=OMIM_Disease HTTP/1.1" 200 3757 -LINE 378 : 2020-10-01 18:28:00,539 [INFO ] Save file of enrichment results: Job Id:3eca4e952190b13a42eec8ac971f4020 -LINE 387 : 2020-10-01 18:28:00,542 [WARNING ] Warning: No enrich terms using library OMIM_Disease when cutoff = 0.05 -LINE 388 : 2020-10-01 18:28:00,543 [INFO ] Done. - -LINE 226 : 2020-10-01 18:28:00,579 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:00,794 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:00,799 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:01,268 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:28:01,384 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:28:01,384 [DEBUG ] Start Enrichr using library: GO_Biological_Process_2018 -LINE 370 : 2020-10-01 18:28:01,384 [INFO ] Analysis name: , Enrichr Library: GO_Biological_Process_2018 -LINE 226 : 2020-10-01 18:28:01,388 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:01,604 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:01,610 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:03,256 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:28:04,263 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:04,479 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210194&filename=GO_Biological_Process_2018..reports&backgroundType=GO_Biological_Process_2018 HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:04,485 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:05,012 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210194&filename=GO_Biological_Process_2018..reports&backgroundType=GO_Biological_Process_2018 HTTP/1.1" 200 None -LINE 378 : 2020-10-01 18:28:05,592 [INFO ] Save file of enrichment results: Job Id:dd96d7a397d8c020737f46a2b2524510 -LINE 387 : 2020-10-01 18:28:05,614 [WARNING ] Warning: No enrich terms using library GO_Biological_Process_2018 when cutoff = 0.05 -LINE 388 : 2020-10-01 18:28:05,615 [INFO ] Done. - -LINE 226 : 2020-10-01 18:28:05,623 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:05,838 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:05,844 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:06,392 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:28:06,504 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:28:06,504 [DEBUG ] Start Enrichr using library: KEGG_2019_Human -LINE 370 : 2020-10-01 18:28:06,504 [INFO ] Analysis name: , Enrichr Library: KEGG_2019_Human -LINE 226 : 2020-10-01 18:28:06,508 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:06,724 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:06,729 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:08,408 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:28:09,417 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:09,633 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210196&filename=KEGG_2019_Human..reports&backgroundType=KEGG_2019_Human HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:09,639 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:10,145 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210196&filename=KEGG_2019_Human..reports&backgroundType=KEGG_2019_Human HTTP/1.1" 200 None -LINE 378 : 2020-10-01 18:28:10,267 [INFO ] Save file of enrichment results: Job Id:35d7658398d9bbc51ecac6e6b223dd26 -LINE 387 : 2020-10-01 18:28:10,272 [WARNING ] Warning: No enrich terms using library KEGG_2019_Human when cutoff = 0.05 -LINE 388 : 2020-10-01 18:28:10,273 [INFO ] Done. - -LINE 226 : 2020-10-01 18:28:10,281 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:10,496 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:10,501 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:10,971 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:28:11,087 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:28:11,087 [DEBUG ] Start Enrichr using library: OMIM_Disease -LINE 370 : 2020-10-01 18:28:11,088 [INFO ] Analysis name: , Enrichr Library: OMIM_Disease -LINE 226 : 2020-10-01 18:28:11,091 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:11,307 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:11,313 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:12,947 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:28:13,955 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:14,171 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210197&filename=OMIM_Disease..reports&backgroundType=OMIM_Disease HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:14,177 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:14,674 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210197&filename=OMIM_Disease..reports&backgroundType=OMIM_Disease HTTP/1.1" 200 4650 -LINE 378 : 2020-10-01 18:28:14,682 [INFO ] Save file of enrichment results: Job Id:9fe47f0e81846baf798bf53600c7103d -LINE 387 : 2020-10-01 18:28:14,685 [WARNING ] Warning: No enrich terms using library OMIM_Disease when cutoff = 0.05 -LINE 388 : 2020-10-01 18:28:14,686 [INFO ] Done. - -LINE 226 : 2020-10-01 18:28:14,696 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:14,912 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:14,918 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:15,375 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:28:15,487 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:28:15,488 [DEBUG ] Start Enrichr using library: GO_Biological_Process_2018 -LINE 370 : 2020-10-01 18:28:15,488 [INFO ] Analysis name: , Enrichr Library: GO_Biological_Process_2018 -LINE 226 : 2020-10-01 18:28:15,491 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:15,707 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:15,712 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:17,394 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:28:18,399 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:18,614 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210198&filename=GO_Biological_Process_2018..reports&backgroundType=GO_Biological_Process_2018 HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:18,618 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:19,112 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210198&filename=GO_Biological_Process_2018..reports&backgroundType=GO_Biological_Process_2018 HTTP/1.1" 200 None -LINE 378 : 2020-10-01 18:28:19,668 [INFO ] Save file of enrichment results: Job Id:9d6f2fa9e24ec9e0e1bf473184e0e3af -LINE 388 : 2020-10-01 18:28:20,220 [INFO ] Done. - -LINE 226 : 2020-10-01 18:28:20,227 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:20,443 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:20,446 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:20,913 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:28:21,028 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:28:21,028 [DEBUG ] Start Enrichr using library: KEGG_2019_Human -LINE 370 : 2020-10-01 18:28:21,028 [INFO ] Analysis name: , Enrichr Library: KEGG_2019_Human -LINE 226 : 2020-10-01 18:28:21,031 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:21,247 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:21,251 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:22,352 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:28:23,358 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:23,573 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210199&filename=KEGG_2019_Human..reports&backgroundType=KEGG_2019_Human HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:23,577 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:24,054 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210199&filename=KEGG_2019_Human..reports&backgroundType=KEGG_2019_Human HTTP/1.1" 200 None -LINE 378 : 2020-10-01 18:28:24,168 [INFO ] Save file of enrichment results: Job Id:e8db677eefc1b0ad6b266cbf91e74339 -LINE 388 : 2020-10-01 18:28:24,580 [INFO ] Done. - -LINE 226 : 2020-10-01 18:28:24,587 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:24,802 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:24,805 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:25,259 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:28:25,369 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:28:25,370 [DEBUG ] Start Enrichr using library: OMIM_Disease -LINE 370 : 2020-10-01 18:28:25,370 [INFO ] Analysis name: , Enrichr Library: OMIM_Disease -LINE 226 : 2020-10-01 18:28:25,373 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:25,588 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:25,592 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:26,696 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:28:27,702 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:27,918 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210200&filename=OMIM_Disease..reports&backgroundType=OMIM_Disease HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:27,923 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:28,404 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210200&filename=OMIM_Disease..reports&backgroundType=OMIM_Disease HTTP/1.1" 200 3482 -LINE 378 : 2020-10-01 18:28:28,411 [INFO ] Save file of enrichment results: Job Id:0bb70f504f28afb09c8b7aacb31c0c2d -LINE 388 : 2020-10-01 18:28:28,632 [INFO ] Done. - -LINE 226 : 2020-10-01 18:28:28,641 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:28,857 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:28,862 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:29,319 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:28:29,430 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:28:29,430 [DEBUG ] Start Enrichr using library: GO_Biological_Process_2018 -LINE 370 : 2020-10-01 18:28:29,430 [INFO ] Analysis name: , Enrichr Library: GO_Biological_Process_2018 -LINE 226 : 2020-10-01 18:28:29,433 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:29,649 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:29,654 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:31,209 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:28:32,217 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:32,433 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210201&filename=GO_Biological_Process_2018..reports&backgroundType=GO_Biological_Process_2018 HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:32,524 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:33,018 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210201&filename=GO_Biological_Process_2018..reports&backgroundType=GO_Biological_Process_2018 HTTP/1.1" 200 None -LINE 378 : 2020-10-01 18:28:33,581 [INFO ] Save file of enrichment results: Job Id:89044b7fecd8f4deb9e8830540a056da -LINE 388 : 2020-10-01 18:28:34,142 [INFO ] Done. - -LINE 226 : 2020-10-01 18:28:34,151 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:34,366 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:34,371 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:34,840 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:28:34,956 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:28:34,956 [DEBUG ] Start Enrichr using library: KEGG_2019_Human -LINE 370 : 2020-10-01 18:28:34,956 [INFO ] Analysis name: , Enrichr Library: KEGG_2019_Human -LINE 226 : 2020-10-01 18:28:34,959 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:35,176 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:35,180 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:36,228 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:28:37,236 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:37,452 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210202&filename=KEGG_2019_Human..reports&backgroundType=KEGG_2019_Human HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:37,457 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:37,936 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210202&filename=KEGG_2019_Human..reports&backgroundType=KEGG_2019_Human HTTP/1.1" 200 None -LINE 378 : 2020-10-01 18:28:38,050 [INFO ] Save file of enrichment results: Job Id:579850a9be59a4c81b417ee3b854d167 -LINE 388 : 2020-10-01 18:28:38,421 [INFO ] Done. - -LINE 226 : 2020-10-01 18:28:38,431 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:38,647 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:38,652 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:39,119 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:28:39,243 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:28:39,243 [DEBUG ] Start Enrichr using library: OMIM_Disease -LINE 370 : 2020-10-01 18:28:39,243 [INFO ] Analysis name: , Enrichr Library: OMIM_Disease -LINE 226 : 2020-10-01 18:28:39,246 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:39,462 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:39,465 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:40,524 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:28:41,532 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:41,748 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210203&filename=OMIM_Disease..reports&backgroundType=OMIM_Disease HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:41,753 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:42,257 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210203&filename=OMIM_Disease..reports&backgroundType=OMIM_Disease HTTP/1.1" 200 3763 -LINE 378 : 2020-10-01 18:28:42,265 [INFO ] Save file of enrichment results: Job Id:9d009f3b8de0c5fea3de064f91895606 -LINE 387 : 2020-10-01 18:28:42,268 [WARNING ] Warning: No enrich terms using library OMIM_Disease when cutoff = 0.05 -LINE 388 : 2020-10-01 18:28:42,269 [INFO ] Done. - -LINE 226 : 2020-10-01 18:28:42,304 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:42,521 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:42,526 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:42,995 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:28:43,110 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:28:43,111 [DEBUG ] Start Enrichr using library: GO_Biological_Process_2018 -LINE 370 : 2020-10-01 18:28:43,111 [INFO ] Analysis name: , Enrichr Library: GO_Biological_Process_2018 -LINE 226 : 2020-10-01 18:28:43,114 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:43,331 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:43,336 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:45,165 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:28:46,174 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:46,390 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210204&filename=GO_Biological_Process_2018..reports&backgroundType=GO_Biological_Process_2018 HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:46,396 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:46,923 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210204&filename=GO_Biological_Process_2018..reports&backgroundType=GO_Biological_Process_2018 HTTP/1.1" 200 None -LINE 378 : 2020-10-01 18:28:47,503 [INFO ] Save file of enrichment results: Job Id:88c08df1fd31666c2b682dac3cdb5502 -LINE 388 : 2020-10-01 18:28:48,062 [INFO ] Done. - -LINE 226 : 2020-10-01 18:28:48,070 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:48,286 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:48,292 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:48,763 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:28:48,879 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:28:48,879 [DEBUG ] Start Enrichr using library: KEGG_2019_Human -LINE 370 : 2020-10-01 18:28:48,879 [INFO ] Analysis name: , Enrichr Library: KEGG_2019_Human -LINE 226 : 2020-10-01 18:28:48,882 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:49,098 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:49,101 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:50,305 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:28:51,312 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:51,528 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210205&filename=KEGG_2019_Human..reports&backgroundType=KEGG_2019_Human HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:51,533 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:52,014 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210205&filename=KEGG_2019_Human..reports&backgroundType=KEGG_2019_Human HTTP/1.1" 200 None -LINE 378 : 2020-10-01 18:28:52,236 [INFO ] Save file of enrichment results: Job Id:8317f64550ca47e81c410588a3a5d052 -LINE 388 : 2020-10-01 18:28:52,595 [INFO ] Done. - -LINE 226 : 2020-10-01 18:28:52,603 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:52,819 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:52,824 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:53,298 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:28:53,413 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:28:53,414 [DEBUG ] Start Enrichr using library: OMIM_Disease -LINE 370 : 2020-10-01 18:28:53,414 [INFO ] Analysis name: , Enrichr Library: OMIM_Disease -LINE 226 : 2020-10-01 18:28:53,417 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:53,634 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:53,640 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:54,861 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:28:55,872 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:56,087 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210206&filename=OMIM_Disease..reports&backgroundType=OMIM_Disease HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:56,091 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:56,589 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210206&filename=OMIM_Disease..reports&backgroundType=OMIM_Disease HTTP/1.1" 200 4397 -LINE 378 : 2020-10-01 18:28:56,596 [INFO ] Save file of enrichment results: Job Id:69e6a57d4850994acd2b1156f5473487 -LINE 387 : 2020-10-01 18:28:56,602 [WARNING ] Warning: No enrich terms using library OMIM_Disease when cutoff = 0.05 -LINE 388 : 2020-10-01 18:28:56,602 [INFO ] Done. - -LINE 226 : 2020-10-01 18:28:56,613 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:56,829 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:56,834 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:57,294 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:28:57,407 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:28:57,408 [DEBUG ] Start Enrichr using library: GO_Biological_Process_2018 -LINE 370 : 2020-10-01 18:28:57,408 [INFO ] Analysis name: , Enrichr Library: GO_Biological_Process_2018 -LINE 226 : 2020-10-01 18:28:57,411 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:28:57,628 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:28:57,633 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:28:59,374 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:29:00,382 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:00,598 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210207&filename=GO_Biological_Process_2018..reports&backgroundType=GO_Biological_Process_2018 HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:00,604 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:01,133 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210207&filename=GO_Biological_Process_2018..reports&backgroundType=GO_Biological_Process_2018 HTTP/1.1" 200 None -LINE 378 : 2020-10-01 18:29:01,596 [INFO ] Save file of enrichment results: Job Id:db407afdaa5d312dd900832857de40fc -LINE 387 : 2020-10-01 18:29:01,613 [WARNING ] Warning: No enrich terms using library GO_Biological_Process_2018 when cutoff = 0.05 -LINE 388 : 2020-10-01 18:29:01,614 [INFO ] Done. - -LINE 226 : 2020-10-01 18:29:01,623 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:01,838 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:01,842 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:02,296 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:29:02,406 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:29:02,406 [DEBUG ] Start Enrichr using library: KEGG_2019_Human -LINE 370 : 2020-10-01 18:29:02,406 [INFO ] Analysis name: , Enrichr Library: KEGG_2019_Human -LINE 226 : 2020-10-01 18:29:02,409 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:02,625 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:02,628 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:03,783 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:29:04,789 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:05,005 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210208&filename=KEGG_2019_Human..reports&backgroundType=KEGG_2019_Human HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:05,010 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:05,486 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210208&filename=KEGG_2019_Human..reports&backgroundType=KEGG_2019_Human HTTP/1.1" 200 None -LINE 378 : 2020-10-01 18:29:05,601 [INFO ] Save file of enrichment results: Job Id:d91837c77ab2aabdd45354d2a8e72b0f -LINE 388 : 2020-10-01 18:29:05,833 [INFO ] Done. - -LINE 226 : 2020-10-01 18:29:05,840 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:06,055 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:06,058 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:06,513 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:29:06,625 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:29:06,625 [DEBUG ] Start Enrichr using library: OMIM_Disease -LINE 370 : 2020-10-01 18:29:06,626 [INFO ] Analysis name: , Enrichr Library: OMIM_Disease -LINE 226 : 2020-10-01 18:29:06,629 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:06,845 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:06,849 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:08,008 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:29:09,014 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:09,230 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210210&filename=OMIM_Disease..reports&backgroundType=OMIM_Disease HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:09,235 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:09,728 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210210&filename=OMIM_Disease..reports&backgroundType=OMIM_Disease HTTP/1.1" 200 3208 -LINE 378 : 2020-10-01 18:29:09,735 [INFO ] Save file of enrichment results: Job Id:452dd34ccfcc0312a29d016ac2482f01 -LINE 387 : 2020-10-01 18:29:09,739 [WARNING ] Warning: No enrich terms using library OMIM_Disease when cutoff = 0.05 -LINE 388 : 2020-10-01 18:29:09,740 [INFO ] Done. - -LINE 226 : 2020-10-01 18:29:09,750 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:09,966 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:09,971 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:10,429 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:29:10,541 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:29:10,542 [DEBUG ] Start Enrichr using library: GO_Biological_Process_2018 -LINE 370 : 2020-10-01 18:29:10,542 [INFO ] Analysis name: , Enrichr Library: GO_Biological_Process_2018 -LINE 226 : 2020-10-01 18:29:10,545 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:10,762 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:10,767 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:12,322 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:29:13,328 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:13,544 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210212&filename=GO_Biological_Process_2018..reports&backgroundType=GO_Biological_Process_2018 HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:13,550 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:14,122 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210212&filename=GO_Biological_Process_2018..reports&backgroundType=GO_Biological_Process_2018 HTTP/1.1" 200 None -LINE 378 : 2020-10-01 18:29:14,700 [INFO ] Save file of enrichment results: Job Id:9ca0281abcbe25820ac037427ddec4fa -LINE 388 : 2020-10-01 18:29:15,337 [INFO ] Done. - -LINE 226 : 2020-10-01 18:29:15,346 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:15,561 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:15,567 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:16,036 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:29:16,152 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:29:16,153 [DEBUG ] Start Enrichr using library: KEGG_2019_Human -LINE 370 : 2020-10-01 18:29:16,153 [INFO ] Analysis name: , Enrichr Library: KEGG_2019_Human -LINE 226 : 2020-10-01 18:29:16,156 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:16,373 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:16,379 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:17,439 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:29:18,447 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:18,663 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210213&filename=KEGG_2019_Human..reports&backgroundType=KEGG_2019_Human HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:18,669 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:19,162 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210213&filename=KEGG_2019_Human..reports&backgroundType=KEGG_2019_Human HTTP/1.1" 200 None -LINE 378 : 2020-10-01 18:29:19,281 [INFO ] Save file of enrichment results: Job Id:fd437229d9a99eee3f54bac689c66830 -LINE 388 : 2020-10-01 18:29:19,655 [INFO ] Done. - -LINE 226 : 2020-10-01 18:29:19,663 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:19,878 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:19,882 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:20,334 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:29:20,447 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:29:20,447 [DEBUG ] Start Enrichr using library: OMIM_Disease -LINE 370 : 2020-10-01 18:29:20,447 [INFO ] Analysis name: , Enrichr Library: OMIM_Disease -LINE 226 : 2020-10-01 18:29:20,451 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:20,667 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:20,674 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:21,719 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:29:22,725 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:22,941 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210214&filename=OMIM_Disease..reports&backgroundType=OMIM_Disease HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:22,947 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:23,561 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210214&filename=OMIM_Disease..reports&backgroundType=OMIM_Disease HTTP/1.1" 200 4057 -LINE 378 : 2020-10-01 18:29:23,569 [INFO ] Save file of enrichment results: Job Id:0bc32f414a5d074e9ffb7f7453b1e5d1 -LINE 388 : 2020-10-01 18:29:23,809 [INFO ] Done. - -LINE 226 : 2020-10-01 18:29:23,818 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:24,033 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:24,038 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:24,508 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:29:24,622 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:29:24,623 [DEBUG ] Start Enrichr using library: GO_Biological_Process_2018 -LINE 370 : 2020-10-01 18:29:24,623 [INFO ] Analysis name: , Enrichr Library: GO_Biological_Process_2018 -LINE 226 : 2020-10-01 18:29:24,625 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:24,841 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:24,847 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:25,873 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:29:26,882 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:27,098 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210215&filename=GO_Biological_Process_2018..reports&backgroundType=GO_Biological_Process_2018 HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:27,104 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:27,613 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210215&filename=GO_Biological_Process_2018..reports&backgroundType=GO_Biological_Process_2018 HTTP/1.1" 200 None -LINE 378 : 2020-10-01 18:29:28,076 [INFO ] Save file of enrichment results: Job Id:450a4e0ea9bb00011a5971f471290126 -LINE 388 : 2020-10-01 18:29:28,581 [INFO ] Done. - -LINE 226 : 2020-10-01 18:29:28,589 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:28,805 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:28,810 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:29,268 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:29:29,381 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:29:29,381 [DEBUG ] Start Enrichr using library: KEGG_2019_Human -LINE 370 : 2020-10-01 18:29:29,381 [INFO ] Analysis name: , Enrichr Library: KEGG_2019_Human -LINE 226 : 2020-10-01 18:29:29,384 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:29,600 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:29,604 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:30,370 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:29:31,379 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:31,595 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210216&filename=KEGG_2019_Human..reports&backgroundType=KEGG_2019_Human HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:31,600 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:32,086 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210216&filename=KEGG_2019_Human..reports&backgroundType=KEGG_2019_Human HTTP/1.1" 200 None -LINE 378 : 2020-10-01 18:29:32,205 [INFO ] Save file of enrichment results: Job Id:0d1397eb6194d8168b7c1e2283a21382 -LINE 388 : 2020-10-01 18:29:32,572 [INFO ] Done. - -LINE 226 : 2020-10-01 18:29:32,581 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:32,797 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:32,803 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:33,275 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:29:33,391 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:29:33,391 [DEBUG ] Start Enrichr using library: OMIM_Disease -LINE 370 : 2020-10-01 18:29:33,391 [INFO ] Analysis name: , Enrichr Library: OMIM_Disease -LINE 226 : 2020-10-01 18:29:33,395 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:33,611 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:33,617 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:34,401 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:29:35,413 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:35,629 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210217&filename=OMIM_Disease..reports&backgroundType=OMIM_Disease HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:35,635 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:36,203 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210217&filename=OMIM_Disease..reports&backgroundType=OMIM_Disease HTTP/1.1" 200 1216 -LINE 378 : 2020-10-01 18:29:36,211 [INFO ] Save file of enrichment results: Job Id:3a859741e46d9798988eab7e1f91cfe9 -LINE 387 : 2020-10-01 18:29:36,214 [WARNING ] Warning: No enrich terms using library OMIM_Disease when cutoff = 0.05 -LINE 388 : 2020-10-01 18:29:36,215 [INFO ] Done. - -LINE 226 : 2020-10-01 18:29:36,255 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:36,471 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:36,476 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:36,949 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:29:37,065 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:29:37,066 [DEBUG ] Start Enrichr using library: GO_Biological_Process_2018 -LINE 370 : 2020-10-01 18:29:37,066 [INFO ] Analysis name: , Enrichr Library: GO_Biological_Process_2018 -LINE 226 : 2020-10-01 18:29:37,069 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:37,286 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:37,291 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:39,865 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:29:40,872 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:41,088 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210218&filename=GO_Biological_Process_2018..reports&backgroundType=GO_Biological_Process_2018 HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:41,094 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:42,365 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210218&filename=GO_Biological_Process_2018..reports&backgroundType=GO_Biological_Process_2018 HTTP/1.1" 200 None -LINE 378 : 2020-10-01 18:29:42,929 [INFO ] Save file of enrichment results: Job Id:64d1ec0d0354380ff16ae322950a4aa9 -LINE 387 : 2020-10-01 18:29:42,958 [WARNING ] Warning: No enrich terms using library GO_Biological_Process_2018 when cutoff = 0.05 -LINE 388 : 2020-10-01 18:29:42,959 [INFO ] Done. - -LINE 226 : 2020-10-01 18:29:42,968 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:43,184 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:43,189 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:43,658 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:29:43,774 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:29:43,774 [DEBUG ] Start Enrichr using library: KEGG_2019_Human -LINE 370 : 2020-10-01 18:29:43,774 [INFO ] Analysis name: , Enrichr Library: KEGG_2019_Human -LINE 226 : 2020-10-01 18:29:43,778 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:43,994 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:43,999 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:45,636 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:29:46,644 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:46,859 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210219&filename=KEGG_2019_Human..reports&backgroundType=KEGG_2019_Human HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:46,864 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:47,350 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210219&filename=KEGG_2019_Human..reports&backgroundType=KEGG_2019_Human HTTP/1.1" 200 None -LINE 378 : 2020-10-01 18:29:47,467 [INFO ] Save file of enrichment results: Job Id:b3dbbb57cb3b68005bc3f919814b798f -LINE 387 : 2020-10-01 18:29:47,472 [WARNING ] Warning: No enrich terms using library KEGG_2019_Human when cutoff = 0.05 -LINE 388 : 2020-10-01 18:29:47,473 [INFO ] Done. - -LINE 226 : 2020-10-01 18:29:47,481 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:47,696 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:47,701 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:48,154 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:29:48,266 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:29:48,267 [DEBUG ] Start Enrichr using library: OMIM_Disease -LINE 370 : 2020-10-01 18:29:48,267 [INFO ] Analysis name: , Enrichr Library: OMIM_Disease -LINE 226 : 2020-10-01 18:29:48,270 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:48,486 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:48,492 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:50,145 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:29:51,150 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:51,365 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210220&filename=OMIM_Disease..reports&backgroundType=OMIM_Disease HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:51,371 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:51,863 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210220&filename=OMIM_Disease..reports&backgroundType=OMIM_Disease HTTP/1.1" 200 4645 -LINE 378 : 2020-10-01 18:29:51,871 [INFO ] Save file of enrichment results: Job Id:d8e6ff473904433b360268083acb21c4 -LINE 387 : 2020-10-01 18:29:51,874 [WARNING ] Warning: No enrich terms using library OMIM_Disease when cutoff = 0.05 -LINE 388 : 2020-10-01 18:29:51,875 [INFO ] Done. - -LINE 226 : 2020-10-01 18:29:51,885 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:52,101 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/datasetStatistics HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:52,106 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:52,581 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/datasetStatistics HTTP/1.1" 200 None -LINE 351 : 2020-10-01 18:29:52,696 [INFO ] Connecting to Enrichr Server to get latest library names -LINE 369 : 2020-10-01 18:29:52,696 [DEBUG ] Start Enrichr using library: GO_Biological_Process_2018 -LINE 370 : 2020-10-01 18:29:52,696 [INFO ] Analysis name: , Enrichr Library: GO_Biological_Process_2018 -LINE 226 : 2020-10-01 18:29:52,700 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:52,916 [DEBUG ] http://amp.pharm.mssm.edu:80 "POST /Enrichr/addList HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:52,922 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:54,463 [DEBUG ] https://maayanlab.cloud:443 "POST /Enrichr/addList HTTP/1.1" 200 74 -LINE 226 : 2020-10-01 18:29:55,467 [DEBUG ] Starting new HTTP connection (1): amp.pharm.mssm.edu:80 -LINE 433 : 2020-10-01 18:29:55,682 [DEBUG ] http://amp.pharm.mssm.edu:80 "GET /Enrichr/export?userListId=30210221&filename=GO_Biological_Process_2018..reports&backgroundType=GO_Biological_Process_2018 HTTP/1.1" 307 171 -LINE 939 : 2020-10-01 18:29:55,685 [DEBUG ] Starting new HTTPS connection (1): maayanlab.cloud:443 -LINE 433 : 2020-10-01 18:29:56,570 [DEBUG ] https://maayanlab.cloud:443 "GET /Enrichr/export?userListId=30210221&filename=GO_Biological_Process_2018..reports&backgroundType=GO_Biological_Process_2018 HTTP/1.1" 200 None -LINE 378 : 2020-10-01 18:29:57,125 [INFO ] Save file of enrichment results: Job Id:95ccd0d86d7e618b8ca4dbddf1f59f4e -LINE 388 : 2020-10-01 18:29:57,667 [INFO ] Done. - -LINE 226 : 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diff --git a/session_topology/lab/lecture_short.pdf b/session_topology/lab/lectures/lecture_short.pdf similarity index 100% rename from session_topology/lab/lecture_short.pdf rename to session_topology/lab/lectures/lecture_short.pdf diff --git a/session_topology/lab/topology_lab.ipynb b/session_topology/lab/topology_lab.ipynb deleted file mode 100644 index 05f77fa0..00000000 --- a/session_topology/lab/topology_lab.ipynb +++ /dev/null @@ -1,1702 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Rui Benfeitas, Scilifelab, NBIS National Bioinformatics Infrastructure Sweden

\n", - "rui.benfeitas@scilifelab.se\n", - "\n", - "**Abstract** \n", - "In this notebook we will explore how to generate and analyse a multi-omic network comprising metabolites quantifications and gene expression. We will compare these networks against randomly generated networks, and compute different network metrics. At the end we will also perform a community analysis and functional characterization at the gene level. \n", - "
\n", - "
\n", - "**Contents**\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## Preamble\n", - "import sklearn, itertools, random\n", - "import pandas as pd\n", - "import numpy as np\n", - "import igraph as ig\n", - "import sklearn.neighbors\n", - "from sklearn.preprocessing import StandardScaler\n", - "import scipy as sp\n", - "from statsmodels.stats.multitest import multipletests\n", - "import gseapy as gp\n", - "\n", - "# Plotting\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Biological network topology analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Objective** \n", - "In this notebook you will learn how to build and analyse a network built and analysed from a gene-metabolite association analysis. Other mixed networks may also be similarly analyzed, differring only in whether and how you can apply the final functional analysis.\n", - "\n", - "**Data** \n", - "As a test case we will be using the file [met_genes.tsv](met_genes.tsv) which contains abundances for 125 metabolites and 1992 genes, for 24 samples.\n", - "\n", - "**Software** \n", - "This notebook relies on python's igraph for most of the analyses. Most, if not all, functions used here can also be applied with R's igraph. Other packages exist for network analysis including [networkx](https://networkx.github.io/) and [graph-tool](https://graph-tool.skewed.de/). [Snap.py](https://snap.stanford.edu/snappy/) is also a good alternative for large networks.\n", - "\n", - "We will build our network through an association analysis, but there are other methods to do this including [Graphical Lasso](http://statweb.stanford.edu/~tibs/ftp/graph.pdf) or [linear SVR](https://papers.nips.cc/paper/1187-support-vector-method-for-function-approximation-regression-estimation-and-signal-processing.pdf)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data preparation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You will use a gene expression dataset (RNAseq, expressed as TPM) from a disease group with 24 samples to keep analysis memory and time requirements reasonable for this lesson. It is assumed that all batch effects or other possible technical artifacts are not present, and that all data is ready for analysis. However, there are several important considerations in preprocessing your data:\n", - " - How should you deal with missing values? Should you impute them? How?\n", - " - Should you remove samples based on number of missing values?\n", - " - How should you normalize your data in order to make it comparable throughout?\n", - " \n", - "This will depend on the type of that that you have and what you want to do with it, and will severely affect downstream results. It is thus important to carefully think about this." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data=pd.read_csv('data/met_genes.tsv', sep=\"\\t\", index_col=0)\n", - "data.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "No duplicated features are present." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "any(data.index.duplicated()) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "data.groupby('Type').agg('count')[['p10']] #1992 genes, 125 metabolites" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data.shape #2117 features, 25 samples" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A very quick view shows that several gene clusters are found, including two major groups. However, the analysis below does not perform any statistical filtering." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Association analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our initial network analysis will be performed on the association analysis using Spearman correlations. Because this network has a big chance of producing false positives we will consider [Bonferroni correction](https://en.wikipedia.org/wiki/Bonferroni_correction) to control for familywise error, as well as [FDR](https://en.wikipedia.org/wiki/False_discovery_rate#Benjamini%E2%80%93Hochberg_procedure). " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A very quick view shows that several gene clusters are found, including two major groups. However, the analysis below does not perform any statistical filtering." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "values=data.loc[:,data.columns!='Type']\n", - "meta=data[['Type']]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "values.isna().any().any() #we have no rows with NA" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will perform a gene-gene, gene-metabolite, and metabolite-metabolite association analysis by computing pairwise [Spearman correlations](https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.spearmanr.html#scipy.stats.spearmanr). Choosing other non-parametric (Kendall or Spearman) vs parametric (Pearson) methods depends on your data. Here, because we have a small sample size, and want to save on computational time we choose Spearman. \n", - "\n", - "The following takes a few minutes to run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "#Correlation and P val matrices\n", - "Rmatrix, Pmatrix= sp.stats.spearmanr(values.T)\n", - "Rmatrix=pd.DataFrame(Rmatrix, index=values.index.copy(), columns=values.index.copy())\n", - "\n", - "#resulting R matrix\n", - "sns.clustermap(Rmatrix, cmap=\"RdBu_r\", center=0); " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we look at the matrix of P values, we can already see that many of the correlations in the top right columns are not significant even before multiple hypothesis correction:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Pmatrix=pd.DataFrame(Pmatrix, index=values.index.copy(), columns=values.index.copy())\n", - "changed_Pmatrix=Pmatrix.copy()\n", - "changed_Pmatrix[changed_Pmatrix>0.01]=1\n", - "#resulting P matrix, similar to that seen in the section above\n", - "sns.clustermap(changed_Pmatrix); " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now adjust the P values based on the number of comparisons done. The heatmaps above are highlighting a total of $2117^2 \\approx 4.5m$ correlations. However, these numbers consider that the same correlation is computed twice (gene A vs gene B, and gene B vs gene A). If we were to include all correlations, we would thus be including many repeated analyses, and we are only interested in half of that above, and excluding the correlation of a feature with itself. \n", - "This means $\\frac{2117!}{2!(2117-2)!} \\approx 2.2m$ correlations. At an error rate of 0.05, this means that the probability of finding at least one false positive is nearly 100%: $1-0.95^{2000000} \\approx 1$. We thus need to correct P values.\n", - "\n", - "In the following cell, we convert the matrix of p*p features to a long matrix, concatenate both R and P for each correlation, and correct based on [Bonferroni](https://en.wikipedia.org/wiki/Bonferroni_correction) (`Padj`) and [FDR](https://en.wikipedia.org/wiki/False_discovery_rate) (Benjamin-Hochberg)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#prepare P matrix\n", - "Psquared=Pmatrix.where(np.triu(np.ones(Pmatrix.shape),1).astype(bool))\n", - "Psquared.columns.name='Feat2'\n", - "Pmatrix=Pmatrix.stack()\n", - "Pmatrix.index.names=['v1','v2']\n", - "Pmatrix=Pmatrix.reset_index()\n", - "Pmatrix.columns=['feat1','feat2','P']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#prepare R matrix\n", - "Rmatrix=Rmatrix.where(np.triu(np.ones(Rmatrix.shape),1).astype(bool))\n", - "Rmatrix.columns.name='Feat2'\n", - "Rmatrix=Rmatrix.stack()\n", - "Rmatrix.index.names=['v1','v2'] #Avoid stacked names colliding\n", - "Rmatrix=Rmatrix.reset_index()\n", - "Rmatrix.columns=['feat1','feat2','R']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# join both\n", - "PRmatrix=pd.merge(Rmatrix.copy(), Pmatrix.copy(), on=['feat1','feat2']) #Correlation matrix with both R and P\n", - "PRmatrix=PRmatrix.loc[PRmatrix.feat1!=PRmatrix.feat2].dropna()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Multiple hypothesis correction computed on the P column\n", - "adjP=pd.DataFrame(multipletests(PRmatrix['P'], method='bonferroni', alpha=0.01)[1], columns=['Padj'])\n", - "FDR=pd.DataFrame(multipletests(PRmatrix['P'], method='fdr_bh', alpha=0.01)[1], columns=['FDR'])\n", - "\n", - "PRmatrix=pd.concat([ PRmatrix, adjP], axis=1)\n", - "PRmatrix=pd.concat([ PRmatrix, FDR], axis=1)\n", - "\n", - "PRmatrix.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#total number of correlations w/o repetition: 2.2m\n", - "PRmatrix.shape[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Considering the Bonferroni correction we find 16305 correlations that are statistically significant at an $\\alpha < 0.01$. If we consider instead FDR as correction method, we find 402368, which at a FDR of 0.01 implies $0.01 \\times 402368 = 4023$ false positives." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sum(PRmatrix.Padj<0.01)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sum(PRmatrix.FDR<0.01)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's add two additional columns, where we assign `R=0` for those correlations that are not statistically significant (`adjP > 0.01`, and `FDR > 0.01`). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "PRmatrix.loc[:,'R (padj)']=PRmatrix['R'].copy()\n", - "PRmatrix.loc[:,'R (fdr)']=PRmatrix['R'].copy()\n", - "PRmatrix.loc[PRmatrix['Padj']>0.01,'R (padj)']=0\n", - "PRmatrix.loc[PRmatrix['FDR']>0.01,'R (fdr)']=0\n", - "\n", - "all_mets=meta.loc[meta.Type=='met'].index\n", - "PRmatrix['feat1_type']=['met' if x in all_mets else 'gene' for x in PRmatrix.feat1 ]\n", - "PRmatrix['feat2_type']=['met' if x in all_mets else 'gene' for x in PRmatrix.feat2 ]\n", - "PRmatrix['int_type']=PRmatrix.feat1_type+'_'+PRmatrix.feat2_type\n", - "PRmatrix.to_csv('data/association_matrix.tsv', sep=\"\\t\", index=False) #export correlation matrix for faster loading" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can see how the initial heatmap of correlations looks. The next cell converts the matrix from long to squared matrix so that we can generate the heatmap. We will plot those features that show statistically significant associations with more than 5% of the features after FDR correction, and compare them between FDR- and Bonferroni-corrected datasets. \n", - "\n", - "The following plot shows the heatmap of the Spearman rank correlation coefficients after Bonferroni-correction - correlations where Padj > 0.05 are shown as 0." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "#Transforming to a squared matrix again\n", - "PRQ=pd.concat([\n", - " PRmatrix.copy(), \n", - " PRmatrix.copy().rename(columns={'feat1':'feat2','feat2':'feat1'}).loc[:,PRmatrix.columns]\n", - " ]).drop_duplicates()\n", - "\n", - "Rmatrix_fdr=PRQ.copy().pivot(index='feat1',columns='feat2',values='R (fdr)')\n", - "Rmatrix_fdr=Rmatrix_fdr.loc[Rmatrix_fdr.sum()!=0]\n", - "Rmatrix_padj=PRQ.copy().pivot(index='feat1',columns='feat2',values='R (padj)')\n", - "\n", - "Rmatrix_fdr=Rmatrix_fdr.loc[Rmatrix_fdr.index,Rmatrix_fdr.index].fillna(0)\n", - "Rmatrix_padj=Rmatrix_padj.loc[Rmatrix_fdr.index,Rmatrix_fdr.index].fillna(0)\n", - "\n", - "\n", - "#Showing only the top correlated features\n", - "top_features=Rmatrix_fdr.index[(Rmatrix_fdr!=0).sum()>0.05*Rmatrix_fdr.shape[0]] #top features based on FDR\n", - "Rmatrix_fdr_top=Rmatrix_fdr.copy().loc[top_features,top_features] #subsetting R (fdr corrected) matrix\n", - "Rmatrix_padj_top=Rmatrix_padj.copy().loc[top_features,top_features] #subsetting R (bonferroni corrected) matrix\n", - "\n", - "g=sns.clustermap(Rmatrix_padj_top, cmap=\"RdBu_r\", center=0);\n", - "g.fig.suptitle('Spearman R (Padj < 0.01, Bonferroni)');\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "g=sns.clustermap(Rmatrix_fdr_top, cmap=\"RdBu_r\", center=0);\n", - "g.fig.suptitle('Spearman R (FDR<0.01)');\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The plots above show that the Bonferroni correction is only selecting very high (absolute) correlations. This should remove false positives, but it may also remove weaker correlations that are biologically relevant and true positives. The Bonferroni correction also removes most of the negatively-associated features. Notice this from the distribution of correlation coefficients:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "shortPR=PRmatrix.copy().loc[:,['feat1','feat2','R (padj)','R (fdr)']]\n", - "shortPR=shortPR.loc[shortPR.feat1!=shortPR.feat2]\n", - "\n", - "fig=plt.figure(figsize=(8,4))\n", - "p=sns.histplot(shortPR['R (padj)'][shortPR['R (padj)']!=0], color='black', label='Bonferroni (<0.01)', kde=True, bins=100);\n", - "p.set(ylabel='PDF (Bonferroni)')\n", - "ax2=p.twinx()\n", - "g=sns.histplot(shortPR['R (fdr)'][shortPR['R (fdr)']!=0], ax=ax2, color='red', label='FDR (<0.01)', kde=True, bins=100);\n", - "g.set(ylabel='PDF (FDR)')\n", - "\n", - "fig.legend()\n", - "plt.xlabel('R')\n", - "plt.title('Distribution of correlation coefficients')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also observe that the Bonferroni correction yields a more homogeneous number of associated features for each feature (i.e. first neighbors), compared to the FDR filtering. This has a consequence on the network structure." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "neighbor_number=pd.DataFrame()\n", - "for n_neighbors in np.arange(1,21):\n", - " padj_count=((Rmatrix_padj!=0).sum()==n_neighbors).sum()\n", - " fdr_count=((Rmatrix_fdr!=0).sum()==n_neighbors).sum()\n", - " \n", - " out=pd.Series([padj_count, fdr_count], index=['num_features(Padj)','num_features(FDR)'], name=n_neighbors)\n", - " neighbor_number=pd.concat([neighbor_number, out],1)\n", - " \n", - "neighbor_number=neighbor_number.T.rename_axis('num_neighbors').reset_index()\n", - "\n", - "fig,ax=plt.subplots(figsize=(16, 8), nrows=2)\n", - "ax=ax.flatten()\n", - "sns.lineplot(data=neighbor_number, x='num_neighbors', y='num_features(Padj)', color='black', label='Padj', ax=ax[0]);\n", - "sns.lineplot(data=neighbor_number, x='num_neighbors', y='num_features(FDR)', color='red', label='FDR', ax=ax[0]);\n", - "ax[0].set(ylabel='Number of features')\n", - "fig.suptitle('Number of features with <21 neighbors')\n", - "ax[0].set_xticks(np.arange(1,21,1));\n", - "\n", - "### boxplot requires a long df\n", - "feat_associations_padj=pd.concat([\n", - " shortPR.copy().loc[shortPR['R (padj)']!=0,][['feat1','feat2']],\n", - " shortPR.copy().loc[shortPR['R (padj)']!=0,][['feat2','feat1']].rename(columns={'feat1':'feat2','feat2':'feat1'})]).drop_duplicates().groupby('feat1').agg('count')\n", - "\n", - "feat_associations_fdr=pd.concat([\n", - " shortPR.copy().loc[shortPR['R (fdr)']!=0,][['feat1','feat2']],\n", - " shortPR.copy().loc[shortPR['R (fdr)']!=0,][['feat2','feat1']].rename(columns={'feat1':'feat2','feat2':'feat1'})]).drop_duplicates().groupby('feat1').agg('count')\n", - "\n", - "feat_associations=pd.concat([feat_associations_padj, feat_associations_fdr],1, sort=True)\n", - "feat_associations.fillna(0,inplace=True)\n", - "feat_associations.columns=['Padj','FDR']\n", - "\n", - "sns.boxplot( data=feat_associations, notch=True, ax=ax[1], orient='h', palette={'Padj':'black','FDR':'red'});\n", - "plt.title('Number of associations per feature')\n", - "fig.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that the Bonferroni correction leads to many nodes being associated with 1 or 2 other nodes, whereas the FDR correction leads to substantially higher number of associations for some of the nodes. This can also raise questions about the biological plausibility of such high number of associations - is it biologically significant that a gene is co-expressed with 800 other genes/metabolites?\n", - "\n", - "We can also be a bit more strict on the FDR that we consider as statistically significant. Let's compare the number of potential false positives at different FDR. The following plot further highlights an FDR = 0.01 (dashed gray line)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fdr_df=pd.DataFrame()\n", - "for fdr in np.append(np.geomspace(1e-4, 0.01, 30), [0.02, 0.03, 0.04, 0.05,0.1]):\n", - " out=pd.Series([fdr,(PRmatrix.Padjfdr,'R (fdr)']=0\n", - " \n", - " temp=temp.loc[temp.feat1!=shortPR.feat2]\n", - " \n", - " color={0.05:'gray',0.01:'green',0.001:'red',0.0001:'blue'}[fdr]\n", - "\n", - " \n", - " p=sns.histplot(\n", - " temp['R (fdr)'][temp['R (fdr)']!=0], color=color, label='FDR <'+str(fdr), kde=True, ax=ax, bins=50);\n", - " p.set(ylabel='PDF (FDR)')\n", - "\n", - "fig.legend()\n", - "plt.xlabel('R (FDR)');\n", - "plt.title('Distribution of correlation coefficients');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Predicably, by being more conservative we will be selecting higher absolute correlation coefficients. In this test case we are considering only 25 samples. Larger sample sizes lead to lower nominal and adjusted p-values, and a higher number of statistically significant but milder correlation coefficients. In such cases, one may be more conservative in the significance threshold. Henceforth, we will consider as statistically significant those edges where FDR < 0.01.\n", - "\n", - "One last point comes from the comparison of statistically significant associations within and between omics. Note how so few inter-omic associations are identified, and that Bonferroni correction completely misses any." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "bonferroni_significant=PRmatrix.copy().loc[PRmatrix.Padj<0.01].loc[:,['feat1','feat2','R','int_type']]\n", - "bonferroni_significant['sig_test']='bonferroni'\n", - "fdr_significant=PRmatrix.copy().loc[PRmatrix.FDR<0.01].loc[:,['feat1','feat2','R','int_type']]\n", - "fdr_significant['sig_test']='FDR'\n", - "\n", - "pd.concat([bonferroni_significant,fdr_significant]).groupby(['sig_test','int_type'])['R'].agg('count').reset_index()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Questions\n", - "\n", - "In building a graph from an association analysis:\n", - "- Why do you think that most significant correlations are found only within each omic? \n", - "- How will you deal with the positive and negative sets of correlations above?\n", - "- Will you consider the network as weighted? Directed?\n", - "- Which dataset would you select for further analysis: the Bonferroni or the FDR-corrected? Why?\n", - "- What preliminary tests would you perform on the graph to assess whether node relationships are random?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Network construction and preliminary analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now build 4 different networks to analyse further:\n", - "- A full association network filtered using FDR-corrected P values (<0.01). This is an unweighted network.\n", - "- The subset of positively associated features, where correlation coefficient is used as weight.\n", - "- kNN-G that we will generate from the expression profile. This will be unweighted.\n", - "- A random network based on the [Erdos-Renyi model](https://en.wikipedia.org/wiki/Erd%C5%91s%E2%80%93R%C3%A9nyi_model), with the same node and edge number of each network. \n", - "\n", - "This will be a null-model for our analyses. The idea is that if a certain property found in one of our graphs is reproduced in a random graph, then we do not need to account for any other possible explanations for that feature. In other words, if a property of a graph (e.g. clustering) is not found in a random network, we can assume that it does not appear in our biological network due to randomness." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Prepares table for being read by igraph\n", - "PRmatrix=pd.read_csv('data/association_matrix.tsv', sep=\"\\t\")\n", - "PRmatrix.loc[PRmatrix['FDR']>0.01,'R (fdr)']=0\n", - "PRmatrix=PRmatrix.loc[PRmatrix['R (fdr)']!=0,['feat1','feat2','R (fdr)']]\n", - "PRmatrix=PRmatrix.loc[PRmatrix.feat1!=PRmatrix.feat2] #drops self correlations\n", - "\n", - "fdr_pos_mat=PRmatrix.loc[PRmatrix['R (fdr)']>0]\n", - "fdr_neg_mat=PRmatrix.loc[PRmatrix['R (fdr)']<0]\n", - "\n", - "PRmatrix=PRmatrix.loc[PRmatrix.isin(pd.unique(fdr_pos_mat[['feat1','feat2']].values.flatten())).sum(1)==2,] #selects only nodes also found in the positive network so that we can compare networks of the same sizes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now build the kNNG, using distances as input to determine the nearest neighbours. Because this data contains both gene expressions and metabolite quantifications, we need to normalize them beforehand. (We didn't need to do this above as we were comparing ranks)\n", - "\n", - "We start by standardizing all features" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Imports and normalizes met and gene data so that we can compute similarities between them\n", - "data=pd.read_csv('data/met_genes.tsv', sep=\"\\t\", index_col=0)\n", - "\n", - "scaled_data=pd.DataFrame(StandardScaler().fit_transform(data.loc[:,data.columns!='Type'].T).T, columns=data.columns[data.columns!='Type'], index=data.index)\n", - "scaled_data_values=scaled_data.copy()\n", - "scaled_data['Type']=data.Type\n", - "\n", - "#Plots the data distribution\n", - "fig,ax=plt.subplots(figsize=(12,8))\n", - "sns.kdeplot(\n", - " scaled_data.loc[scaled_data.Type=='met',scaled_data.columns!='Type'].values.flatten(), \n", - " color='r', label='Mets', ax=ax, legend=False)\n", - "ax2=ax.twinx()\n", - "sns.kdeplot(\n", - " scaled_data.loc[scaled_data.Type=='genes',scaled_data.columns!='Type'].values.flatten(),\n", - " color='b', label='Genes', ax=ax2, legend=False)\n", - "fig.suptitle('Distribution for met and genes');\n", - "fig.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now generate the graphs from the dataframes above" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "### Generating the kNN graph\n", - "#Computes a kNN adjacency matrix from the input dataset\n", - "#and prepares table for being read by igraph\n", - "input_ds=scaled_data_values.loc[scaled_data_values.index.isin(pd.unique(fdr_pos_mat[['feat1','feat2']].values.flatten()))]\n", - "knnG=sklearn.neighbors.kneighbors_graph(input_ds.values, 200, metric='euclidean')\n", - "knnG=pd.DataFrame(knnG.toarray(), columns=input_ds.index.copy(), index=input_ds.index.copy()) #adjacency matrix\n", - "knnG.index.name='gene1'\n", - "knnG.columns.name='gene2'\n", - "knnG=knnG.stack().reset_index().rename(columns={0:'Connectivity'})\n", - "knnG=knnG.loc[knnG['Connectivity']!=0]\n", - "\n", - "### Generates each of the graphs\n", - "#positive associations, weighted\n", - "pos_w=ig.Graph.TupleList([tuple(x) for x in fdr_pos_mat.values], directed=False, edge_attrs=['w'])\n", - "\n", - "#full network, unweighted\n", - "edge_list=PRmatrix.copy().loc[PRmatrix.isin(pd.unique(fdr_pos_mat[['feat1','feat2']].values.flatten())).sum(1)==2,['feat1','feat2']].values\n", - "all_u=ig.Graph.TupleList([tuple(x) for x in edge_list], directed=False)\n", - "\n", - "#knnG, unweighted\n", - "knn=ig.Graph.TupleList([tuple(x) for x in knnG.values], directed=False)\n", - "\n", - "#random network, unweighted, node and edge number based on a network of the same size\n", - "random_posw=ig.Graph.Erdos_Renyi(n=input_ds.shape[0], m=len(fdr_pos_mat.values), directed=False, loops=False)\n", - "\n", - "random_allu=ig.Graph.Erdos_Renyi(n=input_ds.shape[0], m=len(edge_list), directed=False, loops=False)\n", - "\n", - "random_knn=ig.Graph.Erdos_Renyi(n=input_ds.shape[0], m=len(knnG.values), directed=False, loops=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For representation purposes we will see how a short knn graph looks - be careful in drawing the others, as they have many more edges it becomes computationally heavy." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "#random subset of knn graph for plotting\n", - "short_knn=ig.Graph.TupleList([tuple(x) for x in knnG.values[random.sample(list(np.arange(len(knnG.values))), 5000)]], directed=False)\n", - "\n", - "#This plots each graph, using degree to present node size:\n", - "short_knn.vs['degree']=short_knn.degree() \n", - "short_knn.vs['degree_size']=[(x*15)/(max(short_knn.vs['degree'])) for x in short_knn.vs['degree']] #degree is multiplied by 10 so that we can see all nodes\n", - "\n", - "layout = short_knn.layout_mds()\n", - "ig.plot(short_knn, layout=layout, vertex_color='white', edge_color='silver', vertex_size=short_knn.vs['degree_size'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the next table, we can see that while the same number of nodes is found in all networks, the number of edges varies greatly. We also see that the network is fully connected, which is not allways the case. If it wasn't connected, we could select the ***k*** largest connected components, and proceed the analyses with them. The largest connected component is called the *giant component*." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#function to get graph properties, takes a few minutes to run\n", - "def graph_prop(input_graph):\n", - " ncount=nn.vcount()\n", - " ecount=nn.ecount()\n", - " diameter=nn.diameter()\n", - " av_path=nn.average_path_length()\n", - " dens=nn.density()\n", - " clustering=nn.transitivity_undirected() #this is the global clustering coefficient\n", - " conn=nn.is_connected()\n", - " min_cut=nn.mincut_value()\n", - " out=pd.DataFrame([ncount, ecount, diameter, av_path, dens, clustering, conn, min_cut],\n", - " index=['node_count','edge_count','diameter','av_path_length','density','clustering_coef','connected?','minimum_cut']).T\n", - " return(out)\n", - "\n", - "#summarizing graph properties\n", - "network_stats=pd.DataFrame()\n", - "for nn in [pos_w, all_u, knn, random_posw, random_allu, random_knn]:\n", - " network_stats=pd.concat([network_stats,graph_prop(nn)])\n", - " \n", - "network_stats.index=['pos_w','all_u','knn','pos_w_random', 'all_u_random', 'knn_random']\n", - "network_stats" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Questions:\n", - "- Why is the diameter and average path length lower in the case of the full network and the random network, compared to the other two networks? What about the other random networks?\n", - "- Why do you think the clustering coefficient is lower for the knn compared with the other networks?\n", - "- Why is the minimum cut much larger in the random network compared to the others?\n", - "- How do you think the selected *k* would influence the properties above for kNNG?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Centrality analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll look into different centrality measures:\n", - "- [Degree](https://en.wikipedia.org/wiki/Degree_(graph_theory)) - number of neighbors of a node\n", - "- [Betweenness](https://en.wikipedia.org/wiki/Betweenness_centrality) - measures how many shortest paths in the network pass through a node.\n", - "- [Closeness](https://en.wikipedia.org/wiki/Centrality#Closeness_centrality) - the average length of the shortest paths between a node and all other nodes \n", - "- [Eccentricity](https://en.wikipedia.org/wiki/Distance_(graph_theory)) - largest shortest path from a node to any other node. Nodes with high eccentricity tend to be on the periphery.\n", - "- [Eigenvector centrality](https://en.wikipedia.org/wiki/Eigenvector_centrality) - a node is more central if its neighbors show a high degree.\n", - "\n", - "Degree, Betweenness, Closeness and Eigenvector centralities may be additionally computed for the positive association network by taking into account each edge's weight. For instance, for degree this is done for each node by summing each edge's degree.\n", - "\n", - "Because the number of shortest paths in a network scales with the network size, we normalize Eccentricity and Betweenness with respect to the network size so that they can be compared between the four networks above.\n", - "\n", - "Note that many [other centrality metrics](https://en.wikipedia.org/wiki/Centrality) can be computed. For instance, [PageRank](https://en.wikipedia.org/wiki/PageRank) and [HITS](https://en.wikipedia.org/wiki/HITS_algorithm) take into account edge directionality to compute what are the most central nodes in a network. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Degree distribution** \n", - "Let's start by comparing the degrees of the random network against the three other networks. From the figures below it seems that there is no relationship between the degree of the random network, and any of the three others." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axes=plt.subplots(nrows=1, ncols=3, figsize=(15,5))\n", - "\n", - "for i, ax in zip(range(4), axes.flat):\n", - " sns.regplot(x=[pos_w,all_u,knn][i].degree(), y=[random_posw, random_allu, random_knn][i].degree(), \n", - " ax=ax, color=['blue','lightsalmon','green'][i],\n", - " line_kws={'color':'black'}\n", - " );\n", - " ax.set_title(['pos','all','knn'][i])\n", - " ax.set(xlabel='k ('+['pos','all','knn'][i]+')', ylabel='k (random)')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "def transform_degree(graph):\n", - " alldegs=graph.degree()\n", - " alldegs=pd.DataFrame([[key,len(list(group))] for key,group in itertools.groupby(alldegs)], columns=['k','count'])\n", - " alldegs['P(k)']=[x/alldegs['count'].sum() for x in alldegs['count']]\n", - " alldegs=alldegs.loc[:,['k','P(k)']]\n", - " alldegs.drop_duplicates(inplace=True)\n", - " alldegs.reset_index(drop=True, inplace=True)\n", - " return(alldegs)\n", - "\n", - "\n", - "fig, ax = plt.subplots(figsize=(7, 7))\n", - "# ax.set(yscale='log', xscale='log')\n", - "p=sp.stats.probplot(pos_w.degree(), plot=ax)\n", - "a=sp.stats.probplot(all_u.degree(), plot=ax)\n", - "k=sp.stats.probplot(knn.degree(), plot=ax)\n", - "r=sp.stats.probplot(random_posw.degree(), plot=ax)\n", - "r2=sp.stats.probplot(random_allu.degree(), plot=ax)\n", - "r3=sp.stats.probplot(random_knn.degree(), plot=ax)\n", - "\n", - "col=['blue','','peru','','green','','lightblue','','lightsalmon','','greenyellow','']\n", - "for x in np.arange(0,11,2):\n", - " ax.get_lines()[x].set_markerfacecolor(col[x])\n", - " ax.get_lines()[x].set_markeredgewidth(0)\n", - " ax.get_lines()[x+1].set_color(col[x])\n", - "\n", - "\n", - "fig.legend(labels=['pos','pos','all','all','knn','knn','pos_random','pos_random','all_random','all_random','knn_random','knn_random']);\n", - "\n", - "ax.set(xlabel='Data quantiles', ylabel='observed degree (k)')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Centrality** \n", - "We will now compare different centrality metrics between the graphs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "centralities_list=['degree (larger ~ central)','eccentricity (smaller ~ central)','betweenness (larger ~ central)', 'closeness (larger ~ central)','eigenvector (larger ~ central)']\n", - "\n", - "def combine_raw_centralities():\n", - " \"\"\"\n", - " Combines centrality metrics\n", - " \"\"\"\n", - " def centrality_raw(input_graph, graph_name):\n", - " \"\"\"\n", - " Computes centrality metrics for a graph.\n", - " \"\"\"\n", - " deg=input_graph.degree(loops=False)\n", - " node_n=input_graph.vcount()\n", - " #scaled to account for network size\n", - " ecc=[(2*x / ((node_n-1)*(node_n-2))) for x in input_graph.eccentricity()]\n", - " btw=[(2*x / ((node_n-1)*(node_n-2))) for x in input_graph.betweenness(directed=False)] \n", - " eig=input_graph.eigenvector_centrality(directed=False, scale=False)\n", - " \n", - " # For disconnected graphs, computes closeness from the largest connected component\n", - " if(input_graph.is_connected()):\n", - " cls=input_graph.closeness(normalized=True)\n", - " else:\n", - " cls=input_graph.clusters(mode='WEAK').giant().closeness(normalized=True)\n", - " \n", - " out=pd.DataFrame([deg, ecc, btw, cls,eig], index=centralities_list).T\n", - " out['graph']=graph_name\n", - " out=out.loc[:,np.append(['graph'],out.columns[out.columns!='graph'])]\n", - " \n", - " ##Adds centralities for each node in the network\n", - " input_graph.vs['degree']=out['degree (larger ~ central)']\n", - " input_graph.vs['eccentricity']=out['eccentricity (smaller ~ central)']\n", - " input_graph.vs['betweenness']=out['betweenness (larger ~ central)']\n", - " input_graph.vs['closeness']=out['closeness (larger ~ central)']\n", - " input_graph.vs['eigenvector']=out['eigenvector (larger ~ central)']\n", - " \n", - " return(out)\n", - " \n", - " #Computes centralities for all networks\n", - " network_centralities_raw=pd.DataFrame()\n", - " network_centralities_raw=pd.concat([network_centralities_raw,centrality_raw(pos_w,'pos_w')])\n", - " network_centralities_raw=pd.concat([network_centralities_raw,centrality_raw(all_u,'all_u')])\n", - " network_centralities_raw=pd.concat([network_centralities_raw,centrality_raw(knn,'knn')])\n", - " network_centralities_raw=pd.concat([network_centralities_raw,centrality_raw(random_posw,'pos_w_random')])\n", - " network_centralities_raw=pd.concat([network_centralities_raw,centrality_raw(random_allu,'all_u_random')])\n", - " network_centralities_raw=pd.concat([network_centralities_raw,centrality_raw(random_knn,'knn_random')])\n", - " return(network_centralities_raw)\n", - "\n", - "network_centralities_raw=combine_raw_centralities()\n", - "\n", - "\n", - "fig, axes=plt.subplots(nrows=5, figsize=(16,20), sharey='row')\n", - "for i, ax in zip(range(6), axes.flat):\n", - " sns.boxplot(\n", - " data=network_centralities_raw, x='graph', y=centralities_list[i], notch=True, ax=ax, showfliers=False)\n", - " ax.set_title(centralities_list[i])\n", - " ax.set(xlabel='')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When interpreting the results above, it is important to bear in mind that these networks have different network sizes. Recall:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "network_stats" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Overall, we see:\n", - "- The median degree centrality decreases from `Full network > kNNG > Positive assoc. network`.\n", - "- The median betweenness centrality tends to decrease from `Positive assoc. network > Full network > kNNG`.\n", - "- The median closeness centrality tends to decrease from `Full network > kNNG > Positive assoc. network`.\n", - "- The eccentricity is very homogeneous for the `Full network (all_u)`, and slightly lower than in the `Full network`. In turn, most nodes in the `kNNG` tend to display an eccentricity of 3-4.\n", - "\n", - "\n", - "### Questions:\n", - "- Can you explain these observations?\n", - "- Based on the plots above, which graphs do you think follow a [*small world*](https://en.wikipedia.org/wiki/Small-world_network) behavior?\n", - "\n", - "We will also explore the relationships between different centrality metrics. Because these have different interpretations, we will compute ranks for each centrality, and perform the correlations on the ranks. In the following cell we do this, and then compute correlations within the 5 metrics for the full network. One additional column is presented (`median_centrality`), that is basically the median of the ranks of the 5 other centralities. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "### Compute centrality ranks so that we can compare them within and between networks\n", - "full_centralities=pd.DataFrame()\n", - "for net in [0,1,2]:\n", - " net_in=[pos_w, all_u, knn][net]\n", - " net_nm=['pos_w', 'all_u', 'knn'][net]\n", - " temp=pd.DataFrame([net_in.vs[att] for att in ['name','degree','betweenness', 'closeness','eccentricity','eigenvector']], index=['name','degree','betweenness', 'closeness','eccentricity','eigenvector']).T\n", - " temp.columns=[x+'|'+net_nm for x in temp.columns]\n", - " temp.rename(columns={'name|'+net_nm:'name'}, inplace=True)\n", - " \n", - " ## For all but eccentricity centrality, we compute the rank in ascending mode\n", - " ## so that higher ranking means more central. we need to reverse this for eccentricity\n", - " temp.loc[:,temp.columns.str.contains('deg|bet|clos|eig')]=temp.loc[:,temp.columns.str.contains('deg|bet|clos|eig')].rank(pct=True, ascending=True)\n", - " temp.loc[:,temp.columns.str.contains('eccentricity')]=temp.loc[:,temp.columns.str.contains('eccentricity')].rank(pct=True, ascending=False\n", - " )\n", - " temp['median_centrality|'+net_nm]=temp.loc[:,temp.columns!='name'].median(1)\n", - " if(net==0):\n", - " full_centralities=temp\n", - " else:\n", - " full_centralities=pd.merge(full_centralities, temp, on='name')\n", - "full_centralities.set_index('name', inplace=True)\n", - "full_centralities=pd.merge(full_centralities, data[['Type']], left_index=True, right_index=True, how='left')\n", - "full_centralities['median|ALL']=full_centralities.loc[:,full_centralities.columns.str.contains('median')].median(1)\n", - "\n", - "\n", - "### Correlations are computed between ranks, after inverting the rank for eccentricity\n", - "def correlations_centralities(graph_name):\n", - " \"\"\"\n", - " Returns squared correlation matrix.\n", - " \"\"\"\n", - " temp_corr=full_centralities.copy().loc[:,full_centralities.columns!='Type'].dropna().astype('float')\n", - " temp_corr=temp_corr.loc[:,temp_corr.columns.str.contains(graph_name)]\n", - " temp_corr.columns=temp_corr.columns.str.replace('\\|.+','')\n", - " temp_corr=temp_corr.corr(method='spearman')\n", - " np.fill_diagonal(temp_corr.values, np.nan)\n", - " return(temp_corr)\n", - "\n", - "all_u_centcorr=correlations_centralities('all_u')\n", - "knn_centcorr=correlations_centralities('knn')\n", - "pos_w_centcorr=correlations_centralities('pos_w')\n", - "\n", - "fig,ax=plt.subplots(nrows=3, figsize=(8,16), sharex=True)\n", - "ax=ax.flatten()\n", - "for i in range(3):\n", - " tdata=[all_u_centcorr,knn_centcorr, pos_w_centcorr][i]\n", - " tname=['all_u','knn','pos_w'][i]\n", - " sns.heatmap(tdata, cmap=\"RdBu_r\", center=0, annot=True, ax=ax[i]);\n", - " ax[i].set(title=tname)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The plot above shows that most of the centrality metrics are positively correlated in the full network and in the positively coexpression network. However, this is not the case for the KNN network.\n", - "\n", - "#### Questions\n", - "\n", - "1. How do you explain the inverse relationship between degree and most other metrics in the KNN network?\n", - "2. Why do you think that the KNN network specifically displays this opposite trend?\n", - "\n", - "The next figure may help in answering the question above. We highlight the most central nodes based on degree (red) and eccentricity (green), in addition to a random subset of 500 nodes. Of these, which do you think displays the shortest path to all other nodes?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "knn_centralities=full_centralities.loc[:,full_centralities.columns.str.contains('knn')]\n", - "knn_centralities=knn_centralities.loc[:,knn_centralities.columns!='median_centrality|knn']\n", - "knn_centralities.columns=knn_centralities.columns.str.replace('\\\\|.+','')\n", - "\n", - "##top nodes based on eccentricity\n", - "knn_top_ecc=knn_centralities['eccentricity'].sort_values(ascending=False).index.values[:1]\n", - "\n", - "##top nodes based on degree\n", - "knn_top_deg=knn_centralities['degree'].sort_values(ascending=False).index.values[:1]\n", - "\n", - "##random nodes to help visualize\n", - "##warning, do not increase this value much higher than 500, or you may have problems rendering this image\n", - "knn_others=knn_centralities.sample(500).index.values\n", - "\n", - "## full list\n", - "node_list=np.append(np.append(knn_top_deg, knn_top_ecc), knn_others)\n", - "\n", - "## subsets nodes\n", - "knn_to_draw=knn.subgraph(knn.vs.select([x.index for x in knn.vs if x['name'] in node_list]))\n", - "knn_to_draw.vs['color']=['red' if x['name'] in knn_top_deg else 'green' if x['name'] in knn_top_ecc else 'white' for x in knn_to_draw.vs]\n", - "\n", - "layout = knn_to_draw.layout_auto()\n", - "ig.plot(knn_to_draw, layout=layout, vertex_color=knn_to_draw.vs['color'], edge_color='silver', vertex_size=7)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Community analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Node communities may be identified based on different metrics including [Modularity](https://en.wikipedia.org/wiki/Modularity_(networks)) or [Density](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.84.016114). We will look at community detection through modularity." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Modularity of a small graph\n", - "\n", - "Recall that the Modularity of a community is given by $$Q = \\frac{1}{2m} \\sum_{c}(e_c - \\frac{K_c^2}{2m})$$\n", - "\n", - "where $e_c$ is the number of edges in community $c$, and $\\frac{K_c^2}{2m}$ is the expected number of edges in the community given the $K_c$ sum of degrees of its nodes, for a network with $m$ edges. This will correspond to\n", - "\n", - "$$Q = \\frac{1}{2m} \\sum_{ij}[A_{ij} - \\frac{k_i k_j}{2m}\\delta(c_i,c_j)]$$\n", - "\n", - "where $A_ij$ is the Adjacency between nodes $i$ and $j$, $k_i$ and $k_j$ are their degree, and $\\delta(c_i,c_j)$ is the [Kronecker delta](https://en.wikipedia.org/wiki/Kronecker_delta), defined as 1 if nodes $i$ and $j$ are in the same community, or 0 if they are not. Let's examine the following small network, with communities given by the two colors: red `[A,B,C,D,H]` and blue `[E,F,G]`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "g = ig.Graph([(0,1), (0,2), (1,2), (0,3), (2,3), (1,3), (2,4), (4,5), (5,6), (4,6), (4,7), (5,7), (6,7)])\n", - "\n", - "g.vs[\"name\"] = [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\",\"H\"]\n", - "g.vs[\"module\"] = [\"f\", \"f\", \"f\", \"f\", \"m\", \"m\",\"m\", \"f\"]\n", - "\n", - "\n", - "layout = g.layout_circle()\n", - "g.vs[\"label\"] = g.vs[\"name\"]\n", - "color_dict = {\"m\": \"cyan\", \"f\": \"pink\"}\n", - "g.vs[\"color\"] = [color_dict[module] for module in g.vs[\"module\"]]\n", - "ig.plot(g, layout = layout, bbox = (300, 300), margin = 50)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this network, we have the following adjacency matrix." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pd.DataFrame( g.get_adjacency(), index=g.vs[\"name\"], columns=g.vs[\"name\"] )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will compute the modularity of this network given the 2 communities above, herein identified as communities `1` and `2`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def modularity(graph, membership):\n", - " \"\"\"\n", - " Computes the modularity of `graph` for a given module `membership`.\n", - " \"\"\"\n", - " m=len(graph.es.indices) #edge number\n", - " A=pd.DataFrame(graph.get_adjacency()) #adjacency matrix\n", - " Q=[] \n", - " for i in A.index:\n", - " for j in A.columns:\n", - " if(membership[i]==membership[j]):\n", - " deltaij=1\n", - " else:\n", - " deltaij=0\n", - " Q=np.append(Q, (A.loc[i,j]-(g.vs[i].degree()*g.vs[j].degree())/(2*m))*deltaij)\n", - " Q=(1/(2*m))*np.sum(Q)\n", - " out=Q\n", - " return(out)\n", - "\n", - "modularity(g, [1,1,1,1,2,2,2,1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As this is a very small network, we can take a brute-force approach and examine all possible membership combinations that will yield the highest possible modularity." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "all_memberships=[x for x in itertools.product([1,2], repeat=8)]\n", - "\n", - "\n", - "membership_modularity=pd.DataFrame()\n", - "for membership in all_memberships:\n", - " group1=[g.vs['name'][x] for x in range(len(g.vs['name'])) if membership[x]==1]\n", - " group2=[g.vs['name'][x] for x in range(len(g.vs['name'])) if membership[x]==2]\n", - " out=pd.Series([group1, group2, membership, modularity(g, membership)], index=['comm1', 'comm2','memb','Q'])\n", - " membership_modularity=pd.concat([membership_modularity, out], 1)\n", - " \n", - "membership_modularity=membership_modularity.T.sort_values('Q', ascending=False)\n", - "membership_modularity.head(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the 2 top hits that maximize modularity define the same communities as `[A,B,C,D]` and `[E,F,G,H]`. For larger networks we cannot use a brute-force approach, and instead rely on the 2-pass Louvain algorithm, which has since been improved with the Leiden algorithm." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Modularity of gene-metabolite networks" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below, we perform the community analysis on the 4 networks. We will perform one additional community analysis by considering the edge weights from the positively associated network. Importantly, this method searches for the largest possible communities for our network, which may not always be the desired. Alternative models such as the [Constant Potts Model](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.84.016114) allow you to identify smaller communities. Should we know that our data has special feature classes, we can compare whether the communities identify those classes by examining them individually, and increasing the resolution if needed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pos_comm = leidenalg.find_partition(pos_w, leidenalg.ModularityVertexPartition)\n", - "pos_w_comm = leidenalg.find_partition(pos_w, leidenalg.ModularityVertexPartition, weights='w')\n", - "all_comm = leidenalg.find_partition(all_u, leidenalg.ModularityVertexPartition)\n", - "knn_comm = leidenalg.find_partition(knn, leidenalg.ModularityVertexPartition)\n", - "random_all = leidenalg.find_partition(random_allu, leidenalg.ModularityVertexPartition)\n", - "random_posw = leidenalg.find_partition(random_posw, leidenalg.ModularityVertexPartition)\n", - "random_knn = leidenalg.find_partition(random_knn, leidenalg.ModularityVertexPartition)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Predictably, we can see that the modularity score of any of the networks is substantially larger than that of the random network." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.round(pos_comm.modularity,3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.round(pos_w_comm.modularity,3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.round(all_comm.modularity,3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.round(knn_comm.modularity,3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.round(random_all.modularity,3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.round(random_posw.modularity,3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.round(random_knn.modularity,3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Comparing the different communities by size:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "code_folding": [] - }, - "outputs": [], - "source": [ - "#Compiles feat lists per community\n", - "def get_community_table():\n", - " comm_counts=pd.DataFrame()\n", - " feat_lists=pd.DataFrame()\n", - " for i in [0,1,2,3]:\n", - " graph=[pos_w,pos_w,all_u,knn][i]\n", - " comm=[pos_comm,pos_w_comm,all_comm,knn_comm][i]\n", - " name=['pos','pos_w','all','knn'][i]\n", - " temp=pd.DataFrame(list(zip(graph.vs['name'],[x+1 for x in comm.membership]))).rename(columns={0:'feat',1:'community'})\n", - " counts=pd.DataFrame(temp.groupby('community')['feat'].agg(len))\n", - " counts.columns=[name]\n", - " comm_counts=pd.concat([comm_counts, counts],1)\n", - " \n", - " gl=pd.DataFrame(temp.groupby('community')['feat'].apply(list)).reset_index()\n", - " gl['community']=['c'+str(i) for i in gl['community']]\n", - " gl['network']=name\n", - " gl=gl.loc[:,['network','community','feat']]\n", - " feat_lists=pd.concat([feat_lists, gl])\n", - " \n", - " comm_counts.index=['c'+str(i) for i in comm_counts.index]\n", - " return([comm_counts,feat_lists])\n", - "\n", - "#Plotting community sizes\n", - "import seaborn as sns\n", - "fig, ax = plt.subplots(figsize=(7, 4))\n", - "bar_data=get_community_table()[0].fillna(0).T\n", - "bar_data.plot(kind='bar', stacked=True, ax=ax);\n", - "# ## number of communities in each\n", - "# for index, row in groupedvalues.iterrows():\n", - "# g.text(row.name,row.tip, round(row.total_bill,2), color='black', ha=\"center\")\n", - "ax.legend(get_community_table()[0].index, loc='right', bbox_to_anchor=(1.15, 1));\n", - "ax.set_title('Community size')\n", - "plt.xticks(rotation=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "scrolled": false - }, - "source": [ - "Some of the communities are very small in the `pos` and full networks, and comprise only 2 and 3 elements. Can we really call this a community?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(7, 4))\n", - "sns.barplot(\n", - " data=bar_data.T.unstack().reset_index().rename(columns={'level_0':'network','level_1':'community',0:'size'}),\n", - " x='network',y='size', hue='community'\n", - " )\n", - "\n", - "ax.set(yscale='log');\n", - "ax.legend(loc='right', bbox_to_anchor=(1.15, 1));\n", - "ax.set_title('Community size')\n", - "plt.xticks(rotation=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Functional analysis** \n", - "In order to perform functional enrichment, we will extract each of the communities and perform a hypergeometric test **on the genes** to understand whether they are particularly enriched in specific biological functions. \n", - "We will use [enrichr](https://gseapy.readthedocs.io/en/master/gseapy_example.html#2.-Enrichr-Example) to perform the gene set enrichment analysis. As background we will use the full list of genes that were quantified.\n", - "\n", - "We will look at 3 gene set libraries. Should you have other kinds of data, enrichr allows you to define your own feature sets and perform a similar analysis. The challenge is in identifying comprehensive and well curated gene sets." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#All the available human libraries\n", - "gp.get_library_name(database='Human')[:15]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "code_folding": [], - "scrolled": true - }, - "outputs": [], - "source": [ - "#we will search 3 libraries for significantly enriched gene sets\n", - "gene_sets=['GO_Biological_Process_2018','KEGG_2019_Human','OMIM_Disease']\n", - "background=[x for x in all_u.copy().vs['name'] if x in data.loc[data.Type=='genes'].index]\n", - "all_genes=data.loc[data.Type=='genes'].index\n", - "\n", - "def perform_enrich(network):\n", - " temp=get_community_table()[1].copy()\n", - " temp=temp.loc[temp['network']==network]\n", - " output_enrichr=pd.DataFrame()\n", - " for comm in temp['community'].values:\n", - " gl=list(temp.loc[temp['community']==comm, 'feat'])[0]\n", - " gl=list([x for x in gl if x in all_genes])\n", - " \n", - " if(len(gl)<30):\n", - " continue\n", - " print('Found '+str(len(gl))+' genes in community '+comm)\n", - " for bp in gene_sets:\n", - " print('Analyzing '+network+' network | Comm: '+comm+'/'+str(len(temp.index))+' | BP: '+bp)\n", - " enr=gp.enrichr(\n", - " gene_list=gl,\n", - " gene_sets=bp,\n", - " background=background,\n", - " outdir='Enrichr',\n", - " format='png'\n", - " )\n", - "\n", - " results=enr.results.sort_values('Adjusted P-value', ascending=True)\n", - " results=results.loc[results['Adjusted P-value']<0.05,]\n", - " results['BP']=bp\n", - " results['Comm']=comm\n", - " results['Graph']=network\n", - " output_enrichr=pd.concat([output_enrichr, results])\n", - " \n", - " return(output_enrichr)\n", - "\n", - "all_enriched=pd.DataFrame()\n", - "for net in ['pos', 'pos_w', 'all', 'knn']: \n", - " all_enriched=pd.concat([all_enriched,perform_enrich(net)])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the output of the cell above you can readily see that some communities such as `c1` display no enriched terms from either [GO](http://geneontology.org/), [KEGG](https://www.genome.jp/kegg/) or [OMIM](https://www.omim.org/).\n", - "\n", - "Running the command above not only gives you the results after significance testing (Q<0.05), but it also outputs some preliminary barplots with the statistically significant results (found under `/Enrichr/`). For instance:\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "enriched_terms=all_enriched.loc[:,['Graph','Comm','Term','Adjusted P-value']].copy()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "enriched_terms['Adjusted P-value']=-1*np.log10(enriched_terms['Adjusted P-value'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "enriched_terms.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(7, 4))\n", - "data_bars=pd.DataFrame(enriched_terms.groupby(['Graph','Comm'])['Term'].agg('count')).stack().reset_index().rename(columns={0:'Count'})\n", - "sns.barplot(x='Graph', y='Count', data=data_bars, hue='Comm')\n", - "ax.set_title('Number of significant Terms (Q < 0.05) per community')\n", - "ax.legend(loc='right', bbox_to_anchor=(1.15, 1));\n", - "plt.xticks(rotation=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that some of these communities are very big, which explains the big number of biological processes found above." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "###Number of genes/community\n", - "# We skipped communities with <30 genes\n", - "get_community_table()[0].fillna(0).T" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pd.DataFrame(enriched_terms.groupby(['Graph','Comm'])['Term'].agg('count'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now find whether the Full Network, Positively associated, and Positively associated weighted, show any common terms among their biggest communities. We do not compare with kNN-G as this shows very homogeneous and different communities than the other two networks" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Finding consensus\n", - "temp=enriched_terms.copy()\n", - "temp['comm_term']=temp.Comm+'_'+temp.Term\n", - "temp=temp.loc[:,['Graph','comm_term']]\n", - "\n", - "consensus=pd.DataFrame()\n", - "consensus=pd.concat([consensus, temp.loc[temp['Graph']=='pos']])\n", - "consensus=pd.merge(consensus,\n", - " temp.loc[temp['Graph']=='pos_w'], on=\"comm_term\", how='outer', suffixes=['pos','pos_w'])\n", - "consensus=pd.merge(consensus, \n", - " temp.loc[temp['Graph']=='all'], on=\"comm_term\", how='outer').rename(columns={'Graph':'all'})\n", - "\n", - "consensus=consensus.loc[consensus.isna().sum(1)==0].loc[:,['comm_term','Graphpos','Graphpos_w','all']]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Among the biggest communities we find several biological processes (55) that are simultaneously identified in the same community in the three graphs (`Full`, `Pos assoc`, and `Pos assoc weighted`)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "consensus['comm']=[x[0] for x in consensus.comm_term.str.split('\\_')]\n", - "consensus.groupby('comm')['comm_term'].agg('count')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Questions** \n", - "- Would you exclude any communities based on its size?\n", - "- Having identified these communities, how would you try to validate them?\n", - "- Would you now determine the relevant community to investigate further?\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you want to export communities to use them in other sections." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Requires running the community detection from the previous section\n", - "patlas=pd.read_csv('data/proteinatlas.tsv', sep=\"\\t\").loc[:,['Ensembl','Gene']]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "code_folding": [] - }, - "outputs": [], - "source": [ - "#Compiles gene lists per community. We need Ensembl ids for further analyses\n", - "def get_ensembl():\n", - " comm_counts=pd.DataFrame()\n", - " gene_lists=pd.DataFrame()\n", - " for i in [0,1,2,3]:\n", - " graph=[pos_w,pos_w,all_u,knn][i]\n", - " comm=[pos_comm,pos_w_comm,all_comm,knn_comm][i]\n", - " name=['pos','pos_w','all','knn'][i]\n", - " temp=pd.DataFrame(list(zip(graph.vs['name'],[x+1 for x in comm.membership]))).rename(columns={0:'gene',1:'community'})\n", - "\n", - " gl=pd.DataFrame(temp.groupby('community')['gene'].apply(list)).reset_index()\n", - " gl['community']=['c'+str(i) for i in gl['community']]\n", - " gl['network']=name\n", - " gl=gl.loc[:,['network','community','gene']]\n", - " gene_lists=pd.concat([gene_lists, gl])\n", - "\n", - " gene_communities=gene_lists\n", - " gene_mat=pd.DataFrame()\n", - " for net in gene_communities['network'].unique():\n", - " temp=gene_communities.copy().loc[gene_communities['network']==net,]\n", - " for comm in temp['community'].unique():\n", - " gl=list(temp.copy().loc[temp['community']==comm,'gene'])[0]\n", - " el=[patlas.loc[patlas['Gene']==x,'Ensembl'].iloc[0] for x in gl if x in patlas['Gene'].values]\n", - "\n", - " df=pd.DataFrame([net,comm,gl,el]).T\n", - " df.columns=['network','community','Gene','Ensembl']\n", - " gene_mat=pd.concat([gene_mat, df])\n", - " \n", - " return(gene_mat)\n", - "\n", - "get_ensembl().to_csv('data/gene_communities.tsv', sep=\"\\t\", index=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Conclusion" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we've performed some network analyses based on a met-gene association network. We've explored different centrality measures to characterize the networks, and identified the communities of genes in these networks. We have also used gene set enrichment analysis to characterize these communities based on the genes, but it remains to show whether similar results would be attained if we considered the metabolites in each community. " - ] - } - ], - "metadata": { - "hide_input": false, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.2" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": true, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/session_topology/lab/topology_lab_part1.ipynb b/session_topology/lab/topology_lab_part1.ipynb new file mode 100644 index 00000000..75705916 --- /dev/null +++ b/session_topology/lab/topology_lab_part1.ipynb @@ -0,0 +1,2194 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Network topology lab\n", + "\n", + "- current lecturer: Sergiu Netotea, Scilifelab, NBIS\n", + "- past lecturer: Rui Benfeitas, Scilifelab, NBIS\n", + "\n", + "\n", + "__Introduction__\n", + "In this notebook we will explore how to generate and analyse a multi-omic network comprising metabolites quantifications and gene expression. We will compare these networks against randomly generated networks, and compute different network metrics. At the end we will also perform a community analysis and functional characterization at the gene level. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Biological network topology analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Objective** \n", + "In this notebook you will learn how to build and analyse a network built and analysed from a gene-metabolite association analysis. Other mixed networks may also be similarly analyzed, differring only in whether and how you can apply the final functional analysis.\n", + "\n", + "**Data** \n", + "As a test case we will be using the file [met_genes.tsv](met_genes.tsv) which contains abundances for 125 metabolites and 1992 genes, for 24 samples.\n", + "\n", + "**Software** \n", + "This notebook relies on python's igraph for most of the analyses. Most, if not all, functions used here can also be applied with R's igraph. Other packages exist for network analysis including [networkx](https://networkx.github.io/) (which is the default goto for modern datascience) and [graph-tool](https://graph-tool.skewed.de/). [Snap.py](https://snap.stanford.edu/snappy/) is also a good alternative for large networks.\n", + "\n", + "We will build our network through an association analysis, but there are other methods to do this including [Graphical Lasso](http://statweb.stanford.edu/~tibs/ftp/graph.pdf) or [linear SVR](https://papers.nips.cc/paper/1187-support-vector-method-for-function-approximation-regression-estimation-and-signal-processing.pdf).\n", + "\n", + "**Environment**\n", + "igraph and gseapy need their own separate python environments, since they do not sit well with the default data science stack. Because of this, the analysis is separated in three parts, each must be run on a specific environments.\n", + "\n", + "**Contents**\n", + "\n", + "- Part 1:\n", + " - > environment: datasci, contains plotting and statistical/ML libraries\n", + " - Data preparation\n", + " - Association analysis\n", + " - Network construction\n", + " - Graph and community study plots (from part 2 and 3)\n", + "- Part 2:\n", + " - > environment: igraph, contains graph study and leidenalg based community detection\n", + " - Preliminary analysis\n", + " - Centrality analysis\n", + " - Community detection\n", + "- Part 3:\n", + " - > environment: gseapy, annotation studies and basic data processing\n", + " - Functional analysis\n", + " - Conclusion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data preparation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You will use a gene expression dataset (RNAseq, expressed as TPM) from a disease group with 24 samples to keep analysis memory and time requirements reasonable for this lesson. It is assumed that all batch effects or other possible technical artifacts are not present, and that all data is ready for analysis. However, there are several important considerations in preprocessing your data:\n", + " - How should you deal with missing values? Should you impute them? How?\n", + " - Should you remove samples based on number of missing values?\n", + " - How should you normalize your data in order to make it comparable throughout?\n", + " \n", + "This will depend on the type of that that you have and what you want to do with it, and will severely affect downstream results. It is thus important to carefully think about this." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "## Preamble\n", + "import itertools, random\n", + "import pandas as pd\n", + "import numpy as np\n", + "import sklearn\n", + "import sklearn.neighbors\n", + "import scipy as sp\n", + "from statsmodels.stats.multitest import multipletests\n", + "\n", + "# Plotting\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Typep10p11p12p14p15p16p18p22p23...p37p38p4p40p41p46p48p5p8p9
feature
C0_accarnitinesmet0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.103152...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.049757
C2_accarnitinesmet0.0073560.0384540.0117380.0119230.0151040.0123170.0219050.0177770.039760...0.0000000.0125420.0188960.0202120.0152630.0125100.0096770.0157570.0107060.018615
C3_accarnitinesmet0.0000000.0000000.0000000.0000000.0008880.0011160.0016820.0010240.002179...0.0000000.0008270.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000995
C3DC_C4OH_accarnitinesmet0.0000000.0000000.0004550.0011630.0000000.0000000.0000000.0000000.001065...0.0000000.0004800.0000000.0007170.0000000.0007820.0003980.0000000.0000000.000312
C5DC_C6OH_accarnitinesmet0.0010350.0014790.0004300.0015270.0004850.0007920.0009540.0009950.001356...0.0057920.0004000.0009870.0012550.0008850.0017100.0005970.0012180.0008480.000507
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5 rows × 25 columns

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" + ], + "text/plain": [ + " Type p10 p11 p12 p14 p15 \\\n", + "feature \n", + "C0_accarnitines met 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "C2_accarnitines met 0.007356 0.038454 0.011738 0.011923 0.015104 \n", + "C3_accarnitines met 0.000000 0.000000 0.000000 0.000000 0.000888 \n", + "C3DC_C4OH_accarnitines met 0.000000 0.000000 0.000455 0.001163 0.000000 \n", + "C5DC_C6OH_accarnitines met 0.001035 0.001479 0.000430 0.001527 0.000485 \n", + "\n", + " p16 p18 p22 p23 ... p37 \\\n", + "feature ... \n", + "C0_accarnitines 0.000000 0.000000 0.000000 0.103152 ... 0.000000 \n", + "C2_accarnitines 0.012317 0.021905 0.017777 0.039760 ... 0.000000 \n", + "C3_accarnitines 0.001116 0.001682 0.001024 0.002179 ... 0.000000 \n", + "C3DC_C4OH_accarnitines 0.000000 0.000000 0.000000 0.001065 ... 0.000000 \n", + "C5DC_C6OH_accarnitines 0.000792 0.000954 0.000995 0.001356 ... 0.005792 \n", + "\n", + " p38 p4 p40 p41 p46 \\\n", + "feature \n", + "C0_accarnitines 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "C2_accarnitines 0.012542 0.018896 0.020212 0.015263 0.012510 \n", + "C3_accarnitines 0.000827 0.000000 0.000000 0.000000 0.000000 \n", + "C3DC_C4OH_accarnitines 0.000480 0.000000 0.000717 0.000000 0.000782 \n", + "C5DC_C6OH_accarnitines 0.000400 0.000987 0.001255 0.000885 0.001710 \n", + "\n", + " p48 p5 p8 p9 \n", + "feature \n", + "C0_accarnitines 0.000000 0.000000 0.000000 0.049757 \n", + "C2_accarnitines 0.009677 0.015757 0.010706 0.018615 \n", + "C3_accarnitines 0.000000 0.000000 0.000000 0.000995 \n", + "C3DC_C4OH_accarnitines 0.000398 0.000000 0.000000 0.000312 \n", + "C5DC_C6OH_accarnitines 0.000597 0.001218 0.000848 0.000507 \n", + "\n", + "[5 rows x 25 columns]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "data=pd.read_csv('data/met_genes.tsv', sep=\"\\t\", index_col=0)\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "No duplicated features are present." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "any(data.index.duplicated()) " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
p10
Type
genes1992
met125
\n", + "
" + ], + "text/plain": [ + " p10\n", + "Type \n", + "genes 1992\n", + "met 125" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.groupby('Type').agg('count')[['p10']] #1992 genes, 125 metabolites" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2117, 25)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.shape #2117 features, 25 samples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A very quick view shows that several gene clusters are found, including two major groups. However, the analysis below does not perform any statistical filtering." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Association analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our initial network analysis will be performed on the association analysis using Spearman correlations. Because this network has a big chance of producing false positives we will consider [Bonferroni correction](https://en.wikipedia.org/wiki/Bonferroni_correction) to control for familywise error, as well as [FDR](https://en.wikipedia.org/wiki/False_discovery_rate#Benjamini%E2%80%93Hochberg_procedure). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A very quick view shows that several gene clusters are found, including two major groups. However, the analysis below does not perform any statistical filtering." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "values=data.loc[:,data.columns!='Type']\n", + "meta=data[['Type']]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "values.isna().any().any() #we have no rows with NA" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will perform a gene-gene, gene-metabolite, and metabolite-metabolite association analysis by computing pairwise [Spearman correlations](https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.spearmanr.html#scipy.stats.spearmanr). Choosing other non-parametric (Kendall or Spearman) vs parametric (Pearson) methods depends on your data. Here, because we have a small sample size, and want to save on computational time we choose Spearman. \n", + "\n", + "The following takes a few minutes to run." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Correlation and P val matrices\n", + "Rmatrix, Pmatrix= sp.stats.spearmanr(values.T)\n", + "Rmatrix=pd.DataFrame(Rmatrix, index=values.index.copy(), columns=values.index.copy())\n", + "\n", + "#resulting R matrix\n", + "sns.clustermap(Rmatrix, cmap=\"RdBu_r\", center=0); " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we look at the matrix of P values, we can already see that many of the correlations in the top right columns are not significant even before multiple hypothesis correction:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Pmatrix=pd.DataFrame(Pmatrix, index=values.index.copy(), columns=values.index.copy())\n", + "changed_Pmatrix=Pmatrix.copy()\n", + "changed_Pmatrix[changed_Pmatrix>0.01]=1\n", + "#resulting P matrix, similar to that seen in the section above\n", + "sns.clustermap(changed_Pmatrix); " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now adjust the P values based on the number of comparisons done. The heatmaps above are highlighting a total of $2117^2 \\approx 4.5m$ correlations. However, these numbers consider that the same correlation is computed twice (gene A vs gene B, and gene B vs gene A). If we were to include all correlations, we would thus be including many repeated analyses, and we are only interested in half of that above, and excluding the correlation of a feature with itself. \n", + "This means $\\frac{2117!}{2!(2117-2)!} \\approx 2.2m$ correlations. At an error rate of 0.05, this means that the probability of finding at least one false positive is nearly 100%: $1-0.95^{2000000} \\approx 1$. We thus need to correct P values.\n", + "\n", + "In the following cell, we convert the matrix of p*p features to a long matrix, concatenate both R and P for each correlation, and correct based on [Bonferroni](https://en.wikipedia.org/wiki/Bonferroni_correction) (`Padj`) and [FDR](https://en.wikipedia.org/wiki/False_discovery_rate) (Benjamin-Hochberg)." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "#prepare P matrix\n", + "Psquared=Pmatrix.where(np.triu(np.ones(Pmatrix.shape),1).astype(bool))\n", + "Psquared.columns.name='Feat2'\n", + "Pmatrix=Pmatrix.stack()\n", + "Pmatrix.index.names=['v1','v2']\n", + "Pmatrix=Pmatrix.reset_index()\n", + "Pmatrix.columns=['feat1','feat2','P']" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "#prepare R matrix\n", + "Rmatrix=Rmatrix.where(np.triu(np.ones(Rmatrix.shape),1).astype(bool))\n", + "Rmatrix.columns.name='Feat2'\n", + "Rmatrix=Rmatrix.stack()\n", + "Rmatrix.index.names=['v1','v2'] #Avoid stacked names colliding\n", + "Rmatrix=Rmatrix.reset_index()\n", + "Rmatrix.columns=['feat1','feat2','R']" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# join both\n", + "PRmatrix=pd.merge(Rmatrix.copy(), Pmatrix.copy(), on=['feat1','feat2']) #Correlation matrix with both R and P\n", + "PRmatrix=PRmatrix.loc[PRmatrix.feat1!=PRmatrix.feat2].dropna()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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feat1feat2RPPadjFDR
0C0_accarnitinesC2_accarnitines0.3753260.0707181.00.149290
1C0_accarnitinesC3_accarnitines0.5191740.0093291.00.033441
2C0_accarnitinesC3DC_C4OH_accarnitines0.3953630.0558481.00.125357
3C0_accarnitinesC5DC_C6OH_accarnitines-0.0299170.8896311.00.928289
4C0_accarnitinesC5MDC_accarnitines-0.1378010.5207951.00.643703
\n", + "
" + ], + "text/plain": [ + " feat1 feat2 R P Padj FDR\n", + "0 C0_accarnitines C2_accarnitines 0.375326 0.070718 1.0 0.149290\n", + "1 C0_accarnitines C3_accarnitines 0.519174 0.009329 1.0 0.033441\n", + "2 C0_accarnitines C3DC_C4OH_accarnitines 0.395363 0.055848 1.0 0.125357\n", + "3 C0_accarnitines C5DC_C6OH_accarnitines -0.029917 0.889631 1.0 0.928289\n", + "4 C0_accarnitines C5MDC_accarnitines -0.137801 0.520795 1.0 0.643703" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Multiple hypothesis correction computed on the P column\n", + "adjP=pd.DataFrame(multipletests(PRmatrix['P'], method='bonferroni', alpha=0.01)[1], columns=['Padj'])\n", + "FDR=pd.DataFrame(multipletests(PRmatrix['P'], method='fdr_bh', alpha=0.01)[1], columns=['FDR'])\n", + "\n", + "PRmatrix=pd.concat([ PRmatrix, adjP], axis=1)\n", + "PRmatrix=pd.concat([ PRmatrix, FDR], axis=1)\n", + "\n", + "PRmatrix.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2239786" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#total number of correlations w/o repetition: 2.2m\n", + "PRmatrix.shape[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Considering the Bonferroni correction we find 16305 correlations that are statistically significant at an $\\alpha < 0.01$. If we consider instead FDR as correction method, we find 402368, which at a FDR of 0.01 implies $0.01 \\times 402368 = 4023$ false positives." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "16305" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum(PRmatrix.Padj<0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "402368" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum(PRmatrix.FDR<0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's add two additional columns, where we assign `R=0` for those correlations that are not statistically significant (`adjP > 0.01`, and `FDR > 0.01`). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "PRmatrix.loc[:,'R (padj)']=PRmatrix['R'].copy()\n", + "PRmatrix.loc[:,'R (fdr)']=PRmatrix['R'].copy()\n", + "PRmatrix.loc[PRmatrix['Padj']>0.01,'R (padj)']=0\n", + "PRmatrix.loc[PRmatrix['FDR']>0.01,'R (fdr)']=0\n", + "\n", + "all_mets=meta.loc[meta.Type=='met'].index\n", + "PRmatrix['feat1_type']=['met' if x in all_mets else 'gene' for x in PRmatrix.feat1 ]\n", + "PRmatrix['feat2_type']=['met' if x in all_mets else 'gene' for x in PRmatrix.feat2 ]\n", + "PRmatrix['int_type']=PRmatrix.feat1_type+'_'+PRmatrix.feat2_type" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "PRmatrix.to_csv('lab/data/serialization/association_matrix.tsv', sep=\"\\t\", index=False) #export correlation matrix for faster loading" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can see how the initial heatmap of correlations looks. The next cell converts the matrix from long to squared matrix so that we can generate the heatmap. We will plot those features that show statistically significant associations with more than 5% of the features after FDR correction, and compare them between FDR- and Bonferroni-corrected datasets. \n", + "\n", + "The following plot shows the heatmap of the Spearman rank correlation coefficients after Bonferroni-correction - correlations where Padj > 0.05 are shown as 0." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Transforming to a squared matrix again\n", + "PRQ=pd.concat([\n", + " PRmatrix.copy(), \n", + " PRmatrix.copy().rename(columns={'feat1':'feat2','feat2':'feat1'}).loc[:,PRmatrix.columns]\n", + " ]).drop_duplicates()\n", + "\n", + "Rmatrix_fdr=PRQ.copy().pivot(index='feat1',columns='feat2',values='R (fdr)')\n", + "Rmatrix_fdr=Rmatrix_fdr.loc[Rmatrix_fdr.sum()!=0]\n", + "Rmatrix_padj=PRQ.copy().pivot(index='feat1',columns='feat2',values='R (padj)')\n", + "\n", + "Rmatrix_fdr=Rmatrix_fdr.loc[Rmatrix_fdr.index,Rmatrix_fdr.index].fillna(0)\n", + "Rmatrix_padj=Rmatrix_padj.loc[Rmatrix_fdr.index,Rmatrix_fdr.index].fillna(0)\n", + "\n", + "\n", + "#Showing only the top correlated features\n", + "top_features=Rmatrix_fdr.index[(Rmatrix_fdr!=0).sum()>0.05*Rmatrix_fdr.shape[0]] #top features based on FDR\n", + "Rmatrix_fdr_top=Rmatrix_fdr.copy().loc[top_features,top_features] #subsetting R (fdr corrected) matrix\n", + "Rmatrix_padj_top=Rmatrix_padj.copy().loc[top_features,top_features] #subsetting R (bonferroni corrected) matrix\n", + "\n", + "g=sns.clustermap(Rmatrix_padj_top, cmap=\"RdBu_r\", center=0);\n", + "g.fig.suptitle('Spearman R (Padj < 0.01, Bonferroni)');\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g=sns.clustermap(Rmatrix_fdr_top, cmap=\"RdBu_r\", center=0);\n", + "g.fig.suptitle('Spearman R (FDR<0.01)');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The plots above show that the Bonferroni correction is only selecting very high (absolute) correlations. This should remove false positives, but it may also remove weaker correlations that are biologically relevant and true positives. The Bonferroni correction also removes most of the negatively-associated features. Notice this from the distribution of correlation coefficients:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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fv54ZM2bg5eVFrVq16NOnD+fPny/4i8/lwIED9O7dG0dHR+zt7enatSvbtm1T9wcHB6uJ1DvvvINGo6Fhw4aF1nnv3j0mT55M48aNsbGxwc3NjUGDBnHu3Dm1zN27dxk3bhx169bF2tqaxo0bM2PGDNLS0gzq0mg0vPnmm3z++ee0atUKGxsbVq9erX7327dv59VXX6VOnTrY29urx2/cuBE/Pz8cHByoWbMm/fv35+TJk0V+Hxs3bqRfv354enpiZ2dHq1at+Pe//01ycrJaZtSoUSxdulSNT//Q/xzk1/Xo2rVrvPTSS7i5uWFjY0OrVq2YP38+2dnZahl995pPP/2UBQsW0KhRI2rWrImfnx+HDx8uMnaAmzdvMnbsWOrXr4+1tTVeXl4MHz6cmJgYk2IBXZeiDz74gJYtW2JjY0OdOnV45ZVXiIuLM/j3+eqrr0hNTVW/B/2/zc6dOzl79qy6Xf/7kN/Pe1FxF9T16MKFCwQEBBh8Fv2/jV5xf0+K+j1dtmwZbdu2pWbNmjg6OtKyZUumT59erH+Xqmz6+PEEDR9eIY/p48eb++OW2Pnz5/Hw8CAxMdHcoZhs+PDhLFiwwNxhVBhpURCiChk0aBAWFhbs37+/wDJXrlxh8ODBdO/ena+//pratWtz8+ZNQkJCSE9Px9PTk5CQEAYMGMDo0aN57bXXANTkQW/YsGGMGDGCN954w+DCLz9hYWEEBQURHByMh4cHa9eu5a233iI9PZ0pU6aY9Bk/++wzxo4dy6VLl9i8eXOR5c+fP0/Xrl1xc3Pjv//9Ly4uLqxZs4ZRo0YRExPD1KlTDcpPnz6dbt268dVXX3H//n3eeecdhgwZwtmzZ7GwsCjwPPv27aNv3760adOGFStWYGNjw2effcaQIUNYv349zz//PK+99hpt27Zl2LBhTJgwgYCAAGxsbAqsMzExkccff5wrV67wzjvv0LlzZ5KSkti/fz9RUVG0bNmSBw8e0KtXLy5dusR7771HmzZt+P3335k7dy5hYWEGiQrAjz/+yO+//87MmTPx8PDAzc2NY8eOAfDqq68yePBgvv32W5KTk7GysmLOnDn83//9H6+88gr/93//R3p6OvPmzaN79+4cPXoUHx+fAuO/cOECgwYNIigoCAcHB86dO8fHH3/M0aNH2b17NwDvvvsuycnJfP/99xw6dEg91tPTM9864+Li6Nq1K+np6bz//vs0bNiQrVu3MmXKFC5dusRnn31mUH7p0qW0bNlS7Sv/7rvvMmjQICIjI9FqtQXGfvPmTTp16kRGRgbTp0+nTZs23Llzh99++434+Hjc3d2LHUt2djZPPfUUv//+O1OnTqVr165cvXqVWbNm0bNnT44fP46dnR2HDh3i/fffZ8+ePer306hRIw4dOsS4ceNISEhg7dq1AAV+78WJOz8RERF07dpVveHg4eHBb7/9xsSJE7l9+zazZs0yKF/U70lhv6cbNmxg3LhxTJgwgU8//ZQaNWpw8eJFIiIiCvz3qC5SYmJY5OtbIecKCg83+ZhRo0YZ3UQB3e9y06ZNDfZbWlri7OxMmzZteOGFFxg1ahQ1avx9f7lhw4ZcvXoVAFtbW7y9vRk9ejRTpkwpsqVyxowZjB8/vlSrHH/22WfMmzePqKgoHnnkERYtWkT37t0LPWbfvn1MmjSJM2fO4OXlxdSpU3njjTfU/WfOnGHmzJmEhoZy9epVFi5caNS1bubMmfTq1YvXXnvt4eg2qQghKo2VK1cqgHLs2LECy7i7uyutWrVS38+aNUvJ/av8/fffK4ASFhZWYB1xcXEKoMyaNcton76+mTNnFrgvN29vb0Wj0Ridr2/fvkqtWrWU5ORkg88WGRlpUG7Pnj0KoOzZs0fdNnjwYMXb2zvf2PPGPWLECMXGxka5du2aQbmBAwcq9vb2yr179wzOM2jQIINy3333nQIohw4dyvd8el26dFHc3NyUxMREdVtmZqbi6+ur1KtXT8nOzlYURVEiIyMVQJk3b16h9SmKosyePVsBlB07dhRY5vPPP1cA5bvvvjPY/vHHHyuAsn37dnUboGi1WuXu3bsGZfXf/csvv2yw/dq1a4qlpaUyYcIEg+2JiYmKh4eH8txzz6nb8vu3zy07O1vJyMhQ9u3bpwDKn3/+qe4bP358gcd6e3srI0eOVN//+9//VgDlyJEjBuX++c9/KhqNRjl//ryiKH9/z61bt1YyMzPVckePHlUAZf369QXGqiiK8uqrrypWVlZKREREgWWKG8v69esVQPnhhx8Myh07dkwBlM8++0zdNnLkSMXBwcHoXD169FAeeeQRo+15f96LE7f+u1m5cqW6rX///kq9evWUhIQEg7JvvvmmYmtrq/7MmPJ7UtDv6ZtvvqnUrl27wPiqutTUVCUiIkJJTU012vfWM88oyqxZFfJ465lnTI595MiRyoABA5SoqCiDh/53KPf+GzduKKGhocqHH36o1KxZUxk4cKCSkZGh1uXt7a3Mnj1biYqKUiIjI5Xly5crlpaWyueff15oDNevX1esrKyU69evFzvurKws5caNG+r7DRs2KFZWVsry5cuViIgI5a233lIcHByUq1evFljH5cuXFXt7e+Wtt95SIiIilOXLlytWVlbK999/r5Y5evSoMmXKFGX9+vWKh4eHsnDhwnzrevTRRw1+r6uywn6eFUVRpOuREFWMoiiF7m/Xrh3W1taMHTuW1atXc/ny5RKd55lnnil22UceeYS2bdsabAsICOD+/fucOHGiROcvrt27d9O7d2/q169vsH3UqFGkpKQY3MUGGDp0qMH7Nm3aAKh3xvKTnJzMkSNHGD58uDr4FMDCwoLAwEBu3LhR7O5Luf366680b97cYHB6Xrt378bBwYHhw4cbbNd319m1a5fB9ieffBInJ6d868r7b/rbb7+RmZnJyy+/TGZmpvqwtbWlR48eRc6KdfnyZQICAvDw8MDCwgIrKyt69OgBwNmzZws9tiC7d+/Gx8eHxx57zGD7qFGjUBRFvROvN3jwYIOWoOL8e4Luu+/VqxetWrUqdSxbt26ldu3aDBkyxOB7bNeuHR4eHibPLlbauPN68OABu3bt4h//+Af29vYGMQ4aNIgHDx4Yddcqye+J3mOPPca9e/d44YUX+Omnn7h9+3axYxXlz8bGBg8PD4NH7t8h/f66devy6KOPMn36dH766Sd+/fVXo+5sjo6OeHh40LBhQ1577TXatGnD9u3bCz3/d999R9u2bYs13u3cuXNMmzaNBg0a8Omnn6rbFyxYoLaIt2rVikWLFlG/fn2WLVtWYF2ff/45DRo0YNGiRbRq1YrXXnuNV1991aDeTp06MW/ePEaMGFFoa/DQoUNZv359kfFXB5IoCFGFJCcnc+fOHby8vAos06RJE3bu3Imbmxvjx4+nSZMmNGnShP/85z8mnaugriH58fDwKHDbnTt3TDqvqe7cuZNvrPrvKO/5XVxcDN7r/xikpqYWeI74+HgURTHpPMURFxdX5B/LO3fu4OHhYdSU7+bmhqWlpdF5C/t3y7tP36e9U6dOWFlZGTw2btxY6AVeUlIS3bt358iRI3zwwQfs3buXY8eOsWnTJqDw77MwFfHvCcX/7osTS0xMDPfu3cPa2troe4yOji7TC+XixJ3XnTt3yMzMZPHixUbxDRo0CMAoxpJ+rwCBgYF8/fXXXL16lWeeeQY3Nzc6d+7Mjh07TIpbVB5PPvkkbdu2VX+/81IUhb1793L27FmsrKwKrWv//v0Gk3XkFR8fz7Jly+jSpQu+vr6Ehoby0Ucf8eGHHwK68UChoaH069fP4Lh+/fpx8ODBAus9dOiQ0TH9+/fn+PHjZGRkFBpzXo899hhHjx41GidWHckYBSGqkG3btpGVlVXklKbdu3ene/fuZGVlcfz4cRYvXkxQUBDu7u6MGDGiWOcyZTac6OjoArfpLzhsbW0BjP5jLe1FlIuLC1FRUUbbb926BYCrq2up6gdwcnKiRo0aZX6eOnXqcOPGjULLuLi4cOTIERRFMfg3iY2NJTMz0+i8hf275d2nP/b777/H29vbpNh3797NrVu32Lt3r9qKALrB2aVREf+eUPzvvjixuLq64uLiQkhISL71lKYfdl7FiTsvJycntfVrfAEDYBs1alQW4aleeeUVXnnlFZKTk9m/fz+zZs3C39+fv/76y+SfNVG2tm7datAyOnDgQP73v/8VeVzLli05deqUwbZ33nlHHduUkZGBra0tEydOLLSeK1eu0KFDB4Nt2dnZ/Prrr6xevZotW7bQvHlzAgMD2bx5s1Gyfvv2bbKysozG47i7u+f7t0gvOjo632MyMzO5ffu2STfH6tatS1paGtHR0dX+51laFISoIq5du8aUKVPQarW8/vrrxTrGwsKCzp07qzOb6LsBmXJ3sDjOnDnDn3/+abBt3bp1ODo68uijjwKos//k/UOzZcsWo/psbGyKHVvv3r3Vi9bcvvnmG+zt7ctkmkgHBwc6d+7Mpk2bDOLKzs5mzZo11KtXj+bNm5tc78CBA/nrr7+MutPk1rt3b5KSkowWbfvmm2/U/SXVv39/LC0tuXTpEh07dsz3URB90pG3eT73jFx6pvy89e7dm4iICKMua9988w0ajYZevXoVWUdxDBw4kD179hTaZay4sfj7+3Pnzh2ysrLy/Q7Lch2N4sSdl729Pb169eLkyZO0adMm3xjztiAUR3F+Tx0cHBg4cCAzZswgPT2dM2fOmHweUbZ69epFWFiY+sg7Q11B8t6sAPjXv/5FWFgY+/bto1evXsyYMYOuXbsWWo9+aubcrl27hr+/Pzt37mTdunWcOnWKf/3rX4VevOeNJb/4inNMftuLop9yOiUlxaTjqiJpURCiEgoPD1f7EMfGxvL777+zcuVKLCws2Lx5s9EMRbl9/vnn7N69m8GDB9OgQQMePHjA119/DaD2hXd0dMTb25uffvqJ3r174+zsjKura5FTeRbEy8uLoUOHEhwcjKenJ2vWrGHHjh18/PHH2NvbA7ruLS1atGDKlClkZmbi5OTE5s2bOXDggFF9rVu3ZtOmTSxbtowOHTpQo0aNAi9aZ82axdatW+nVqxczZ87E2dmZtWvXsm3bNj755JNCZ74xxdy5c+nbty+9evViypQpWFtb89lnnxEeHs769etLtB5BUFAQGzdu5KmnnuLf//43jz32GKmpqezbtw9/f3969erFyy+/zNKlSxk5ciRXrlyhdevWHDhwgDlz5jBo0KBCxzcUpWHDhsyePZsZM2Zw+fJlBgwYgJOTEzExMRw9ehQHBwd1Yb68unbtipOTE2+88QazZs3CysqKtWvXGiWMoPv3BPj4448ZOHAgFhYWtGnTBmtra6Oyb7/9Nt988w2DBw9m9uzZeHt7s23bNj777DP++c9/lighy8/s2bP59ddfeeKJJ5g+fTqtW7fm3r17hISEMGnSJFq2bFnsWEaMGMHatWsZNGgQb731Fo899hhWVlbcuHGDPXv28NRTT/GPf/yjwuLOz3/+8x8ef/xxunfvzj//+U8aNmxIYmIiFy9e5Oeffy40WS1IQb+nY8aMwc7Ojm7duuHp6Ul0dDRz585Fq9XSqVOn0n4FopQcHBxo2rSpycedPXvWqOXJ1dWVpk2b0rRpU3744QeaNm1Kly5dCv1/ydXVlfj4eINt9erVY/369axevZrnn3+eLl268PLLL/Pss89Su3Zto+MtLCyMWg9iY2MLnPULdN1h8zvG0tLS5ET57t27gPFsgdWRJApCVEKvvPIKANbW1tSuXZtWrVrxzjvv8NprrxX5H1O7du3Yvn07s2bNIjo6mpo1a+Lr68uWLVsM+meuWLGCf/3rXwwdOpS0tDRGjhxpNFCtuNq1a8crr7zCrFmzuHDhAl5eXixYsIC3335bLWNhYcHPP//Mm2++yRtvvIGNjQ0jRoxgyZIlDB482KC+t956izNnzjB9+nQSEhJQFKXAQdwtWrTg4MGDTJ8+nfHjx5OamkqrVq1YuXKl0fz8pdGjRw92797NrFmzGDVqFNnZ2bRt25YtW7bg7+9fojodHR05cOAAwcHBfPnll7z33ns4OTnRqVMnxo4dC+i6bO3Zs4cZM2Ywb9484uLiqFu3LlOmTDGa0rIkpk2bho+PD//5z39Yv349aWlpeHh40KlTJ4NpA/NycXFh27ZtTJ48mZdeegkHBweeeuopNm7cqLYi6QUEBPDHH3/w2WefMXv2bBRFITIyMt/EtE6dOhw8eJBp06Yxbdo07t+/T+PGjfnkk0+YNGlSqT+vXt26dTl69CizZs3io48+4s6dO9SpU4fHH39cXRW5uLFYWFiwZcsW/vOf//Dtt98yd+5cLC0tqVevHj169FATpYqKOz8+Pj6cOHGC999/n//7v/8jNjaW2rVr06xZM3WcgqkK+j3t3r07q1at4rvvviM+Ph5XV1cef/xxvvnmm4fiwqo62r17N6dPnzb4Pz0vJycnJkyYwJQpUzh58mSBN0/at29vNFWupaUlI0aMYMSIEURFRfHtt9+yaNEiJkyYwJAhQwgMDGTgwIFYWVlhbW1Nhw4d2LFjh0ECvmPHDp566qkC4/Pz8+Pnn3822LZ9+3Y6duxY5LiKvMLDw6lXr16ZdYWszDRKUVOoCCGEEEIIQDeLVGRkJI0aNTLqQhM0fHiFrqOw6PvvTTpm1KhR3Lt3z6grY+79MTExrFy5kqysLGJiYggJCWHu3Ln07NmTH3/8UZ0hqWHDhgQFBRmsMxAXF0eDBg349ttvjWZq0/v555957bXXuHXrVqFr1wAcP36cVatWsWHDBkaOHMn8+fMB3WKPgYGBfP755/j5+fHll1+yfPlyzpw5o44ZmDZtGjdv3lS7aUZGRuLr68vrr7/OmDFjOHToEG+88Qbr169XZ4RLT09Xk5hBgwbx4osv8uKLL1KzZk2DVphRo0ZhYWHBihUrivjGK7/Cfp5BWhSEEEIIIcqEvbt7iRZCK+m5ykNISAienp5YWlri5ORE27Zt+e9//8vIkSMNFlzLT506dQgMDCQ4OJhhw4blW37QoEFYWVmxc+dO+vfvX2h9+jE0CxYsMBjE//zzz3Pnzh1mz55NVFQUvr6+/PLLLwYDi6Oiorh27Zr6vlGjRvzyyy+8/fbbLF26FC8vL/773/8aTBt969Yt2rdvr77/9NNP+fTTTw2mi37w4AGbN2/mt99+KzT26kJaFIQQQgghiqmoO7CiaJ999hk//fRTlbzYXrp0KT/99FOR60VUFdKiIIQQQgghKo2xY8cSHx9PYmJimU4fXBGsrKxYvHixucOoMNKiIIQQQghRTNKiIKqTon6eZR0FIYQQQgghhBFJFIQQQgghhBBGZIxCMWVmZnLy5Enc3d2LHPUvhBBCiOpJvxhmenq6XA9UQoqikJmZib29fYkWwnzYFDUCQRKFYjp58iSPPfaYucMQQgghhBk5ODiwdu1a0tLSzB2KKESrVq1wcHAwdxiVXnp6OkCBa1pIolBM+mXBjx49iqenp5mjEUIIIYS5JCUlkZmZSZ06dbCzszN3OCKXzMxMLl++TFZWFg8ePDB3OJVadnY2cXFx2NvbY2mZf0ogiUIx6ZsXPT09qVevnpmjEUIIIYS5KIpCdHQ0d+/eNXcoIo/MzExu376NjY1NgRe/4m81atSgQYMGBXbTkm9QCCGEEMIEGo0GT09P3NzcyMjIMHc4Ipfo6GjeeOMN9u7di4eHh7nDqfSsra0LHWsjiYIQQgghRAlYWFgU2LdbmIelpSVXr17F0tJS1rkoAzJcXwghhBBCCGFEEgUhhBBCCCGEEUkUhBBCCCGEEEYkURBCCCGEEEIYkURBCCGEEEIIYUQSBSGEEEIIIYQRSRSEEEIIIYQQRiRREEIIIYQQQhiRREEIIYQQQghhRFZmFkIIIUSlNX38eFJiYoy227u7M2fpUjNEVLWNHz+emHy+T3d3d5bK9ynykERBCCGEEJVWSkwMi3x9jbYHhYebIZqqLyYmBt98vs9w+T5FPqTrkRBCCCGEEMKIJApCCCGEEEIII5IoCCGEEEIIIYxIoiCEEEIIIYQwIomCEEIIIYQQwogkCkIIIYQQQggjkigIIYQQQgghjEiiIIQQQgghhDAiiYIQQgghhBDCiCQKQgghhBBCCCOSKAghhBBCCCGMmD1RuHnzJi+99BIuLi7Y29vTrl07QkND1f2KohAcHIyXlxd2dnb07NmTM2fOGNSRlpbGhAkTcHV1xcHBgaFDh3Ljxg2DMvHx8QQGBqLVatFqtQQGBnLv3r2K+IhCCCGEEEJUOWZNFOLj4+nWrRtWVlb8+uuvREREMH/+fGrXrq2W+eSTT1iwYAFLlizh2LFjeHh40LdvXxITE9UyQUFBbN68mQ0bNnDgwAGSkpLw9/cnKytLLRMQEEBYWBghISGEhIQQFhZGYGBgRX5cIYQQQghRicydO5dOnTrh6OiIm5sbTz/9NOfPnzcoM2rUKDQajcGjS5cuBmXK6qb1tWvXGDJkCA4ODri6ujJx4kTS09PL5bMXh6XZzgx8/PHH1K9fn5UrV6rbGjZsqL5WFIVFixYxY8YMhg0bBsDq1atxd3dn3bp1vP766yQkJLBixQq+/fZb+vTpA8CaNWuoX78+O3fupH///pw9e5aQkBAOHz5M586dAVi+fDl+fn6cP3+eFi1aVNyHFkIIIYQQlcK+ffsYP348nTp1IjMzkxkzZtCvXz8iIiJwcHBQyw0YMMDgetXa2tqgnqCgIH7++Wc2bNiAi4sLkydPxt/fn9DQUCwsLADdTesbN24QEhICwNixYwkMDOTnn38GICsri8GDB1OnTh0OHDjAnTt3GDlyJIqisHjx4vL+KvJl1kRhy5Yt9O/fn2effZZ9+/ZRt25dxo0bx5gxYwCIjIwkOjqafv36qcfY2NjQo0cPDh48yOuvv05oaCgZGRkGZby8vPD19eXgwYP079+fQ4cOodVq1SQBoEuXLmi1Wg4ePJhvopCWlkZaWpr6PncLhhBCCCGEqPr0F+16K1euxM3NjdDQUJ544gl1u42NDR4eHvnWUVY3rbdv305ERATXr1/Hy8sLgPnz5zNq1Cg+/PBDatWqVR5fQaHMmihcvnyZZcuWMWnSJKZPn87Ro0eZOHEiNjY2vPzyy0RHRwPg7u5ucJy7uztXr14FIDo6Gmtra5ycnIzK6I+Pjo7Gzc3N6Pxubm5qmbzmzp3Le++9V+rPKIQQQoiKMX38eFJiYoy227u7M2fpUjNEJKqahIQEAJydnQ227927Fzc3N2rXrk2PHj348MMP1WvLsrppfejQIXx9fdUkAaB///6kpaURGhpKr169yvOj58usiUJ2djYdO3Zkzpw5ALRv354zZ86wbNkyXn75ZbWcRqMxOE5RFKNteeUtk1/5wuqZNm0akyZNUt/fvHkTHx+foj+UEEIIIcrdiZMnCRo+3GBb+MmT7Mxn/GFQeHhFhSUqicTERO7fv6++t7GxwcbGptBjFEVh0qRJPP744/j6+qrbBw4cyLPPPou3tzeRkZG8++67PPnkk4SGhmJjY1NmN62jo6ONbo47OTlhbW1d4I3t8mbWRMHT09Po4rtVq1b88MMPAGoTT3R0NJ6enmqZ2NhY9Yv08PAgPT2d+Ph4g3+g2NhYunbtqpaJyecOQ1xcnNE/iF7eH6jcP2xCCCGEMC+LtDQW5bqYA+h1+LCZohGVTd7ry1mzZhEcHFzoMW+++SanTp3iwIEDBtuff/559bWvry8dO3bE29ubbdu2qWNo81OSm9am3tgub2ad9ahbt25GI8v/+usvvL29AWjUqBEeHh7s2LFD3Z+ens6+ffvUJKBDhw5YWVkZlImKiiI8PFwt4+fnR0JCAkePHlXLHDlyhISEBLWMEEIIIYSoHiIiIkhISFAf06ZNK7T8hAkT2LJlC3v27KFevXqFlvX09MTb25sLFy4Ahjetc8t7Y7uom9YeHh5GLQfx8fFkZGQUeGO7vJk1UXj77bc5fPgwc+bM4eLFi6xbt44vv/yS8ePHA7qsKigoiDlz5rB582bCw8MZNWoU9vb2BAQEAKDVahk9ejSTJ09m165dnDx5kpdeeonWrVurA0patWrFgAEDGDNmDIcPH+bw4cOMGTMGf39/mfFICCGEEKKacXR0pFatWuqjoG5HiqLw5ptvsmnTJnbv3k2jRo2KrPvOnTtcv35d7e1SVjet/fz8CA8PJyoqSi2zfft2bGxs6NChg+lfQhkwa9ejTp06sXnzZqZNm8bs2bNp1KgRixYt4sUXX1TLTJ06ldTUVMaNG0d8fDydO3dm+/btODo6qmUWLlyIpaUlzz33HKmpqfTu3ZtVq1ap01EBrF27lokTJ6oDTYYOHcqSJUsq7sMKIYQQQohKZfz48axbt46ffvoJR0dH9Y6+VqvFzs6OpKQkgoODeeaZZ/D09OTKlStMnz4dV1dX/vGPf6hl9TetXVxccHZ2ZsqUKQXetP7iiy8A3fSouW9a9+vXDx8fHwIDA5k3bx53795lypQpjBkzxiwzHoGZEwUAf39//P39C9yv0WgIDg4utF+Zra0tixcvLnSOWWdnZ9asWVOaUIUQQgghRDWybNkyAHr27GmwfeXKlYwaNQoLCwtOnz7NN998w7179/D09KRXr15s3LixzG9aW1hYsG3bNsaNG0e3bt2ws7MjICCATz/9tBy/gcKZPVEQQgghhBDCHBRFKXS/nZ0dv/32W5H1lNVN6wYNGrB169Yiz1dRzDpGQQghhBBCCFE5SaIghBBCCCGEMCKJghBCCCGEEMKIJApCCCGEEEIII5IoCCGEEEIIIYxIoiCEEEIIIYQwIomCEEIIIYQQwoisoyCEEEKIau3EyZMEDR9usM3e3Z05S5eaKSIhqgZJFIQQQghRrVmkpbHI19dgW1B4uJmiEaLqkK5HQgghhBBCCCOSKAghhBBCCCGMSKIghBBCCCGEMCKJghBCCCGEEMKIJApCCCGEEEIII5IoCCGEEEIIIYxIoiCEEEIIIYQwIomCEEIIIYQQwogkCkIIIYQQQggjkigIIYQQQgghjFiaOwAhhBBCiIp24uRJgoYPN9pu7+7OnKVLzRCREJWPJApCCCGEeOhYpKWxyNfXaHtQeLgZohGicpKuR0IIIYQQQggjkigIIYQQQgghjEiiIIQQQgghhDAiiYIQQgghhBDCiCQKQgghhBBCCCMy65EQQgghRA6ZNlWIv0miIIQQQoiHS3IynpmZoCig0RjskmlThfibJApCCCGEeDikp8P+/XDoEOuys2H+fGjQADp2hMaNzR2dEJWOJApCCCGEqP4uX4YtWyAhAYBMwDI5Gc6ehXPnYMgQaN/evDEKUcnIYGYhhBBCVGvumZmwfr0uSdBqYcQIBnl5wSuvQOvWui5IW7bAwYPmDlWISkVaFIQQQghRfSkKb927B5mZum5GL74I1tZk/P677n39+lCzJhw6BDt2MKJWLXNHLESlIS0KQgghhKi+zp7FLy0NatTQdS+ytjbcr9FAv37w5JMAjL5/H27dMkOgQlQ+kigIIYQQonpKS4OQEN3rbt3A1bXgso8/Dj4+uq4WP/6oa4EQ4iFn1kQhODgYjUZj8PDw8FD3K4pCcHAwXl5e2NnZ0bNnT86cOWNQR1paGhMmTMDV1RUHBweGDh3KjRs3DMrEx8cTGBiIVqtFq9USGBjIvXv3KuIjCiGEEMJc9u2DxERuWlhA9+6Fl9VoYPBg7taoAXFxsHt3xcQoRCVm9haFRx55hKioKPVx+vRpdd8nn3zCggULWLJkCceOHcPDw4O+ffuSmJiolgkKCmLz5s1s2LCBAwcOkJSUhL+/P1lZWWqZgIAAwsLCCAkJISQkhLCwMAIDAyv0cwohhBCiAmVmwokTACzVasHKquhj7O2ZV7u27vWhQ3D1avnFJ0QVYPZEwdLSEg8PD/VRp04dQNeasGjRImbMmMGwYcPw9fVl9erVpKSksG7dOgASEhJYsWIF8+fPp0+fPrRv3541a9Zw+vRpdu7cCcDZs2cJCQnhq6++ws/PDz8/P5YvX87WrVs5f/682T63EEIIIcrR+fO6rke1anHY1rbYhx22s4N27XRvfvtNNyOSEA8psycKFy5cwMvLi0aNGjFixAguX74MQGRkJNHR0fTr108ta2NjQ48ePTiYM31ZaGgoGRkZBmW8vLzw9fVVyxw6dAitVkvnzp3VMl26dEGr1apl8pOWlsb9+/fVR+5WDCGEEEJUcqdO6Z7btEHJs/pykfr00Q16joqCiIiyj02IKsKsiULnzp355ptv+O2331i+fDnR0dF07dqVO3fuEB0dDYC7u7vBMe7u7uq+6OhorK2tcXJyKrSMm5ub0bnd3NzUMvmZO3euOqZBq9Xi4+NTqs8qhBBCiAqSnAwXLuhet2lj+vEODtC1q+71rl2QqzuzEA8TsyYKAwcO5JlnnqF169b06dOHbdu2AbB69Wq1jCbPXQBFUYy25ZW3TH7li6pn2rRpJCQkqI8IuaMghBBCVA2nT+u6DHl5QU6XZpP5+ekShvh4dayDEA8bs3c9ys3BwYHWrVtz4cIFdfajvHf9Y2Nj1VYGDw8P0tPTiY+PL7RMTEyM0bni4uKMWitys7GxoVatWurD0dGxVJ9NCCGEEBVE3+2obduS12FtDT166F7v24dNRkbp4xKiiqlUiUJaWhpnz57F09OTRo0a4eHhwY4dO9T96enp7Nu3j645zYEdOnTAysrKoExUVBTh4eFqGT8/PxISEjh69Kha5siRIyQkJKhlhBBCCFFNxMbqxhbUqAG+vqWr69FHwdkZkpPpmjOGUoiHiaU5Tz5lyhSGDBlCgwYNiI2N5YMPPuD+/fuMHDkSjUZDUFAQc+bMoVmzZjRr1ow5c+Zgb29PQEAAAFqtltGjRzN58mRcXFxwdnZmypQpalcmgFatWjFgwADGjBnDF198AcDYsWPx9/enRYsWZvvsQgghhCgH+mnWmzUDe/vS1WVhoVuIbcsWel68qJtFycam9DEKUUWYNVG4ceMGL7zwArdv36ZOnTp06dKFw4cP4+3tDcDUqVNJTU1l3LhxxMfH07lzZ7Zv327QDWjhwoVYWlry3HPPkZqaSu/evVm1ahUWFhZqmbVr1zJx4kR1dqShQ4eyZMmSiv2wQgghhCh/+jv/rVqVTX1t2sCePWgTE2HNGhg9umzqFaIKMGvXow0bNnDr1i3S09O5efMmP/zwg8HsQhqNhuDgYKKionjw4AH79u3DN08zoq2tLYsXL+bOnTukpKTw888/U79+fYMyzs7OrFmzRp3qdM2aNdTWL6gihBBCiGrBITtb1+0IoFGjsqnUwgK6dNG9njcPsrPLpl5RKcydO5dOnTrh6OiIm5sbTz/9tNE6W4qiEBwcjJeXF3Z2dvTs2ZMzZ84YlElLS2PChAm4urri4ODA0KFDuXHjhkGZ+Ph4AgMD1Rk1AwMDuXfvnkGZa9euMWTIEBwcHHB1dWXixImkp6eXy2cvjko1RkEIIYQQoqTapKXpZjtydoZatcqu4g4dSLGy0i3i9tNPZVevMLt9+/Yxfvx4Dh8+zI4dO8jMzKRfv34kJyerZT755BMWLFjAkiVLOHbsGB4eHvTt29dgja2goCA2b97Mhg0bOHDgAElJSfj7+5OVa2rdgIAAwsLCCAkJISQkhLCwMAIDA9X9WVlZDB48mOTkZA4cOMCGDRv44YcfmDx5csV8Gfkwa9cjIYQQQoiy0j4tTfeirFoT9GxsONCkCf3OnYOPPoKnnwZTF3ETlVJISIjB+5UrV+Lm5kZoaChPPPEEiqKwaNEiZsyYwbBhwwDdNP7u7u6sW7eO119/nYSEBFasWMG3336rjpFds2YN9evXZ+fOnfTv35+zZ88SEhLC4cOH1UWAly9fjp+fH+fPn6dFixZs376diIgIrl+/jpeXFwDz589n1KhRfPjhh9Qqy+S3mKRFQQghhBDVQnt9F42GDcu87v1NmoCtLRw9Cn/8Ueb1i8ohISEB0HVbB4iMjCQ6Olod5wq6KfR79OjBwYMHAQgNDSUjI8OgjJeXF76+vmqZQ4cOodVq1SQBoEuXLmi1WoMyvr6+apIA0L9/f9LS0ggNDS2nT1w4SRSEEEIIUfWlpNBUv9ZBOSQKSba28OKLujcyIUqll5iYqI5NvX//Pmn61qZCKIrCpEmTePzxx9Uxsfr1vPKuveXu7q7ui46OxtraGicnp0LLuLm5GZ3Tzc3NoEze8zg5OWFtbW20rlhFkURBCCGEEFXflSu65zp1oGbNMq/+xMmTfJJzjqzvvmPm4MFMHz++zM8jyoaPj486aFir1TJ37twij3nzzTc5deoU69evN9qnydPVTFEUo2155S2TX/mSlKlIkigIIYQQouqLjNQ9l/X4hBwWaWlMffxxaNAAC0VhdlISKTEx5XIuUXoREREkJCSoj2nTphVafsKECWzZsoU9e/ZQr149dbuHhweA0R392NhY9e6/h4cH6enpxMfHF1omJp+fl7i4OIMyec8THx9PRkaGUUtDRZFEQQghhBBVn75FoRy6HRl47DHdc2goFjJVaqXl6OhIrVq11IdNAQvlKYrCm2++yaZNm9i9ezeN8iSajRo1wsPDgx07dqjb0tPT2bdvH127dgWgQ4cOWFlZGZSJiooiPDxcLePn50dCQgJHjx5Vyxw5coSEhASDMuHh4UTpp/gFtm/fjo2NDR06dCjlN1IyMuuREEIIIaq2xES4fZtsoEZ5JwotW4KjIyQm0jbPPPmi6hk/fjzr1q3jp59+wtHRUb2jr9VqsbOzQ6PREBQUxJw5c2jWrBnNmjVjzpw52NvbExAQoJYdPXo0kydPxsXFBWdnZ6ZMmULr1q3VWZBatWrFgAEDGDNmDF988QUAY8eOxd/fnxYtWgDQr18/fHx8CAwMZN68edy9e5cpU6YwZswYs8x4BJIoCCGEEKKqu3oVgItWVjS3syvfc1lYQIcOsHcvT1y6VL7nEuVu2bJlAPTs2dNg+8qVKxk1ahQAU6dOJTU1lXHjxhEfH0/nzp3Zvn07jo6OavmFCxdiaWnJc889R2pqKr1792bVqlVYWFioZdauXcvEiRPV2ZGGDh3KklwD4y0sLNi2bRvjxo2jW7du2NnZERAQwKefflpOn75okigIIYQQomq7eROAcGtrmlfE+Tp0gP37aXj3LoSFQbt2FXFWUQ4URSmyjEajITg4mODg4ALL2NrasnjxYhYvXlxgGWdnZ9asWVPouRo0aMDWrVuLjKmiSKIghBBCiKotp0/3X1ZWFXO+mjWhVSs4c4bfn3mGH9q3N9ht7+7OnKVLKyYWIcqRJApCCCGEqLoU5e9Ewdq64s776KNw5gzdb96k+/PPQ65zB4WHV1wcQpQjmfVICCGEEFXX3buQng6Wlly1rMD7n40acdPCAtLS4MyZijuvEBVIEgUhhBBCVF23bumePTzIrshFqTQatjo46F6fOFFx5xWiAkmiIIQQQoiqSz/nfM7CWBXpN3t7qFEDbtwAWXxNVEMmtdElJCSwefNmfv/9d65cuUJKSgp16tShffv29O/fX10wQgghhBCiQugTBS+vv19XkHgLC926ChEREBoKgwZV6PmFKG/FalGIiopizJgxeHp6Mnv2bJKTk2nXrh29e/emXr167Nmzh759++Lj48PGjRvLO2YhhBBCCIOBzHh6mieGRx/VPZ8+DZmZ5olBiHJSrBaFtm3b8vLLL3P06FF8fX3zLZOamsqPP/7IggULuH79OlOmTCnTQIUQQgghDMTH6wYTW1hAnTrmiaFxY6hVC+7fh3PnoIDrJCGqomIlCmfOnKFOEb+AdnZ2vPDCC7zwwgvExcWVSXBCCCGEEAXStya4u+uSBXPQaKBtW/j9d/jzT0kURLVSrK5HRSUJpS0vhBBCCGEyc3c70tOvzHzpkq5lQYhqolgtClu2bGHgwIFYWVmxZcuWQssOHTq0TAITQgghhChUZUkUnJ2hQQO4dg1OnYLatc0bjxBlpFiJwtNPP010dDRubm48/fTTBZbTaDRkZWWVVWxCCCGEEPmrDAOZc2vXTpcohIVBjx7mjkaIMlGsrkfZ2dm4ubmprwt6SJIghBBCiIrgnpUFqam6dQxyrlHMyscHrKzgzh0a3r1r7miEKBOy4JoQQgghqpwmGRm6F3XqgKVJy0KVDxsbXbIAPHblinljEaKMlOg3a9euXezatYvY2Fiys7MN9n399ddlEpgQQgghREG89WsWVIbWBL127eDPP2l/4wakpIC9vbkjEqJUTG5ReO+99+jXrx+7du3i9u3bxMfHGzyEEEIIIcpbw9wtCpWFtzfUro1dZiZs3mzuaIQoNZNbFD7//HNWrVpFYGBgecQjhBBCCFEktUWhMiUK+jUV9u2DVavgxRfNHZEQpWJyi0J6ejpdu3Ytj1iEEEIIIYqmKDSojIkC6BIFgF27dLMgCVGFmZwovPbaa6xbt648YhFCCCGEKFpCAnaKoluN2cnJ3NEYcnLiQp06uulbv/nG3NEIUSomdz168OABX375JTt37qRNmzZYWVkZ7F+wYEGZBSeEEEIIYSQuTvfs4qKbHrWSOertTbO4OF33oxkzdF2SKrmTJ08yfPhwo+3u7u4sXbrUDBGJysDkROHUqVO0y1mqPDw83GCfpgr8IgghhBCiitMnCpWt21GOP+vW5cVz5+DSJThwALp3N3dIRUpLS8PX19doe95rPfFwMTlR2LNnT3nEIYQQQghRPPpEwdXVvHEUIN3SEp59FlauhNWrq0SiIER+SrVCyY0bN9BoNNStW7es4hGi0pg+fjwpMTEG2+zd3ZkjTbBCCGFelbxFAYDAQF2i8P33sGQJ2NqaOyIhTGZyx77s7Gxmz56NVqvF29ubBg0aULt2bd5//32jxdeEqMpSYmJY5Otr8MibOAghhKhgilI1EoUePaBePUhIgF9+MXc0QpSIyYnCjBkzWLJkCR999BEnT57kxIkTzJkzh8WLF/Puu++WR4xCCCGEEDqJiZCeThboBjNXVjVqQECA7vWaNeaNRYgSMjlRWL16NV999RX//Oc/adOmDW3btmXcuHEsX76cVatWlTiQuXPnotFoCAoKUrcpikJwcDBeXl7Y2dnRs2dPzpw5Y3BcWloaEyZMwNXVFQcHB4YOHcqNGzcMysTHxxMYGIhWq0Wr1RIYGMi9e/dKHKsQQgghzCSnNeGGpaVuetTK7KWXdM/btkF8vHljEaIETE4U7t69S8uWLY22t2zZkrt375YoiGPHjvHll1/Spk0bg+2ffPIJCxYsYMmSJRw7dgwPDw/69u1LYmKiWiYoKIjNmzezYcMGDhw4QFJSEv7+/mRlZallAgICCAsLIyQkhJCQEMLCwmRlaSGEEKIqykkUrlqWaphluTpx8iRBw4cT9N573KpVC9LT2dCnD9PHjzd3aEKYxOTfsrZt27JkyRL++9//GmxfsmQJbfWrEZogKSmJF198keXLl/PBBx+o2xVFYdGiRcyYMYNhw4YButYMd3d31q1bx+uvv05CQgIrVqzg22+/pU+fPgCsWbOG+vXrs3PnTvr378/Zs2cJCQnh8OHDdO7cGYDly5fj5+fH+fPnadGihckxCyGEEMJMchKFK1ZWPGHmUApikZbGIv1UowkJsHMnI+7c4bCMcxPlJCEhgc2bN/P7779z5coVUlJSqFOnDu3bt6d///507dq1RPWa3KLwySef8PXXX+Pj48Po0aN57bXX8PHxYdWqVcybN8/kAMaPH8/gwYPVC329yMhIoqOj6devn7rNxsaGHj16cPDgQQBCQ0PJyMgwKOPl5YWvr69a5tChQ2i1WjVJAOjSpQtarVYtk5+0tDTu37+vPnK3YgghhBDCTKpAi4IBfcJw9SpOycnmjUVUO1FRUYwZMwZPT09mz55NcnIy7dq1o3fv3tSrV489e/bQt29ffHx82Lhxo8n1m/xb1qNHD/766y+WLl3KuXPnUBSFYcOGMW7cOLy8vEyqa8OGDZw4cYJjx44Z7YuOjgZ0KwLm5u7uztWrV9Uy1tbWOOVZvt3d3V09Pjo6Gjc3N6P63dzc1DL5mTt3Lu+9955Jn0cIIYQQ5SjXjEdXrazMHEwxabXQsCFcuUKH69fNHY2oZtq2bcvLL7/M0aNH810wDyA1NZUff/yRBQsWcP36daZMmVLs+k1KFPR377/44gs+/PBDUw41cv36dd566y22b9+ObSFzC+dd7VlRlCJXgM5bJr/yRdUzbdo0Jk2apL6/efMmPj4+hZ5XVE35rZcAEH7y5N93goQQQphfSgo8eADA9arSogDQurUuUbh2TZfsFHEdI0RxnTlzhjpFTBNsZ2fHCy+8wAsvvECcfmrhYjLpt8zKyorw8PAiL9SLIzQ0lNjYWDp06KBuy8rKYv/+/SxZsoTz588DuhYBT09PtUxsbKzayuDh4UF6ejrx8fEGrQqxsbFqXywPDw9i8rkIjIuLM2qtyM3GxgYbGxv1/f3790v4SUVlp18vIa9ehw+bIRohhBAFunNH96zVkl6VLrZ9fOCXX/BMTIQ//4R27cwdkagmikoS9FJSUrC3ty92eT2Txyi8/PLLrFixwtTDjPTu3ZvTp08TFhamPjp27MiLL75IWFgYjRs3xsPDgx07dqjHpKens2/fPjUJ6NChA1ZWVgZloqKiCA8PV8v4+fmRkJDA0aNH1TJHjhwhISGhxAM7hBBCCGEG+kShMq+fkB9bW9BPniJrKogK9ODBA+bPn0/jxo1LdLzJ7Xbp6el89dVX7Nixg44dO+Lg4GCwf8GCBcWqx9HR0agvlYODAy4uLur2oKAg5syZQ7NmzWjWrBlz5szB3t6egJwFTLRaLaNHj2by5Mm4uLjg7OzMlClTaN26tTo4ulWrVgwYMIAxY8bwxRdfADB27Fj8/f1lxiMhhBCiKtFPw+7sDFFR5o3FVK1bQ0QErF8PH39c+deAEFVGeno67733Htu3b8fKyoqpU6fy9NNPs3LlSmbMmIFGo+Gtt94qUd0mJwrh4eE8+uijAPz1118G+8qiS1JuU6dOJTU1lXHjxhEfH0/nzp3Zvn07jo6OapmFCxdiaWnJc889R2pqKr1792bVqlVY5PoFXLt2LRMnTlRnRxo6dChLliwp01iFEEIIUc6qcqLQrBnJVlY43LoFe/dC797mjkhUE8HBwSxdupS+ffvyxx9/8Oyzz/Lqq6+yd+9e5s6dS0BAAFYlHPxvUqKQlZVFcHAwrVu3xtnZuUQnLMzevXsN3ms0GoKDgwkODi7wGFtbWxYvXszixYsLLOPs7MwaaeoTQgghqraq2vUIwMKCsHr16BYZqet+JImCKCPfffcdq1at4h//+Ad//vkn7du35/79+5w5cwbLUg76N2mMgoWFBf379ychIaFUJxVCCCGEMImiGLYoVEHHGzTQvfjhB90MTkKUgevXr9OpUydAN12qtbU177zzTqmTBCjBYObWrVtz+fLlUp9YCCGEEKLYkpIgI0M3tWie9ZOqiisuLuDtDYmJ8Msv5g5HVBMZGRlYW1ur762srNBqtWVSt8mpxocffsiUKVN4//336dChg9Fg5lq1apVJYEIIIYQQqlxTo1bVgcCKRgMjRugGM2/YAMOHmzskUU3MnDkTe3t7QDe4+YMPPjBKFoo74VBuJicKAwYMAHQDgnMPXtYvYJaVlWVyEEIIIYQQhdJ3O6qK4xNy0ycK27bB/fsgN1hFKT3xxBPq+mMAXbt2Ner9U9IJh0xOFPbs2VOiEwkhhBBClFgVH5+gattWt6bC+fOwZQu89JK5IxJVXN7JgMqSyYlCjx49yiMOIYQQQoiCVYNE4cTJkwQ9+ywDatRgAHDmX/9i7aFDzFm61NyhCZEvkwczA/z++++89NJLdO3alZs3bwLw7bffcuDAgTINTgghhBACqNpTo+awSEtjka8vA3r1AuCR2Fi4ds3MUYnqIDk5mZkzZ+Lr60vNmjVxdHSkTZs2zJ49m5RSzLBlcqLwww8/0L9/f+zs7Dhx4gRpaWkAJCYmMmfOnBIHIoQQQgiRr2owNaqBOnXA3R2ys2lz65a5oxFVXHp6Oj169OCTTz6hWbNmTJgwgfHjx9OoUSM+/PBDevfuTUZGRonqNjlR+OCDD/j8889Zvny5wSpvXbt25cSJEyUKQgghhBCiQImJkJmpmxq1dm1zR1M2fH0BePT6dTMH8nDbv38/Q4YMwcvLC41Gw48//miwf9SoUWg0GoNHly5dDMqkpaUxYcIEXF1dcXBwYOjQody4ccOgTHx8PIGBgWi1WrRaLYGBgdy7d8+gzLVr1xgyZAgODg64uroyceJE0tPTi/wMy5Yt48aNG/z5559s3ryZuXPn8tFHH/HTTz/x559/EhkZyeeff16i78fkMQrnz5/niSeeMNpeq1Ytow8shBBCCFFq+m5HTk5VdmpUI76+sGsXTePimDl4MPft7Ax227u7y9iFCpCcnEzbtm155ZVXeOaZZ/ItM2DAAFauXKm+z71mAUBQUBA///wzGzZswMXFhcmTJ+Pv709oaCgWOT+vAQEB3Lhxg5CQEADGjh1LYGAgP//8MwBZWVkMHjyYOnXqcODAAe7cucPIkSNRFIXFixcX+hk2bdrEu+++S4sWLYz2tWzZkhkzZvD9998zYcKE4n8xOUxOFDw9Pbl48SINGzY02H7gwAEaN25scgBCCCGEEIWqTt2O9GrXhnr1qHHjBrOzs9UWBr2g8HDzxPWQGThwIAMHDiy0jI2NDR4eHvnuS0hIYMWKFXz77bf06dMHgDVr1lC/fn127txJ//79OXv2LCEhIRw+fJjOnTsDsHz5cvz8/Dh//jwtWrRg+/btREREcP36dby8vACYP38+o0aN4sMPPyx0nbKIiAh69uxZ4P5evXoxe/bsQj9jQUzuevT666/z1ltvceTIETQaDbdu3WLt2rVMmTKFcePGlSgIIYQQQogC6VsUqlOiAPDII7rnM2fMG4co1N69e3Fzc6N58+aMGTOG2NhYdV9oaCgZGRn069dP3ebl5YWvry8HDx4E4NChQ2i1WjVJAOjSpQtardagjK+vr5okAPTv35+0tDRCQ0MLje/evXu4FDLI38XFhYSEBNM+dA6TWxSmTp1KQkICvXr14sGDBzzxxBPY2NgwZcoU3nzzzRIFIYQQQghRoOrYogDwyCNk//YbNa5fh3v3qs/4i0ogMTGR+/fvq+9tbGywsbExuZ6BAwfy7LPP4u3tTWRkJO+++y5PPvkkoaGh2NjYEB0djbW1NU5OTgbHubu7Ex0dDUB0dDRubm5Gdbu5uRmUcXd3N9jv5OSEtbW1WqYg2dnZahen/NSoUaPECyIXK1E4deoUvr6+1Kiha4D48MMPmTFjBhEREWRnZ+Pj40PNmjVLFIAQQgghRKGqy6rMeTk68qe1Ne3T03WtCt26mTuiasPHx8fg/axZswgODja5nueff1597evrS8eOHfH29mbbtm0MGzaswOMURTFYDTm/lZFLUqagc/Xu3RtLy/wv6zMzMws9vjDFShTat29PVFQUbm5uNG7cmGPHjuHi4kLHjh1LfGIhzGH6+PGkxMQYbAs/edKob6gQQojKQaMoEB+ve1PdWhSAPfb2ukQhPFwShTIUERFB3bp11fclaU3Ij6enJ97e3ly4cAEADw8P0tPTiY+PN2hViI2NpWvXrmqZmDzXHgBxcXFqK4KHhwdHjhwx2B8fH09GRoZRS0Nes2bNKjLuggZqF6VYiULt2rWJjIzEzc2NK1eukJ2dXaKTCWFuKTExLMqTFPQ6fNhM0QghhCiK44MHf0+NqtWaO5wyt8/WlkkaDURHw+3b4Opq7pCqBUdHx0IHAJfUnTt3uH79Op6engB06NABKysrduzYwXPPPQdAVFQU4eHhfPLJJwD4+fmRkJDA0aNHeeyxxwA4cuQICQkJajLh5+fHhx9+SFRUlFr39u3bsbGxoUOHDoXGVJxEoaSKlSg888wz9OjRA09PTzQaDR07diywL9Tly5fLNEAhhBBCPLxckpN1L7Ta6jM1ai73LSygSRO4eFHX/ahHD3OH9FBJSkri4sWL6vvIyEjCwsJwdnbG2dmZ4OBgnnnmGTw9Pbly5QrTp0/H1dWVf/zjHwBotVpGjx7N5MmTcXFxwdnZmSlTptC6dWt1FqRWrVoxYMAAxowZwxdffAHopkf19/dXpzTt168fPj4+BAYGMm/ePO7evcuUKVMYM2ZMkQnP7t27eeKJJwrselQaxarxyy+/ZNiwYVy8eJGJEycyZswYHB0dyzwYIYQQQojc1EQhz2DRauWRR3SJQni4JAoV7Pjx4/Tq1Ut9P2nSJABGjhzJsmXLOH36NN988w337t3D09OTXr16sXHjRoPr4IULF2Jpaclzzz1HamoqvXv3ZtWqVQY31deuXcvEiRPV2ZGGDh3KkiVL1P0WFhZs27aNcePG0a1bN+zs7AgICODTTz8t8jP07dtXHSIAuhmVfvjhB4OuVyVV7NRjwIABgG4aqLfeeksSBSGEEEKUOzVRqM4zArVsCT//rOt6FBcHdeqYO6KHRs+ePVEUpcD9v/32W5F12Nrasnjx4kIXRnN2dmbNmjWF1tOgQQO2bt1a5Pnyyhv/mTNnSEtLM7me/Ji8jsLKlSslSRBCCCFEhXgoWhRsbXXdj0DWVBCVismdmZKTk/noo4/YtWsXsbGxRgObZYyCEEIIIcrKQ5EoAPj4wIULcPYsFLLKrhB5aTQao2lWi5pStbhMThRee+019u3bR2BgoDq4WQghhBCiPDw0iUKLFlCjBsTG6rogCVFMeddRSElJYciQIVhbWxuUO3HihMl1m5wo/Prrr2zbto1uMtevEEIIIcpTaiq1HzzQva7uiYKdHTRu/PfsR9VtcTlRbvJOj/rUU0+VWd0mJwpOTk44V8MFT4QQQghRyVy5onu2ttZdSFd3Pj66ROHsWXj8cXNHI6qI8lxHweTBzO+//z4zZ84kJSWlPOIRQgghhNCJjNQ9OznpFlyr7lq21HU/iomhTmKiuaMRwvQWhfnz53Pp0iXc3d1p2LAhVlZWBvtL0v9JCCGEEMKIfoKU6t7tSM/ODho1gkuXaHvzprmjEVXAgAEDmDlzprrCc0ESExP57LPPqFmzJuPHjy92/SYnCk8//bSphwghhBBCmO5hSxRA1/3o0iXa3bhh7khEFfDss8/y3HPP4ejoyNChQ+nYsSNeXl7Y2toSHx9PREQEBw4c4JdffsHf35958+aZVL/JiUJ59oMSQgghhFA9jIlCy5awdSv1EhJ04xWaNjV3RKISGz16NIGBgXz//fds3LiR5cuXc+/ePUA3TaqPjw/9+/cnNDSUFi1amFy/yYmCXmhoKGfPnlWDaN++fUmrEkIIIYQwpk8UqvOqzHnZ2+u6H12+DN9/D//+t7kjEpWctbU1AQEBBAQEAJCQkEBqaiouLi5GQwRMZXKiEBsby4gRI9i7dy+1a9dGURQSEhLo1asXGzZsoI4sOy6EEEKI0lKUh7NFAXTdjy5fhv/9r0IShRs3bpCRkVHu5xEVQ6vVotVqy6Quk2c9mjBhAvfv3+fMmTPcvXuX+Ph4wsPDuX//PhMnTiyToIQQQgjxkIuLg+RksuHhalEAaNmSLI0GTpz4O1kqJ9euXWPFihXExcVx//79cj2XqHpMThRCQkJYtmwZrVq1Urf5+PiwdOlSfv311zINTgghhBAPqZwL5AQ7O7AscU/pqsnBgYuurrrX339fbqdJT0/np59+AnSr++7atavcziWqJpMThezs7Hz7O1lZWZGdnV0mQQkhhBDiIZezhsIdBwczB2Ief9arp3vxv/+V2zl27drF3bt3sbe3B+DUqVNcv3693M4nqh6TE4Unn3ySt956i1u3bqnbbt68ydtvv03v3r3LNDghhBBCPKRyWhQe1kThlJeXbvG148f/XqG6DF25coWjR48CMGzYMDVZCAkJQVGUMj+fqJpMbstbsmQJTz31FA0bNqR+/fpoNBquXbtG69atWbNmTXnEKITJpo8fT0pMjNH28JMnwdfXDBEJIYQwyUOeKCTZ2kKPHrBnj6770ZQpZVr/tm3bAHj00Udp0qQJtWrVIjMzk1u3bvHnn3/Srl27Mj2fKD9ff/01L774IjY2NmVet8ktCvXr1+fEiRNs27aNoKAgJk6cyC+//EJoaCj19M1kxbRs2TLatGlDrVq1qFWrFn5+fgbjHBRFITg4GC8vL+zs7OjZsydnzpwxqCMtLY0JEybg6uqKg4MDQ4cO5UaeRUri4+MJDAxUR4EHBgaqc8yK6iklJoZFvr5Gj6y0NHOHJoQQojge8kQBgGee0T1v3lym1d6/f5/bt2+j0Wjo27cvABYWFjzxxBMAHDp0qEzPJ8rXmDFjSEhIUN97eXlxpYxaoUxOFPT69u3LhAkTmDhxIn369ClRHfXq1eOjjz7i+PHjHD9+nCeffJKnnnpKTQY++eQTFixYwJIlSzh27BgeHh707duXxMREtY6goCA2b97Mhg0bOHDgAElJSfj7+5OVlaWWCQgIICwsjJCQEEJCQggLCyMwMLCkH10IIYQQ5e0hTxROnDzJrF9+ASD74EHe9fcnaPhwpo8fX+q69TdU3d3dsbW1Vbe3bdsW0E2F/+DBg1KfR1SMvF3FEhMTy2zcsEmJQmJiIqGhoSQlJQFw4sQJXn75ZZ599lnWrl1r8smHDBnCoEGDaN68Oc2bN+fDDz+kZs2aHD58GEVRWLRoETNmzGDYsGH4+vqyevVqUlJSWLduHaBbUGLFihXMnz+fPn360L59e9asWcPp06fZuXMnAGfPniUkJISvvvoKPz8//Pz8WL58OVu3buX8+fMmxyyEEEKIcpaeDjmDah/WRMEiLY33OnWCunWpAbwPLPL1zbdbran0iULeniA1a9akds5UtDdv3iz1eUTVV+xEYf/+/dStW5dOnTrh7e3N9u3b6dmzJ8eOHePs2bO8/PLLLF++vMSBZGVlsWHDBpKTk/Hz8yMyMpLo6Gj69eunlrGxsaFHjx4cPHgQ0K0OnZGRYVDGy8sLX19ftcyhQ4fQarV07txZLdOlSxe0Wq1aRgghhBCVyNWrugXX7OxILId+11VKy5a653PnyqzKghIF0HUxz11GVH4ajQaNRlPg+9IodqLwf//3fzz77LNcu3aNoKAgnn/+ed58803Onj1LeHg47733HkuXLjU5gNOnT1OzZk1sbGx444032Lx5Mz4+PkRHRwO6ZrHc3N3d1X3R0dFYW1vjlGfFxrxl3NzcjM7r5uamlslPWloa9+/fVx+5uzsJIYQQohzpFxlr3BjK6IKnytKvWxUZCWXQHSg7O1uduTK/REG/TRKFqkNRFJo3b46zszPOzs4kJSXRvn179b3+URLFnvXo1KlTfPnll9SrV4933nmH4OBgnn/+eXX/iBEj+Pjjj00OoEWLFoSFhXHv3j1++OEHRo4cyb59+9T9eTMiRVGKzJLylsmvfFH1zJ07l/fee6+4H0MIIYQQZSVnDQUaNzZvHJWBiwvUqaNbqfqvv3RTppbCvXv3yMrKws7OLt+Lx9yJgkyTWjWsXLmy3OoudqJw//599QfK2toae3t7HB0d1f2Ojo6kpKSYHIC1tTVNmzYFoGPHjhw7doz//Oc/vPPOO4CuRcDT01MtHxsbq7YyeHh4kJ6eTnx8vEGrQmxsLF27dlXLxOTTny8uLs6otSK3adOmMWnSJPX9zZs38fHxMfnzCSGEEMJEuVsU5M62rvtRXJyu+1Epr0Xu3r0L6BKC/G6Yuru7Y2lpyYMHD7hz506pziUqxsiRI8ut7mKnpeXZ/yk3RVFIS0ujUaNGeHh4sGPHDnVfeno6+/btU5OADh06YGVlZVAmKiqK8PBwtYyfnx8JCQnqoiIAR44cISEhQS2THxsbG3Xa1lq1ahkkRUIIIYQoR7kTBfH3OIWLF7HKNatjSegThbp16+a738LCAi8vL0C6H1U1iqJw/Phxvv/+e3744QdOnDhR6lahYrcoKIpC7969sbTUHZKSksKQIUOwtrYGIDMz0+STT58+nYEDB1K/fn0SExPZsGEDe/fuJSQkBI1GQ1BQEHPmzKFZs2Y0a9aMOXPmYG9vT0BAAABarZbRo0czefJkXFxccHZ2ZsqUKbRu3VqdsrVVq1YMGDCAMWPG8MUXXwAwduxY/P39adGihckxCyGEEKKc5U4U9u83byyVgacnaLWQkECLUs56pG8l0A9azk+9evW4du0aN27coGHDhqU6n6gYe/bsYfTo0Vy9elVNDjQaDY0aNeLrr79W18gwVbEThVmzZhm8f+qpp4zKPKNfGKSYYmJiCAwMJCoqCq1WS5s2bQgJCVEX/5g6dSqpqamMGzeO+Ph4OnfuzPbt2w3u7i9cuBBLS0uee+45UlNT6d27N6tWrcLCwkIts3btWiZOnKjOjjR06FCWLFliUqxCCCGEqACKApcu6V5Li4KORqNrVThyhDY5A5FLIiYmRu0mrm81yE/ucQqSKFR+Fy9exN/fn86dO7Nw4UJatmyJoihERETw3//+l0GDBnHq1Ckal+D3qcSJQllYsWJFofs1Gg3BwcEEBwcXWMbW1pbFixezePHiAss4OzuzZs2akoYphBBCiIoSHw/37+tey0Xq33IShUeioiAzEyyLfQmnOnz4MAB16tQxWGgtL32iEBsbS0ZGRsniFRVm0aJFdOnShV27dhlsb9myJf/4xz/o06cPCxcuLPRauSClGzovhBBCCFGW9N2OPDzA3t68sVQmDRqAvT0O6ekl7o6lTxTymxY1N0dHR7RaLYqiEB8fX6JziYqzd+9egoKC8t2n78q/Z8+eEtVdrHR0wIABzJw5s9DBv6Bbufmzzz6jZs2ajC+DJcaFEEII8ZCRgcz5q1EDmjeHsDD2jx3Lpnbt1F327u7MKcZaVkeOHAGKThT0ZRISEtTBz6LyunbtGq1bty5wv6+vL1evXi1R3cVKFJ599lmee+45HB0dGTp0KB07dsTLywtbW1vi4+OJiIjgwIED/PLLL/j7+zNv3rwSBSOEEEKIh5ysoVCwVq0gLIwnYmN54pFH1MXogsLDi3X4uZzVnQubHl6vbt26nDlzRloUqoCkpCTsC2l9s7e3L9ESBlDMRGH06NEEBgby/fffs3HjRpYvX869e/cAXZOGj48P/fv3JzQ0VGYSEkIIIUTJSYtCwRo3JkWjwT4xEW7dggKmOM1PUlISUVFRAMVapbdOnTqArreIqPwiIiKIjo7Od9/t27dLXG+xR8JYW1sTEBCgTk2akJBAamoqLi4uWFlZlTgAIYQQQgiVJAoFs7TkiK0tvVJT4exZkxKFixcvArrrOTs7uyLLu7q6ApCcnExmZqY6Pb6onHr37p3vmgkajQZFUUq89lmJ/9W1Wi1arbakhwtR8W7fxjsjQzf1XjksFiiEEKIMSKJQqN/1icK5c5CzZlRx/PXXXwDUrFmzWOW1Wi2WlpZkZmZy5coVmjZtWqJ4RfmL1HfXKweSHorqLy4Odu2C8+dZBbB0Kfj4QKdOICtuCyFE5ZGZCfpBl5Io5OuIrS1YWMCdO7q/bzldhIpy4cIFoPiJgkajwcXFhZiYGM6dOyeJQiXm7e1dbnXL9Kii+lIU2LEDli2D8+dBoyEddP+5/v47rFgBJRzcI4QQohxcvw5ZWWBjo1uNWBhJqVEDGjXSvTl/vtjHmZoowN/dj86bcB5R8VJSUhg/fjx169bFzc2NgICAUo1LyE0SBVF9HT8OBw/qEoaWLeGf/+RpT08YNgycnCAhAX74gRr59OkTQghhBvpuR40a6aYDFflr3lz3nNOdqDj0XY8cTWhJd3FxAf6eLUlUTrNmzWLVqlUMHjyYESNGsGPHDv75z3+WSd3S9UhUS95378K+fbo3fftCzhogqTVqQOvW4Oama1G4fJlXTLi7IoQQohzlThREwVq0gF9+0bXAJCcX6xBpUai+Nm3axIoVKxgxYgQAL730Et26dSMrKwsLC4tS1V3sdP3rr78mLS2tVCcTokLExTHq8GHIztbNOe3nZ1zG3R2GDAHgpaQkk+7KCCGEKCeyhkLx1Kr1d9esYvz9unfvntoVxcHBodin0ScK0qJQuV2/fp3u3bur7x977DEsLS25detWqesudqIwZswYEhIS1PdeXl5cuXKl1AEIUebGjsUpNRVcXOCppwqe4ah1a3jsMd3rnTt1XZSEEEKYj8x4VHz6dauKkSjoWxM8PT1NmtJe3/UoLi5OVmiuxLKysrC2tjbYpp+xqrSK3fUo79ysiYmJZGdnlzoAIcrUH3/Ajz+SpdFg8eyzugFxhenVi+Rjx3CIi4OLF6FZs4qJUwghhDFJFIqvRQvYuxcuXcKqZctCi+rHJzQz8W+cfs2F1NRUzp8/j19+LfTC7BRFYdSoUdjkuuZ58OABb7zxhkEL0qZNm0yuW0YKiepDUWDaNACOeHvruhcVxdaWrfpfokOHyjE4IYQQRZJEofjc3XVdkDIyaBYbW2hRfYuCqYkC/D2mQcYpVF4jR47Ezc1NXeNMq9Xy0ksv4eXlZbCtJIrdoqDRaAxWdcv7XgizCwnRTXtqY8NvrVrRtZiHbXJw4PnkZF3f2KgomZJPCCHMISFBN301yGDm4tBodK0Kx47hW0Rf9NyJwrFjx0w6jaOjI3FxcTJOoRJbuXJludVtUtej5s2bq8lBUlIS7du3p0ae6cukD5uoSNPHjyclJgaNojB51y7qAbsbNOCP8+f/Hn9QhFhLS/D1hdOnda0Kw4aVb9BCCCGM6Qcy16kji2EWV06i8Eh0tG4CjwKmlNV3PWrevHmJEgWQAc0Pq2InCuWZrQhRUikxMSzy9YXwcN3dKGtrnnz6ad5ft860ivz8dIlCeDj07g0lbKITQghRQtLtyHTe3mBtjfbBA93aQfncIFMUpVRdj/SJgnQ9ejgVe4zCyJEji/UQosIpim4QM+gu+O3tTa/D0xMaNtTVdeJEmYYnhBCiGCRRMJ2lJTRtqnu9ZUu+RW7fvq3OWtmkSROTT6FPFC5evEhGRkbJ4qzE9u/fz5AhQ/Dy8kKj0fDjjz8a7FcUheDgYLy8vLCzs6Nnz56cOXPGoExaWhoTJkzA1dUVBwcHhg4dyo0bNwzKxMfHExgYqI4XCAwM5N69ewZlrl27xpAhQ3BwcMDV1ZWJEyeSnp5eHh+72EwezKwoCsePH+f777/nhx9+4MSJE0YzIglRoa5fh+ho3X+YxexulK927XTPZ8+WSVhCCCFMoO96JOMTTKOfJrWAREHf7ahBgwbY2dmZXL2dnR329vZkZmYSqf83qkaSk5Np27YtS5YsyXf/J598woIFC1iyZAnHjh3Dw8ODvn37kpiYqJYJCgpi8+bNbNiwgQMHDpCUlIS/vz9ZWVlqmYCAAMLCwggJCSEkJISwsDACAwPV/VlZWQwePJjk5GQOHDjAhg0b+OGHH5g8eXL5ffhiMGll5j179jB69GiuXr2qJgcajYZGjRrx9ddf88QTT5RLkEIU6uhR3XPr1iVrTdBr0ULXvzMuTvcQQghRcaRFoWSaNdNNCX76tC7ZypNolabbEeiu81q0aMHJkyc5d+4czZs3L3XIlcnAgQMZOHBgvvsURWHRokXMmDGDYTnjF1evXo27uzvr1q3j9ddfJyEhgRUrVvDtt9/Sp08fANasWUP9+vXZuXMn/fv35+zZs4SEhHD48GE6d+4MwPLly/Hz8+P8+fO0aNGC7du3ExERwfXr1/Hy8gJg/vz5jBo1ig8//JBatWpVwLdhrNgtChcvXsTf35+GDRuyadMmzp49S0REBP/73/+oV68egwYN4rL+l1yICqJNSYGICN2b0rQmANja/v0HSloVhBCiYkmiUDJ2dlzOWUE5v1aF0iYKAC1yWi0etnEKkZGRREdH069fP3WbjY0NPXr04ODBgwCEhoaSkZFhUMbLywtfX1+1zKFDh9BqtWqSANClSxe0Wq1BGV9fXzVJAOjfvz9paWmEhoaW6+csTLEThUWLFtGlSxd2797NU089RYsWLWjZsiXDhg1jz549dO7cmYULF5ZnrEIY6RYZqRtX4O0NHh6lr9DHR/csiYIQQlScrCy4ckX3WhIFk4Xrp/XOJ1G4dOkSAE31YxlKQH+svq6qIDExkfv376uPtLQ0k+uIjo4GwD3Pukzu7u7qvujoaKytrXFyciq0jJubm1H9bm5uBmXynsfJyQlra2u1jDkUO1HYu3cvQUFB+e7TaDQEBQWxZ8+esopLiKI9eEBX/R2oXFl6qbRooZubOjoarzJY+lwIIUQx3LoF6em6sWb16pk7mipHTRT27YP4eIN9V69eBaBhw4Ylrl8/CLoqJQo+Pj4Gi43NnTu3xHXlXTdMUZQi1xLLWya/8iUpU9GKnShcu3aN1q1bF7jf19dX/WEUokJ89x0109N1K1PqB3OVlr292r/zidTUsqlTCCFE4fQ3fRo2BAsLs4ZSFd2pWVPXIp6VpVt8NJcrOS01ZZEoVKUu5hERESQkJKiPadOmmVyHR05Phbx39GNjY9W7/x4eHqSnpxOfJ0HLWyYmJsao/ri4OIMyec8THx9PRkaGUUtDRSp2opCUlIR9IQNF7e3tSUlJKZOghCiWFSt0zx06FLjITIm0agVIoiCEEBVGxieU3tChuudc3Y8ePHigXnx6e3uXqNqTJ0/y0UcfAbo++8OGDWP8+PGli7UCODo6UqtWLfVhY2Njch2NGjXCw8ODHTt2qNvS09PZt28fXbt2BaBDhw5YWVkZlImKiiI8PFwt4+fnR0JCAkf1k68AR44cISEhwaBMeHg4UVFRapnt27djY2NDhw4dTI69rJg061FERESB/aRu375dJgEJUSwXLsD+/WQDNfTTmpaVli1h2zZaZWToFnGTxdeEEKJ8SaJQek89BR99BL/+quvGZW3NtWvXAHBwcMDFxaVE1aalpdGxY0e2b99OZmYm9erV49atW2UZuVklJSVx8eJF9X1kZCRhYWE4OzvToEEDgoKCmDNnDs2aNaNZs2bMmTMHe3t7AgICANBqtYwePZrJkyfj4uKCs7MzU6ZMoXXr1uosSK1atWLAgAGMGTOGL774AoCxY8fi7++vDhTv168fPj4+BAYGMm/ePO7evcuUKVMYM2aM2WY8AhMThd69e+e7ZoJGozF7HyrxkFm1CoBz7u74lPUvUM2a0KABXLumS0g6dizb+oUQQhjSz88viUKJnDh5krfnzWO2jQ2OCQks7dOHC25u3M4ZwNuwYcNSXaNpNBpq167N7du3jbrYVHXHjx+nV69e6vtJkyYBuoWGV61axdSpU0lNTWXcuHHEx8fTuXNntm/fri5EB7Bw4UIsLS157rnnSE1NpXfv3qxatQqLXN3o1q5dy8SJE9XZkYYOHWqwdoOFhQXbtm1j3LhxdOvWDTs7OwICAvj000/L+ysoVLETheq4yIaoorKyYPVqAI40bIhPeZyjSRNdohAZKYmCEEKUN32Lgiy2ViIWaWksbN1a9zfr5EnGp6WBry9Dc7ohlbTbUW7Ozs5qomBra1vq+iqLnj17FrpwsEajITg4mODg4ALL2NrasnjxYhYvXlxgGWdnZ9asWVNoLA0aNGDr1q1FxlyRip0olMUPmRBlYscOuHkTnJ3/numhrDVuDHv26P7Tzc4u2zEQQgghDEnXo7LRogWcPAnnz8OAAepN3nPnzjF8+HBAN+bA19fX5Kr103/evXvXYK5/Ub0V++onJSWF8ePHU7duXdzc3AgICJBxCcI8vv5a9/zSS2SV1+wYXl4kaTSQmgpmnL9YCCGqveRk0M8II4lC6TRurJtiNiEBYmPVtQMaN26Mr68vvr6+JVpPAP5OFO7du1dW0YoqoNiJwqxZs1i1ahWDBw9mxIgR7Nixg3/+85/lGZsQxu7cgZ9+0r1+5ZXyO0+NGoTpZ0ioQtPBCSFElaPv2uzkBLVrmzWUKs/K6u9k6/x5srKyAKhdBt9r7hYF8fAodtejTZs2sWLFCkaMGAHASy+9RLdu3cjKyjIYrCFEudq4UTebQ7t2ukc5CrWx4fEHD3R/xB5/vFzPJYQQDy3pdlS2WrSAv/6C8+fJzFk4tCwSBWdnZ0A3t39hffpF9VLsFoXr16/TvXt39f1jjz2GpaVltZoiS1Ru08eP5/LMmQD8mJVF0PDhhJ88WW7nC9W3KFy9ChkZ5XYeIYR4qMlA5rLVvLnu+dYtXLOzgbJJFPR1pKenk56eXur6RNVQ7EQhKysLa2trg22WlpZqtipEebO7dInGd+6ARsPTffuyyNeXrBL2tSyO65aW4Oiom2Xp+vVyO48QQjzULlzQPTdrZt44qouaNaFePQD6oLtWK2zB3OKytLRU5/NPSkoqdX2iaih21yNFURg1apTBynYPHjzgjTfewMHBQd22adOmso1QiByP6i/WGzXSXcCXN41G1xT+55+6O17SLC6EEGVPv9hV06bmjaM6ad4cbtygH/B17dplts6Vk5MT9+/fJzk5uUzqE5VfsVsURo4ciZubG1qtVn289NJLeHl5GWwzxdy5c+nUqROOjo64ubnx9NNPc/78eYMyiqIQHByMl5cXdnZ29OzZkzNnzhiUSUtLY8KECbi6uuLg4MDQoUO5ceOGQZn4+HgCAwPVOAMDA2XkflWiKHTMWWGS1q0r7rz65EDWERFCiPKhTxSkRaHs5Kz22w3wLMNFSfUDmiVReHgUu0Vh5cqVZX7yffv2MX78eDp16kRmZiYzZsygX79+REREqK0Un3zyCQsWLGDVqlU0b96cDz74gL59+3L+/Hl1VbygoCB+/vlnNmzYgIuLC5MnT8bf35/Q0FB1oHVAQAA3btwgJCQE0C2dHRgYyM8//1zmn0uUgxMncE9K0k371qpVxZ1X32f21i3dVKlCCCHKTno6XLmiey0tCmWnTh1SbW2xe/CAvmXUmgCSKDyMip0olAf9RbveypUrcXNzIzQ0lCeeeAJFUVi0aBEzZsxg2LBhAKxevRp3d3fWrVvH66+/TkJCAitWrODbb7+lT58+AKxZs4b69euzc+dO+vfvz9mzZwkJCeHw4cN07twZgOXLl+Pn58f58+dpkZN5i0pMv5phixaQq/tbuXN0BFdXuH1bt1KzEEKIsnPlim5RS3t78PAwdzTVh0bDzZo1afrgAX1TUjhcRtVKovDwqVTLzSYkJAB/T8EVGRlJdHQ0/fr1U8vY2NjQo0cPDh48CEBoaCgZGRkGZby8vPD19VXLHDp0CK1WqyYJAF26dEGr1aplRCWWmQkbNuhet2lT8eevX1/3LImCEEKUrdzjE8rwzreACzV0l3h+d++iyZn9qLT012cymPnhUWkSBUVRmDRpEo8//ri6tHh0zoq47u7uBmXd3d3VfdHR0VhbW6tZbkFl3NzcjM7p5uamlskrLS2N+/fvq4/ExMTSfUBRcrt3Q3Q0SdbW0KRJxZ/f21v3LDMfCSFE2ZIZj8rNX6mpJADatDTq3bxZJnXqr7UePHhAqnTHfShUmkThzTff5NSpU6xfv95oX97R+oqiFDmCP2+Z/MoXVs/cuXMNBmn7+PgU52OI8rB2LQAn69UDcyzu16CB7vnmTaxyVrkUQghRBmTGo3KRlZXFvcRE9uS8b5FnopiSsrOzU2e/jJRJPh4KlSJRmDBhAlu2bGHPnj3Uy5n7F8Ajp79i3rv+sbGxaiuDh4cH6enpxMfHF1omJibG6LxxcXFGrRV606ZNIyEhQX1ERESU/AOKkktJgZwpd0P1F+wVrXZt3bzU2dnUl6XrhRCi7EiiUC7u37+PAuzIeV9WiYJGo1FbFS5dulQmdYrKzayJgqIovPnmm2zatIndu3fTKM+qjI0aNcLDw4MdO3ao29LT09m3bx9du3YFoEOHDlhZWRmUiYqKIjw8XC3j5+dHQkICR48eVcscOXKEhIQEtUxeNjY21KpVS304VsS8/cLYli2QlASNGnElp29khdNo1O5Hje/cMU8MQghRHUnXo3KhH/P5e40aZNWoQZ3bt3Euo79f+kThsn5FbVGtmTVRGD9+PGvWrGHdunU4OjoSHR1NdHS02u9No9EQFBTEnDlz2Lx5M+Hh4YwaNQp7e3sCAgIA0Gq1jB49msmTJ7Nr1y5OnjzJSy+9ROvWrdVZkFq1asWAAQMYM2YMhw8f5vDhw4wZMwZ/f3+Z8aiy08929OKL5h3oljOgucnt2+aLQQghqpOMDJkatZzo14lKtrTkas6NrhZ//VUmdUuLwsPFrInCsmXLSEhIoGfPnnh6eqqPjRs3qmWmTp1KUFAQ48aNo2PHjty8eZPt27cb3OFfuHAhTz/9NM899xzdunXD3t6en3/+WV1DAWDt2rW0bt2afv360a9fP9q0acO3335boZ9XmCguDn77Tff6xRfNG0tOt6eGd+6AjFMQQojSu3pV9/+pnR14epo7mmpFnyhYWFpyPueGaPMyShT0Mx9Ji8LDwazrKCiKUmQZjUZDcHAwwcHBBZaxtbVl8eLFLF68uMAyzs7OrNHfnRZVw3ff6aZG7dABWrY0byzu7mBtjV16OoSHQ9u25o1HCCGqOn23oyZNoEalGDJZbegTBUsLC843b87AkBC8r17FrgxmKpIWhYeL/GaKyitntiNeesm8cYDuj5h+PYUDB8wbixBCVAf6gcwyPqHM6ROFB2lp/LJjB5dtbKihKChr1qDkmfzFVPpEITIykuwyWp9BVF6SKIjK6eJFOHRId4H+/PPmjkZHP+vS77+bNw4hhKgOZMajcqMfzGwPTHdzwyJnhsdXMzKwL+XFvVarRaPRkJaWxq1bt0obqqjkJFEQlZN+/Ei/fpWn76o+UThwAIrRbU4IIUQh9F2PJFEoU9nZ2WqioF8r6raLCwDOd+9iVcq/XzVq1MDe3h6Q7kcPA0kUROWjKH/PdhQYaN5YcqtblyyNBm7elFWahRCitKRFoVwkJiaSrShY1KihJgqJjo6kWVtjmZXFY2XQXcjBwQGQAc0PA0kUROVz8CBcvqxb5Ozpp80dzd+srLhZu7bu9aFDZg1FCCGqtMxM0K/sK2MUypR+fEKtWrX+3qjRcCenVeHJMkwUpEWh+pNEQVQ+33yjex4+HHKaNyuLSP2ibwcPmjcQIYSoyq5e1SULNjZQt665o6lW9IlCbf2NrRx3cv5+9c7KKnX32Zo1awKSKDwMJFEQlcuDB7ppUaFydTvKcSXnjoy0KAghRCnI1KjlpqBEId7JiawaNagLuMfElOoc0vXo4SG/naJy2boV7t3TTUXas6e5ozESqU8UTp6EMpiPWgghHkpnz+qeW7UybxzVkH4gc95EIdvCgvicqU1bnD9fqnNI16OHhyQKonLRz3b04ouV8i7TPTs78PLSNZkfP27ucIQQomo6d073bO7FNKuhgloU4O/Zj1qUcpVmfaJw584dNTER1ZNZV2YWwkBcHPzyCwBzQ0OJGT7cYHf4yZPg62uOyP6m0YCfH/zwg26cQvfu5o1HCCGqImlRKDeFJQp3XFzIBureuoVjYiKJjo4lOoeVlRV16tQhLi6Oy5cv0759+5IHLCo1SRRE5bFxI2Rmcq12baZ17Wq0u9fhw2YIKh9du+oSBRmnIIQQJSMtCuVCURT1Dr9WqzXan2FtzZ8aDe0VheZ//UVohw4lPleTJk2Ii4vj0qVLkihUY5Wvb4d4eOV0OzquX9issvLz0z0fPCgLrwkhhKnu3NG1IAO0aGHeWKqZxMREsrKzqaHRGE6PmssuCwug9OMUmjRpAsiA5upOEgVROZw/D0ePgoUFJ+rXN3c0hXv0UbC21v2hk/8ghRDCNPrWhPr1devliDKTew2FGgWM89uVs73x5ctYpaeX+FyNGzcGZEBzdSeJgqgc9IOYBwwgydbWvLEUxcYG9M21sp6CEEKYRrodlZuCZjzK7aJGw10nJyyzsmhSiptd+hYFSRSqN0kUhPllZ/+dKFTCtRPypR9DIeMUhBDCNDKQudwUNpBZpdFwvnlzoHTdj6Tr0cNBEgVhfr//DteuQa1aMHSouaMpntzjFIQQQhSftCiUG32ikN9A5tzO54wNaf7XX2iys0t0Ln3Xo2vXrpGRkVGiOkTlJ4mCML/Vq3XPzz4LdnbmjaW49InC6dOQmGjeWIQQoiqRFoVyU5wWhbT0dDYePUpijRo4pKRw75tvOPjddyjx8Sady9PTEzs7O7Kysrhy5UrJgxaVmiQKwrzu39dNiwowapRZQzGJlxd4e+u6TR07Zu5ohBCianjwACIjda+lRaHMFSdRsAfecXcn2dUVgElZWUx3c8PexJYFjUZD06ZNAbhw4UJJwhVVgCQKwrw2boSUFN0fjG7dzB2NaaT7kRBCmOavv3TTSteuDe7u5o6mWsm9hkKhYxRy3M5JFFxv3y7xVN8tcrownS/lVKui8pJEQZjXV1/pnl97TbfqcVUiA5qFEMI0uccnVLX/8yu55ORkMrOy0ECBayjkdtfZmawaNbB78ICaycklOmfznEHRf/31V4mOF5WfJArCfE6d0q2dYGVVdWY7yk3fonDokK4LkhBCiMLpEwUZn1Dmcq+hYJGzqFphsi0suOvkBOS0KpSAtChUf5IoCPNZsUL3PHQouLmZN5aSaNtWN/g6Pl7XnC6EEKJw+oHMMj6hzMXnDEYuTrcjPYPuRyUgLQrVn6W5AxAPqQcPSPniC+yBz6OjOTd8uLor/ORJ8PU1X2zFZWUFnTrB/v26cQryh08IIQonLQrl5u7duwA4OzsX+5g7Li4oQM3kZBpYW5t8Tn2icPPmTZKSkqgpK21XO9KiIMxj0ybs09KgVi3eePJJFvn6qo+stDRzR1d8Mk5BCCGKJzsb9F1U5MZKmStJopBpZUV8TgtEvxJ0oXV2dsY1p1VCZj6qniRREOaxbJnuuX17qFGFfwxl5iMhhCieyEhITQUbG2jUyNzRVDslSRTg7+5H/bKySnReGadQvVXhKzRRZZ06BQcOkKXRQIcO5o6mdPSJQkQE5AwkE0IIkY8//9Q9P/IIWErP57JW2kShvaJQ6/59k88r4xSqN0kURMX77DMATnl5gaOjmYMppTp1IGfBGQ4fNm8sQghRmekThbZtzRtHNZSdnU1ySgoATjkzGRVXuo0NCTnTqbaKiDD53NKiUL1JoiAqVkICrFkDwIEmTcwcTBmR7kdCCFE0SRTKTWZmJgD2dnbY2tqafHxsnToAPFKCRKGqtygEBwej0WgMHh4eHup+RVEIDg7Gy8sLOzs7evbsyZkzZwzqSEtLY8KECbi6uuLg4MDQoUO5ceOGQZn4+HgCAwPRarVotVoCAwPVKW0rM0kURMX65htITgYfHy7lNHdWefoBzX/8Yd44hBCiMgsL0z1LolDmsnISBVO7HendzkkUGly/jqOJ3Y9ytygoJVzh2dweeeQRoqKi1Mfp06fVfZ988gkLFixgyZIlHDt2DA8PD/r27UtiYqJaJigoiM2bN7NhwwYOHDhAUlIS/v7+ZOUa9xEQEEBYWBghISGEhIQQFhZGYBVYQ0oSBVFxFEXtdsS4cdVnVc7u3XXPhw9DRoZ5YxFCiMro3j24elX3WhKFMpdZykQhzcaG0Jy/ya30a10UU5MmTdBoNCQmJhITE1Oi85ubpaUlHh4e6qNOTuKkKAqLFi1ixowZDBs2DF9fX1avXk1KSgrr1q0DICEhgRUrVjB//nz69OlD+/btWbNmDadPn2bnzp0AnD17lpCQEL766iv8/Pzw8/Nj+fLlbN26tdJ32ZJEQVScXbt0c2jXrFk1V2IuSKtW4OICKSlw4oS5oxFCiMrn1Cndc/36YGIfelG0zJw71yVNFABCclZzNrX7kY2NDQ0bNgQq1ziFxMRE7t+/rz7SCpl6/cKFC3h5edGoUSNGjBjB5cuXAYiMjCQ6Opp+/fqpZW1sbOjRowcHc7obh4aGkpGRYVDGy8sLX19ftcyhQ4fQarV07txZLdOlSxe0Wq1aprKSREFUnAULdM8jR0LOwKlqoUYNePxx3ev9+80bixBCVEb68Qnt2pk1jOqqtC0K8Hei0ODaNWrm6lZTHPruR5VpnIKPj486HkCr1TJ37tx8y3Xu3JlvvvmG3377jeXLlxMdHU3Xrl25c+cO0dHRALi7uxsc4+7uru6Ljo7G2traaBB53jJubm5G53Zzc1PLVFaSKIiKEREBv/6q624UFGTuaMqevvvR77+bNw4hhKiMZCBzudInCqbOeJRbtEbD9Xr10GB69yP9gObK1KIQERFBQkKC+pg2bVq+5QYOHMgzzzxD69at6dOnD9u2bQNg9erVahlNnq7SiqIYbcsrb5n8yhenHnOTREFUjEWLdM9PP/33dKLViT5ROHBAt/qoEEKIv0miUG4yMjLIyvm7U5oWBYAIHx/A9O5HlbFFwdHRkVq1aqkPGxubYh3n4OBA69atuXDhgjr7Ud67/rGxsWorg4eHB+np6cTHxxdaJr/xG3FxcUatFZWNJAqi/MXG6mY7Apg0ybyxlJf27cHeHuLjda0nQgghdDIzITxc97qQRGH6+PEEDR9u9Ag/ebKCAq2a9FNs2lhbY29vX6q6zuQkCt5Xr1IrIaHYx1XGFoWSSktL4+zZs3h6etKoUSM8PDzYsWOHuj89PZ19+/bRNWfGww4dOmBlZWVQJioqivDwcLWMn58fCQkJHD16VC1z5MgREhIS1DKVlVkThf379zNkyBC8vLzQaDT8+OOPBvsf9rlrq41lyyAtDTp1gm7dzB1N+bCy+nuaVBmnIIQQf7twAR48AAcHKGT9nJSYGBb5+ho9sgoZhCoMV2QubTeW+1otV7y90QC+ea63CtOyZUsALl26VOig4cpoypQp7Nu3j8jISI4cOcLw4cO5f/8+I0eORKPREBQUxJw5c9i8eTPh4eGMGjUKe3t7AgICANBqtYwePZrJkyeza9cuTp48yUsvvaR2ZQJo1aoVAwYMYMyYMRw+fJjDhw8zZswY/P391daYysqsiUJycjJt27ZlyZIl+e5/2OeurRZSU2HpUt3ryZOrz5So+ZFxCkIIYUzf7ah1a93kD6JM5U4UysJpX18AWudaS6AodevWpXbt2mRlZXHWxPEN5nbjxg1eeOEFWrRowbBhw7C2tubw4cN4e3sDMHXqVIKCghg3bhwdO3bk5s2bbN++HUdHR7WOhQsX8vTTT/Pcc8/RrVs37O3t+fnnn7HIGSAOsHbtWlq3bk2/fv3o168fbdq04dtvv63wz2sqS3OefODAgQwcODDffXnnrgXdwBJ3d3fWrVvH66+/rs5d++2336pZ25o1a6hfvz47d+6kf//+6ty1hw8fVqelWr58OX5+fpw/f77SZ3JV3ooVEBcH3t7wzDPmjqZ85U4UFKV6J0VCCFFcMj6hXJV1ohDh48OgX3/FMzoa17i4Yh2j0Who06YN+/fv59SpU7SrQrNbbdiwodD9Go2G4OBggoODCyxja2vL4sWLWbx4cYFlnJ2dWbNmTUnDNJtKm9rL3LXVQHo6fPyx7vU774ClWfPS8te5s64L0s2bcOWKuaMRQojKQRKFcqUfRFuaGY9yS7W352LOpCOmtCq0adMGgD/1/96iWqi0V26FzV17NWd1x/KcuzYtLc2gn12iiXMKC3QDmG/cAE9PZoaFcX/4cIPd4SdPQk4TZ7Vgbw8dOuhWaN6/Hxo1MndEQghhXooCYWG615IolIuyalFIS0/n4HffAWCdlMR0oNnhwyjFvMnXNuff95R+cT1RLVTaREHPXHPXzp07l/fee8/EaIUqMxP0i5v861/c/+MPFuVJCnodPmyGwMpZz566RGH3bt3CckII8TC7dg2ionQtyu3bmzuaaicrK0udnKW0iYI9MD3nxmoNFxeyoqPxysjAr5jdaPUtCpIoVC+VtuuRueeunTZtmsFCHREy5aVpNmyAy5fB1RXGjjV3NBUnZ6wMO3fq7qQJIcTD7NAh3XO7dmBnZ9ZQqqP4+HiyFQUNGAyuLa1sCwtuu7oCMDTX5DCF8fX1RaPREBsbW+lXGxbFV2kTBXPPXWtjY2OwUEdZ/gJWe1lZ8OGHuteTJummxHtYdOsGtrZw6xZUsZkfhBCizOkTBT8/88ZRTcXGxgJgaWlZ5iv8xuTcTB2SlYVFzsrPhbG3t6dZs2aAtCpUJ2ZNFJKSkggLCyMsp/9iZGQkYWFhXLt2Teaurcq+/RbOnQNnZxg3ztzRVCxb279nP9q507yxCCGEuUmiUK70PSasra3LvO67Tk6kWVvjBLQo5orLMqC5+jFronD8+HHat29P+5x+i5MmTaJ9+/bMnDkTkLlrq6S0NJg1S/f63/8Grda88ZhD7u5HQgjxsEpNBf2qypIolIvcLQplTqMhOqdVoZ1+QHoRZEBz9WPWwcw9e/ZEKaQf98M+d22V9PnnusFrXl7w5pvmjsY8+vbVTQe7dy9kZOimTBVCiIfN8eO6iS08PHRr6Ygyp08UrMrp70y0hwfe16/T9OJFHO/fJ7FWrULLy4Dm6qfSjlEQVVBi4t9jE2bNengHrrVtCy4uuu8j19gYIYR4qOi7HXXtKgtQloOMjAx1atTyShRS7e05ptFQQ1FoW4yLf32LwtmzZ0lPTy+XmETFkkRBlJ1Fi3SrMDdrBq+8Yu5ozKdGDejdW/c610B7IYR4qMj4hHIVFxeHAjjY2xt0ty5rm3LqbhcWVuRsfg0aNKBWrVpkZGRw7ty5cotJVBxJFETZuHnz71WY339futv07at7lnEKQoiHkaJIolDO9AOZ81tUtiz9amFBupUVrnfuUP/69ULLajQaGdBczUiiIMrGv/8Nycm6JubnnjN3NOanH9B8+DDcv2/eWIQQoqJduQIxMbqbRh06mDuaakk/PqG8E4VkjYYzjzwCQMfQ0CLLy4Dm6qXSr8wsqoBDh2DNGtBoWOLlxcVnnzUqEn7yJORZmblaa9gQmjaFixd1qzQ//bS5IxJCiIpz8KDuuX173bTRoszpWxTc3d11rfrl6HjHjrQPC+ORM2f4rV+/QstKi0L1IomCKJ3sbJg4Uff6lVe4mJDAonwSgl6HD1dwYJXA4MHwn//ATz9JoiCEeLhIt6NyV1EtCgA369blppcXdW/d4tGTJzlau3aBZTvktCAdPXqU7OxsatSQzitVmSQKonRWrdJNgVerFsyZA+PHmzuiyuPpp3WJws8/66YILI95roUQojLat0/33LWreeOoppKTk0lKTgYqJlEAONqpE//46Sc6Hj/On87ODB8+3KiMu7s7//nPf3BwcCAhIYEzZ87QunXrColPlA9J80TJxcTAlCm61zNnQs7CLCLH44/rVqe+cwf++MPc0QghRMW4fh3Cww1ngBNlSt+a4FS7drmsypyfM76+pNjZUTshge4JCfj6+ho9YmJisLS0xC+nJen333+vkNhE+ZFEQZTcW29BfLyuD+pbb5k7msrH0hKGDNG9/vFHs4YihBAV5rffdM+PPaZbU0aUOYPxCRUk09KSE48+CsDInNaMgjz++OMAHDhwoNzjEuVLEgVRMj//DBs3goUFfPWVdKspiH5swo8/Fjn/tBBCVAu//qp7HjjQvHFUYxU5PiG34x07ogA90tJwvX27wHKSKFQfkigI092/D+PG6V5PmgQ5dxhEPvr1061QfeUKyFRxQojqLiPj7/VjBgwwbyzVWEWtoZDXvdq1Od+iBQBdC+lS26VLFywsLLh+/TrXrl2rqPBEOZBEQZju7bfhxg1o3BiCg80dTeVmb69LFkC6Hwkhqj/92jGurtCxo7mjqZYURVFbFCqy65HegW7dAGh76hS1ClgnyMHBgUdzbiLKOIWqTRIFYZrvv4evvwaNRvdsb2/uiCq/3N2PhBCiOtN3O+rXTzeYWZS59PR0MjIzsbO1xdXVtcLPf6N+fQ5ZW2ORnY2ffr2MfEj3o+pBOpaL4rt+HcaMAWBP+/b8tHgxLF5sUOShW1itOPz9dX8ww8J0C7A1bWruiIQQonyEhOieZXxCuUlPSwPA29sbjUZjlhgWOzrid+cOHU6c4PcnniAln5uG3bt3Z+HChZIoVHGSKIjiycqCl1+Ge/egUyd+rldPFlYrLldX3d21kBDduhMffGDuiIQQouxFR8PJk7rXRazeK0ouLT0d0CUKFXW+g//f3p3HRVXufwD/DDDDJiCLCi6g5oKCuYApaCpqaGVa1k3LCEq9eV3StEW7r0K6P7vYYpnXJRO1m3sp+bu5+7uiBiiGoCLgFosLuCAgyc48vz8OTA4z7AzDzHzer9d5OfPMc858z+Mzh/M9y3N27VIru1hYiFtubuiYlYUnTp9GVECAxnzDKi9RSkpKQm5uLhwdHVskXmpePC9I9bN0KRAVBdjaAlu3QslTyg3z5pvSv5s3S0kXEZGxqRoW1dcXaOGbbE2FUqlESeUZha5du7bId9oA+LB9e7XJRgj8Wnlp0ZC4OFhWxvSo9u3bo1evXgCAaD5LyGBxb4/qFhn551Hwb78FevbUbzyGaOJE6eFrN28CR47oOxoioub300/Sv7zsSGdu374NAcBSodDLjcyPSvH0xF0XF1gXF2NYDYnAk08+CYD3KRgyJgpUu5QU6ZIjAFiwAJg2Ta/hGCxLS+C116TXGzfqNxYiouZ2+/afNzK/+qp+YzFi6enpAAB3d3eY6fnMvjAzw/9VPnnbLzYWdlpGQKq6ofk8hwc3WEwUqGb370sj9vzxBzByJPDZZ/qOyLBVXX70889ALQ+qISIyOFu3SpdVDhkCeHrqOxqjlZGRAaDlLjuqS2rv3sjs0gXy8nIEREVpfD5p0iSkpKRg3759LR8cNQsmCqRdURHw3HPA5ctAly7Arl2AXK7vqAxb//7Sw+nKyoBt2/QdDRFR8xBCuv8KAEJC9BmJURNCqBKFlrqRuU4yGY489RQAYEBiIjpXO6vg6OgIT09PvY3ORE3HRIE0VVRIp45jYoC2baXTybwxrXlUnVXYsEH640pEZOgSE4ELF6RLLKdM0Xc0Ruvu3bsoKi6GDICbm5u+w1G53qULkvv0gZkQCLpwQd/hUDPj8KikTghgzhzp8hhLS2DvXny4Zg0KKx8XX4XPS2ikV18FPvhA+qN64ADwzDP6joiIqGmqziZMmgRwCEydqbo/QaFQwNzcXL/BVPN/Y8ag96VL8MnOBnbvBl58Ud8hUTPhGQX6kxDA3LnSyEYyGbBlCzBiBApv38bX3t5qU4WWodCoHhwdgdmzpdeffMKzCkRk2EpLpfsTAF52pGNViYKlpaV+A9Eix9kZv1Y+NwFz5kj3OJJRYKJAEqVS+nGvWSMlCRs3Ai+9pO+ojNOiRYC1NXD6NIdKJSLDtncvkJMDuLoCldeqU/MrKSnBlStXALTORAEATowYgRt2dtIIWIsW6TscaiZMFEi6uXbmTGDtWilJ2LyZR4Z0qUMH4K23pNc8q0BEhkqplLZhADBjBmDBq5l1JTk5GWXl5XB2coJCodB3OFpVWFhgrY/Pn/sRhw/rOyRqBkwUTF1BgTS60caNgJkZ8P33fz43gXTnvfeke0Cio6UnXhMRGZpdu4CkJMDBAVi4UN/RGLVz584BAAYMGNCqRxC65OwMzJsnvXnjDSA7W78BUZMxUTBlN24AI0YAhw4BNjbSKeSgIH1HZRo6dpSOwAHAhx9KI00RERmK8nIgNFR6/e67vIlZh3Jzc5GekQEZgP79++s7nLotWwb06QPcuiWNglVWpu+IqAl4ntBUHT0qjcBz9650Kcwvv+DDTZtQWDV6xSM4wpGOLFkincE5dQr417+A+fP1HRERUf1s2SI9Z8fZmdsuHas6m9C9e3fY29vrOZp6aNMG2LMHeOIJ4MQJaaS/FSv0HRU1Es8omJqKCuma0sBAKUkYMACIjQV8fbWObsQRjnSoUyfg88+l10uWANeu6TceIqL6KC0FwsKk14sXA3Z2+o3HiAkhkJiYCEC67MhgeHpKB8IA4KuvgO3b9RsPNRoTBVNy6RLw5JPS6WIhpBuYY2KAbt30HZnp+utfgYAA6UnYM2ZINwcSEbVmS5YA6enSSEdVwz2TTmRkZCAvPx+WCgU8PT31HU7DvPCC1FcAYPVqDtxhoJgomIKyMunIdf/+0tkDOzsp01+/Xhqmk/THzEx6SrONjXRT88qV+o6IiKhm+/b9eRnJunXStot0QgiB6OhoAICXlxfkcrmeI2qEf/xD2v84fFgaDYkMDu9RMBJz5szB7WpPT4YQGF1cjNnXrklnEwBg3Dhg/Xp8uHw5Cv/3f9Wq814EPeneHVi+XBopYtEi6UbnKVP0HRURkbobN4DgYOn1229LT2ImnSkqKsKVW7dgbmYGf39/fYfTOObm0s3uZLCYKBiJ27dvw/uRnXyPjAyMPH4c3dPSpIJ27aSd0ZAQQCZT3Y/wqIBTp1owYlIzZw6QkiI98C4oCGjbVkrqiIhag4cPgalTpYerDRoEfPaZviMyasXFxcjPzwcAPPnkk3BxcdFzROpKSksRs2uXRvk93tNodJgoGBGZUokeV69ieHQ0PDIzAQBlZmaQv/uuNASng4OeI6QayWTAqlXSY+937AAmTwZ+/BF45hl9R0ZEpu7uXWDCBCAuTrp0dedO6TkwpDNHjx5FhVIJF2dnDB8+XN/haLAB8GH79hrli69ebflgSKeYKBiD69cxOTUVzxw9iraVRyDKzc2ROGAAItq1w9rly/UcINVL1QPvcnOlZ1s8+6z0YLZlywBDvDaViAxfWpp0dvPKFcDJSbpHoUcPfUdl1FJTU/FbfDwAYMKECbAwoCde5+bm4qWXXtIo79ChA1avXq2HiKipDKf30Z+EkMav3r9fOuocG4tXKz8qsrJCwsCBiB06FAX29jj9ww9YoOVHy/sRWimFAvj5ZylB+Ne/pJvATpwAvvgCaIVHlYjISJWUSIMr/M//AAUFgLu7dADD0EbeMTBxcXE4cOAAAMDWxgZdu3bVb0ANpFQq1S6DrpKUlKSHaKg5mNSoR2vWrEG3bt1gZWUFHx8fnDx5Ut8h1U9FBR5ERyPt73/HtYAA5Ds6ShvrhQuB2FgoARwHECyTwaWkBKN++w1hGzZgzZo1yL9xA2PS0vCX/Hz81cwMi11csLxnTz4boTWzspIuQ9q9W7pc7PRpaVjbUaOk5LC0VN8REpGxysuTRmLz8pIelFVQAAwZIo2YxyRBZ0pKSnDw4EHsP3AAAsCggQPRtm1bfYdlMgx2/7AFmMwZhZ07d2LBggVYs2YNhg0bhm+//RZPP/00kpOT4e7uru/w1NxLTUXivHmwunYNLrdvw72wEPYAHn0eYwmAaAB7Kqcs4M8xisvKUFZWhoKCAigAxJ89q/EdWWZm2LRpE5ydneHs7AwXFxeUlpaiqKgIVlZWkHEYM/2bPBkYPFg6ordpE3D8uDTZ2wNPPw2MGSMNeevtzSEKiahxiouBxETp/oNjx9QPRri6AuHh0gALZiZ1XLFFKJVK3LlzB3l5efjyyy9RWlYGABgzejSGDx+OryIi9ByhaTCk/UN9MJlEYcWKFZg+fTpmzJgBAPj6669x6NAhrF27Fv/85z/1HJ06ZXExxh49qlZWAOCcQoH09u1xuLQU9/r0gY2LC2xtbTHR2hp7//1veHbqBEAae1mpVKKiogI52dkYOXQo7t+/j9zcXNy/fx+FRUWoUCqRkZmJjMqbngHgDoDln30GC3NztGnTRjXl5ubiwIEDkMvlqumPP/5AfHw8ZDKZagKAhw8f4vz582rlMpkMRYWFSElJUVsnbWUNLW/pZdy8eRN79uzRqKtT48bB2scHPffuReeYGFjl5Uk3E+7cCQAQZmYocnREsZMTipycUG5jg3JLS5RbWaHCygrllpaoUCggzMwAMzMIQPpXJoOQyf58XbkjILhDYLSa/XFHzfwAJVHP5cmq16tpPiHqjFFtWXV9f7UHIqodTqkjJm11q9ZX47BMLctqSF0AkFVUQF5UBIviYsiLi2FROVnn5sI2JwfWubka7ZnXpQvShw3DlaeeQplcLg2w0Exq+z/OzMzEeS0PnSwsLMT58+frVd7UukIIPHz4EOfOndNYhrbyuuoKIVBRUYGysjKUlpYiLy8Pe/bswf3795GdnY3yigr8AaAUQDsXF4wePRp9+vTRWJ6hMMTRkAxp/1AfTCJRKC0tRXx8PBYvXqxWHhgYiJiYGK3zlJSUoOSRjl01TFlWVpbuAq0knJxwsHt3XCwsRLq1Ne44OiLfyQlmlTe0njtxAj1u3MDDGzdwt3Ie6+JiLHB21ljWkuxsePv4qJUVFxdj/86dCBg8GHl5eaoEQnnvHkoAlFRU4GF+Pm5XrnMBgBNxcWrLuA/gp19+0fi+HADbIyM1yu8C+He1jYe2soaWt/QyogH89OKLGnVbkg+A8ZX/9gPQXqkEcnJgkZMDO71GRkSGpBzS9v0ugPjK6RcAydevS8lBMyYI9eEGYPuZMxrl96D974q28uaomwNgx88/16u8IXUB4AGAMxcuqN7LLSwgNzPDhAkT4O7uDplMhrsPHgAASsvLVa8fpa28prrlSiXyi4vVyiqE0ChraHlNdS2FwBx7e43yT9LTcePGDY1yXajaT8vPz4f9I7FYWlrCstpoXY3ZPzQ5wgTcvHlTABDR0dFq5cuWLRO9evXSOk9oaKiAdACOEydOnDhx4sSJkwFPoaGhzbJ/aGpM4oxClerX3QsharwWf8mSJVi4cKHqfXl5OVJSUtClSxeYNeDSjIKCAvTt2xfJycmws+Px3urYPnVjG9WO7VM7tk/d2Ea1Y/vUju1Tt5ZsI6VSiczMTPTt21dtaNnqZxMe1ZD9Q1NjEomCi4sLzM3NkZ2drVZ+584ddOjQQes82k5RDRs2rMHf/aDyVGCnTp3UToGRhO1TN7ZR7dg+tWP71I1tVDu2T+3YPnVr6Taq703Ijdk/NDUmcdeiQqGAj48Pjhw5olZ+5MgR+Pv76ykqIiIiItIX7h/WzSTOKADAwoULERQUBF9fX/j5+WH9+vXIzMzErFmz9B0aEREREekB9w9rZzKJwpQpU5CTk4NPPvkEWVlZ8Pb2xv79++Hh4aHT77W0tERoaGit18aZMrZP3dhGtWP71I7tUze2Ue3YPrVj+9StNbeRvvYPDYVMiGYeCJuIiIiIiAyeSdyjQEREREREDcNEgYiIiIiINDBRICIiIiIiDUwUiIiIiIhIAxOFJlq2bBn8/f1hY2ODtm3b1mseIQSWLl2Kjh07wtraGqNGjcLFixfV6pSUlGDevHlwcXGBra0tJk6ciBs3buhgDXQrNzcXQUFBcHBwgIODA4KCgpCXl1frPDKZTOv0+eefq+qMGjVK4/OpU6fqeG10ozFtFBISorH+Q4cOVatjqn2orKwMH3zwAfr16wdbW1t07NgRr7/+Om7duqVWz5D70Jo1a9CtWzdYWVnBx8cHJ0+erLX+8ePH4ePjAysrK3Tv3h3r1q3TqLN792707dsXlpaW6Nu3LyIjI3UVvs41pH327NmDp556Cu3atYO9vT38/Pxw6NAhtTqbN2/Wuk0qLi7W9aroREPaJyoqSuu6p6amqtUzpv4DNKyNtG2PZTIZvLy8VHWMqQ+dOHECzz33HDp27AiZTIaff/65znlMbRtkVAQ1yccffyxWrFghFi5cKBwcHOo1T3h4uLCzsxO7d+8WFy5cEFOmTBFubm7iwYMHqjqzZs0SnTp1EkeOHBFnz54VAQEBon///qK8vFxHa6Ib48ePF97e3iImJkbExMQIb29vMWHChFrnycrKUps2btwoZDKZuHbtmqrOyJEjxcyZM9Xq5eXl6Xp1dKIxbRQcHCzGjx+vtv45OTlqdUy1D+Xl5YmxY8eKnTt3itTUVBEbGyuGDBkifHx81OoZah/asWOHkMvl4rvvvhPJycli/vz5wtbWVmRkZGit//vvvwsbGxsxf/58kZycLL777jshl8vFTz/9pKoTExMjzM3NxaeffipSUlLEp59+KiwsLMSpU6daarWaTUPbZ/78+WL58uUiLi5OXL58WSxZskTI5XJx9uxZVZ1NmzYJe3t7jW2TIWpo+xw7dkwAEJcuXVJb90e3I8bUf4RoeBvl5eWptc3169eFk5OTCA0NVdUxpj60f/9+8fe//13s3r1bABCRkZG11je1bZCxYaLQTDZt2lSvREGpVApXV1cRHh6uKisuLhYODg5i3bp1QghpoyOXy8WOHTtUdW7evCnMzMzEwYMHmz12XUlOThYA1H7osbGxAoBITU2t93ImTZokRo8erVY2cuRIMX/+/OYKVW8a20bBwcFi0qRJNX7OPqQuLi5OAFD7Q2+ofeiJJ54Qs2bNUivz9PQUixcv1lr//fffF56enmplb731lhg6dKjq/csvvyzGjx+vVmfcuHFi6tSpzRR1y2lo+2jTt29fERYWpnpf3+27IWho+1QlCrm5uTUu05j6jxBN70ORkZFCJpOJ9PR0VZkx9aFH1SdRMLVtkLHhpUctLC0tDdnZ2QgMDFSVWVpaYuTIkYiJiQEAxMfHo6ysTK1Ox44d4e3trapjCGJjY+Hg4IAhQ4aoyoYOHQoHB4d6r8ft27exb98+TJ8+XeOzrVu3wsXFBV5eXnj33XdRUFDQbLG3lKa0UVRUFNq3b49evXph5syZuHPnjuoz9iF1+fn5kMlkGpcHGlofKi0tRXx8vNr/KwAEBgbW2B6xsbEa9ceNG4fffvsNZWVltdYxpL4CNK59qlMqlSgoKICTk5Na+R9//AEPDw907twZEyZMQEJCQrPF3VKa0j4DBw6Em5sbxowZg2PHjql9Ziz9B2iePhQREYGxY8dqPLDLGPpQY5jSNsgYmcyTmVuL7OxsAECHDh3Uyjt06ICMjAxVHYVCAUdHR406VfMbguzsbLRv316jvH379vVej++//x52dnaYPHmyWvm0adPQrVs3uLq6IikpCUuWLMG5c+dw5MiRZom9pTS2jZ5++mn85S9/gYeHB9LS0vDRRx9h9OjRiI+Ph6WlJfvQI4qLi7F48WK8+uqrsLe3V5UbYh+6d+8eKioqtG4/amqP7OxsrfXLy8tx7949uLm51VjHkPoK0Lj2qe7LL7/Ew4cP8fLLL6vKPD09sXnzZvTr1w8PHjzAypUrMWzYMJw7dw49e/Zs1nXQpca0j5ubG9avXw8fHx+UlJTghx9+wJgxYxAVFYURI0YAqLmPGVr/AZreh7KysnDgwAFs27ZNrdxY+lBjmNI2yBgxUdBi6dKlCAsLq7XOmTNn4Ovr2+jvkMlkau+FEBpl1dWnTkuob/sAmusJNGw9Nm7ciGnTpsHKykqtfObMmarX3t7e6NmzJ3x9fXH27FkMGjSoXsvWJV230ZQpU1Svvb294evrCw8PD+zbt08jqWrIcltKS/WhsrIyTJ06FUqlEmvWrFH7rLX3odo0dPuhrX718sZsk1qrxq7L9u3bsXTpUuzdu1ctQR06dKjaYAHDhg3DoEGDsGrVKnzzzTfNF3gLaUj79O7dG71791a99/Pzw/Xr1/HFF1+oEoWGLtMQNHZ9Nm/ejLZt2+L5559XKze2PtRQprYNMiZMFLSYO3dunaOfdO3atVHLdnV1BSBl2G5ubqryO3fuqLJpV1dXlJaWIjc3V+2I8J07d+Dv79+o721O9W2f8+fP4/bt2xqf3b17V+PIgTYnT57EpUuXsHPnzjrrDho0CHK5HFeuXGkVO3kt1UZV3Nzc4OHhgStXrgBgHwKkJOHll19GWloa/vvf/6qdTdCmtfUhbVxcXGBubq5xlO3R7Ud1rq6uWutbWFjA2dm51joN6YOtQWPap8rOnTsxffp0/Pjjjxg7dmytdc3MzDB48GDV781QNKV9HjV06FBs2bJF9d5Y+g/QtDYSQmDjxo0ICgqCQqGota6h9qHGMKVtkDHiPQpauLi4wNPTs9ap+hHu+qq61OHRyxtKS0tx/Phx1Q6cj48P5HK5Wp2srCwkJSW1ip28+raPn58f8vPzERcXp5r39OnTyM/Pr9d6REREwMfHB/3796+z7sWLF1FWVqaWfOlTS7VRlZycHFy/fl21/qbeh6qShCtXruDo0aOqP0a1aW19SBuFQgEfHx+Ny6OOHDlSY3v4+flp1D98+DB8fX0hl8trrdMa+kpDNKZ9AOlMQkhICLZt24Znn322zu8RQiAxMbFV9xVtGts+1SUkJKitu7H0H6BpbXT8+HFcvXpV6z111RlqH2oMU9oGGaWWvnva2GRkZIiEhAQRFhYm2rRpIxISEkRCQoIoKChQ1endu7fYs2eP6n14eLhwcHAQe/bsERcuXBCvvPKK1uFRO3fuLI4ePSrOnj0rRo8ebbBDWz7++OMiNjZWxMbGin79+mkMbVm9fYQQIj8/X9jY2Ii1a9dqLPPq1asiLCxMnDlzRqSlpYl9+/YJT09PMXDgQINrHyEa3kYFBQVi0aJFIiYmRqSlpYljx44JPz8/0alTJ/YhIURZWZmYOHGi6Ny5s0hMTFQbirCkpEQIYdh9qGroxoiICJGcnCwWLFggbG1tVSOsLF68WAQFBanqVw1N+M4774jk5GQRERGhMTRhdHS0MDc3F+Hh4SIlJUWEh4cb7NCEDW2fbdu2CQsLC7F69eoah8pdunSpOHjwoLh27ZpISEgQb7zxhrCwsBCnT59u8fVrqoa2z1dffSUiIyPF5cuXRVJSkli8eLEAIHbv3q2qY0z9R4iGt1GV1157TQwZMkTrMo2pDxUUFKj2dQCIFStWiISEBNWocqa+DTI2TBSaKDg4WADQmI4dO6aqA0Bs2rRJ9V6pVIrQ0FDh6uoqLC0txYgRI8SFCxfUlltUVCTmzp0rnJychLW1tZgwYYLIzMxsobVqPjk5OWLatGnCzs5O2NnZiWnTpmkMs1e9fYQQ4ttvvxXW1tZax7XPzMwUI0aMEE5OTkKhUIjHHntMvP322xrPETAUDW2jwsJCERgYKNq1ayfkcrlwd3cXwcHBGv3DVPtQWlqa1t/ko79LQ+9Dq1evFh4eHkKhUIhBgwaJ48ePqz4LDg4WI0eOVKsfFRUlBg4cKBQKhejatavWBPzHH38UvXv3FnK5XHh6eqrtCBqahrTPyJEjtfaV4OBgVZ0FCxYId3d3oVAoRLt27URgYKCIiYlpwTVqXg1pn+XLl4vHHntMWFlZCUdHRzF8+HCxb98+jWUaU/8RouG/sby8PGFtbS3Wr1+vdXnG1Ieqhsyt6TfDbZBxkQlReUcJERERERFRJd6jQEREREREGpgoEBERERGRBiYKRERERESkgYkCERERERFpYKJAREREREQamCgQEREREZEGJgpERERERKSBiQIRkREpLS1Fjx49EB0drfPvCgkJwfPPP1/j+5deegkrVqzQeRxERKQbTBSIiFqZkJAQyGQyyGQyWFhYwN3dHX/729+Qm5tb57zr16+Hh4cHhg0b1gKRqlu5ciU2b96sev/xxx9j2bJlePDgQYvHQkRETcdEgYioFRo/fjyysrKQnp6ODRs24D//+Q9mz55d53yrVq3CjBkzWiBCTQ4ODmjbtq3q/eOPP46uXbti69ateomHiIiahokCEVErZGlpCVdXV3Tu3BmBgYGYMmUKDh8+XOs8Z8+exdWrV/Hss8+qytLT0yGTybBjxw74+/vDysoKXl5eiIqKUtWpqKjA9OnT0a1bN1hbW6N3795YuXKl2rIrKiqwcOFCtG3bFs7Oznj//fchhFCrU/3SIwCYOHEitm/f3rhGICIivWKiQETUyv3+++84ePAg5HJ5rfVOnDiBXr16wd7eXuOz9957D4sWLUJCQgL8/f0xceJE5OTkAACUSiU6d+6MXbt2ITk5GR9//DE+/PBD7Nq1SzX/l19+iY0bNyIiIgK//vor7t+/j8jIyDpjf+KJJxAXF4eSkpIGrjUREekbEwUiolbol19+QZs2bWBtbY3HHnsMycnJ+OCDD2qdJz09HR07dtT62dy5c/Hiiy+iT58+WLt2LRwcHBAREQEAkMvlCAsLw+DBg9GtWzdMmzYNISEhaonC119/jSVLlqiWsW7dOjg4ONS5Hp06dUJJSQmys7MbsPZERNQaWOg7ACIi0hQQEIC1a9eisLAQGzZswOXLlzFv3rxa5ykqKoKVlZXWz/z8/FSvLSws4Ovri5SUFFXZunXrsGHDBmRkZKCoqAilpaUYMGAAACA/Px9ZWVlal1H98qPqrK2tAQCFhYW11iMiotaHZxSIiFohW1tb9OjRA48//ji++eYblJSUICwsrNZ5XFxc6jUyUhWZTAYA2LVrF9555x28+eabOHz4MBITE/HGG2+gtLS0SesAAPfv3wcAtGvXrsnLIiKilsVEgYjIAISGhuKLL77ArVu3aqwzcOBApKamaj3Kf+rUKdXr8vJyxMfHw9PTEwBw8uRJ+Pv7Y/bs2Rg4cCB69OiBa9euqeo7ODjAzc1N6zLqkpSUhM6dO8PFxaVe60lERK0HEwUiIgMwatQoeHl54dNPP62xTkBAAB4+fIiLFy9qfLZ69WpERkYiNTUVc+bMQW5uLt58800AQI8ePfDbb7/h0KFDuHz5Mj766COcOXNGbf758+cjPDxctYzZs2cjLy+vzrhPnjyJwMDAhq0sERG1CkwUiIgMxMKFC/Hdd9/h+vXrWj93dnbG5MmTtT63IDw8HMuXL0f//v1x8uRJ7N27V3WUf9asWZg8eTKmTJmCIUOGICcnR+OZDYsWLcLrr7+OkJAQ+Pn5wc7ODi+88EKt8RYXFyMyMhIzZ85s5BoTEZE+yURdd6IREZHBuHDhAsaOHYurV6/Czs4O6enp6NatGxISElQ3J+vKK6+8AnNzc2zZsgWAdBZj7969dT7/gYiIWieeUSAiMiL9+vXDZ599hvT09Bb7zvLyciQnJyM2NhZeXl6qcrlcjlWrVrVYHERE1Lx4RoGIyIi1xBmFxMRE+Pv7IyAgAFu2bIGjo6NOvoeIiFoWEwUiIiIiItLAS4+IiIiIiEgDEwUiIiIiItLARIGIiIiIiDQwUSAiIiIiIg1MFIiIiIiISAMTBSIiIiIi0sBEgYiIiIiINDBRICIiIiIiDUwUiIiIiIhIw/8Dzq8VMEYnGQYAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "shortPR=PRmatrix.copy().loc[:,['feat1','feat2','R (padj)','R (fdr)']]\n", + "shortPR=shortPR.loc[shortPR.feat1!=shortPR.feat2]\n", + "\n", + "fig=plt.figure(figsize=(8,4))\n", + "p=sns.histplot(shortPR['R (padj)'][shortPR['R (padj)']!=0], color='black', label='Bonferroni (<0.01)', kde=True, bins=100);\n", + "p.set(ylabel='PDF (Bonferroni)')\n", + "ax2=p.twinx()\n", + "g=sns.histplot(shortPR['R (fdr)'][shortPR['R (fdr)']!=0], ax=ax2, color='red', label='FDR (<0.01)', kde=True, bins=100);\n", + "g.set(ylabel='PDF (FDR)')\n", + "\n", + "fig.legend()\n", + "plt.xlabel('R')\n", + "plt.title('Distribution of correlation coefficients')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also observe that the Bonferroni correction yields a more homogeneous number of associated features for each feature (i.e. first neighbors), compared to the FDR filtering. This has a consequence on the network structure." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "neighbor_number=pd.DataFrame()\n", + "for n_neighbors in np.arange(1,21):\n", + " padj_count=((Rmatrix_padj!=0).sum()==n_neighbors).sum()\n", + " fdr_count=((Rmatrix_fdr!=0).sum()==n_neighbors).sum()\n", + " \n", + " out=pd.Series([padj_count, fdr_count], index=['num_features(Padj)','num_features(FDR)'], name=n_neighbors)\n", + " #neighbor_number=pd.concat([neighbor_number, out],1)\n", + " neighbor_number=pd.concat([neighbor_number, out],axis=1)\n", + " \n", + "neighbor_number=neighbor_number.T.rename_axis('num_neighbors').reset_index()\n", + "\n", + "fig,ax=plt.subplots(figsize=(16, 8), nrows=2)\n", + "ax=ax.flatten()\n", + "sns.lineplot(data=neighbor_number, x='num_neighbors', y='num_features(Padj)', color='black', label='Padj', ax=ax[0]);\n", + "sns.lineplot(data=neighbor_number, x='num_neighbors', y='num_features(FDR)', color='red', label='FDR', ax=ax[0]);\n", + "ax[0].set(ylabel='Number of features')\n", + "fig.suptitle('Number of features with <21 neighbors')\n", + "ax[0].set_xticks(np.arange(1,21,1));\n", + "\n", + "### boxplot requires a long df\n", + "feat_associations_padj=pd.concat([\n", + " shortPR.copy().loc[shortPR['R (padj)']!=0,][['feat1','feat2']],\n", + " shortPR.copy().loc[shortPR['R (padj)']!=0,][['feat2','feat1']].rename(columns={'feat1':'feat2','feat2':'feat1'})]).drop_duplicates().groupby('feat1').agg('count')\n", + "\n", + "feat_associations_fdr=pd.concat([\n", + " shortPR.copy().loc[shortPR['R (fdr)']!=0,][['feat1','feat2']],\n", + " shortPR.copy().loc[shortPR['R (fdr)']!=0,][['feat2','feat1']].rename(columns={'feat1':'feat2','feat2':'feat1'})]).drop_duplicates().groupby('feat1').agg('count')\n", + "\n", + "feat_associations=pd.concat([feat_associations_padj, feat_associations_fdr], axis=1, sort=True)\n", + "feat_associations.fillna(0,inplace=True)\n", + "feat_associations.columns=['Padj','FDR']\n", + "\n", + "sns.boxplot( data=feat_associations, notch=True, ax=ax[1], orient='h', palette={'Padj':'black','FDR':'red'});\n", + "plt.title('Number of associations per feature')\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the Bonferroni correction leads to many nodes being associated with 1 or 2 other nodes, whereas the FDR correction leads to substantially higher number of associations for some of the nodes. This can also raise questions about the biological plausibility of such high number of associations - is it biologically significant that a gene is co-expressed with 800 other genes/metabolites?\n", + "\n", + "We can also be a bit more strict on the FDR that we consider as statistically significant. Let's compare the number of potential false positives at different FDR. The following plot further highlights an FDR = 0.01 (dashed gray line)." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fdr_df=pd.DataFrame()\n", + "for fdr in np.append(np.geomspace(1e-4, 0.01, 30), [0.02, 0.03, 0.04, 0.05,0.1]):\n", + " out=pd.Series([fdr,(PRmatrix.Padj" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig,ax=plt.subplots(figsize=(10,4))\n", + "\n", + "for fdr in [0.05, 0.01, 0.001, 0.0001]:\n", + " ## Plotting R distribution\n", + " temp=PRmatrix.copy()\n", + " temp.loc[:,'R (fdr)']=temp['R'].copy()\n", + " temp.loc[temp['FDR']>fdr,'R (fdr)']=0\n", + " \n", + " temp=temp.loc[temp.feat1!=shortPR.feat2]\n", + " \n", + " color={0.05:'gray',0.01:'green',0.001:'red',0.0001:'blue'}[fdr]\n", + "\n", + " \n", + " p=sns.histplot(\n", + " temp['R (fdr)'][temp['R (fdr)']!=0], color=color, label='FDR <'+str(fdr), kde=True, ax=ax, bins=50);\n", + " p.set(ylabel='PDF (FDR)')\n", + "\n", + "fig.legend()\n", + "plt.xlabel('R (FDR)');\n", + "plt.title('Distribution of correlation coefficients');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Predicably, by being more conservative we will be selecting higher absolute correlation coefficients. In this test case we are considering only 25 samples. Larger sample sizes lead to lower nominal and adjusted p-values, and a higher number of statistically significant but milder correlation coefficients. In such cases, one may be more conservative in the significance threshold. Henceforth, we will consider as statistically significant those edges where FDR < 0.01.\n", + "\n", + "One last point comes from the comparison of statistically significant associations within and between omics. Note how so few inter-omic associations are identified, and that Bonferroni correction completely misses any." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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2139C2_accarnitinesThr_aminoacids_NORM0.634783
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" + ], + "text/plain": [ + " feat1 feat2 R (fdr)\n", + "8 C0_accarnitines C10_accarnitines 0.659505\n", + "68 C0_accarnitines PCaaC42_0_glycerophospholipids 0.722315\n", + "2130 C2_accarnitines Arg_aminoacids 0.711304\n", + "2133 C2_accarnitines His_aminoacids 0.633043\n", + "2139 C2_accarnitines Thr_aminoacids_NORM 0.634783" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "PRmatrix.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Imports and normalizes met and gene data so that we can compute similarities between them\n", + "from sklearn.preprocessing import StandardScaler\n", + "data=pd.read_csv('lab/data/met_genes.tsv', sep=\"\\t\", index_col=0)\n", + "\n", + "scaled_data=pd.DataFrame(StandardScaler().fit_transform(data.loc[:,data.columns!='Type'].T).T, columns=data.columns[data.columns!='Type'], index=data.index)\n", + "scaled_data_values=scaled_data.copy()\n", + "scaled_data['Type']=data.Type\n", + "\n", + "#Plots the data distribution\n", + "fig,ax=plt.subplots(figsize=(12,8))\n", + "sns.kdeplot(\n", + " scaled_data.loc[scaled_data.Type=='met',scaled_data.columns!='Type'].values.flatten(), \n", + " color='r', label='Mets', ax=ax, legend=False)\n", + "ax2=ax.twinx()\n", + "sns.kdeplot(\n", + " scaled_data.loc[scaled_data.Type=='genes',scaled_data.columns!='Type'].values.flatten(),\n", + " color='b', label='Genes', ax=ax2, legend=False)\n", + "fig.suptitle('Distribution for met and genes');\n", + "fig.legend();" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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5 rows × 24 columns

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" + ], + "text/plain": [ + " p10 p11 p12 p14 p15 \\\n", + "feature \n", + "C0_accarnitines -0.283259 -0.283259 -0.283259 -0.283259 -0.283259 \n", + "C2_accarnitines -1.091069 2.662663 -0.562089 -0.539787 -0.155881 \n", + "C3_accarnitines -0.587155 -0.587155 -0.587155 -0.587155 0.849915 \n", + "C5DC_C6OH_accarnitines -0.194339 0.226924 -0.767866 0.272220 -0.716144 \n", + "C5MDC_accarnitines -0.577804 0.551881 -0.558374 0.344380 -0.556174 \n", + "\n", + " p16 p18 p22 p23 p24 ... \\\n", + "feature ... \n", + "C0_accarnitines -0.283259 -0.283259 -0.283259 4.302810 -0.283259 ... \n", + "C2_accarnitines -0.492225 0.665140 0.166774 2.820305 0.229534 ... \n", + "C3_accarnitines 1.218743 2.132726 1.069064 2.937833 -0.587155 ... \n", + "C5DC_C6OH_accarnitines -0.424297 -0.270612 -0.231623 0.110276 -0.139779 ... \n", + "C5MDC_accarnitines -0.397915 -0.199422 -0.504603 -0.086143 -0.057000 ... \n", + "\n", + " p37 p38 p4 p40 p41 \\\n", + "feature \n", + "C0_accarnitines -0.283259 -0.283259 -0.283259 -0.283259 -0.283259 \n", + "C2_accarnitines -1.979012 -0.465146 0.301895 0.460770 -0.136651 \n", + "C3_accarnitines -0.587155 0.750857 -0.587155 -0.587155 -0.587155 \n", + "C5DC_C6OH_accarnitines 4.317705 -0.796138 -0.240113 0.014363 -0.336595 \n", + "C5MDC_accarnitines 4.420350 -0.509324 -0.402203 -0.224593 -0.234445 \n", + "\n", + " p46 p48 p5 p8 p9 \n", + "feature \n", + "C0_accarnitines -0.283259 -0.283259 -0.283259 -0.283259 1.928896 \n", + "C2_accarnitines -0.468992 -0.810965 -0.077043 -0.686711 0.267967 \n", + "C3_accarnitines -0.587155 -0.587155 -0.587155 -0.587155 1.022489 \n", + "C5DC_C6OH_accarnitines 0.446307 -0.609538 -0.021007 -0.371504 -0.694598 \n", + "C5MDC_accarnitines -0.093442 -0.510458 0.256485 -0.386765 -0.651137 \n", + "\n", + "[5 rows x 24 columns]" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "input_ds=scaled_data_values.loc[scaled_data_values.index.isin(pd.unique(fdr_pos_mat[['feat1','feat2']].values.flatten()))]\n", + "input_ds.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "### Generating the kNN graph\n", + "#Computes a kNN adjacency matrix from the input dataset\n", + "#and prepares table for being read by igraph\n", + "from sklearn.neighbors import kneighbors_graph\n", + "\n", + "knnG=kneighbors_graph(input_ds.values, 200, metric='euclidean')\n", + "knnG=pd.DataFrame(knnG.toarray(), columns=input_ds.index.copy(), index=input_ds.index.copy()) #adjacency matrix\n", + "knnG.index.name='gene1'\n", + "knnG.columns.name='gene2'\n", + "knnG=knnG.stack().reset_index().rename(columns={0:'Connectivity'})\n", + "knnG=knnG.loc[knnG['Connectivity']!=0]" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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gene1gene2Connectivity
1C0_accarnitinesC2_accarnitines1.0
2C0_accarnitinesC3_accarnitines1.0
7C0_accarnitinesC10_accarnitines1.0
10C0_accarnitinesC16_accarnitines1.0
11C0_accarnitinesC18_accarnitines1.0
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" + ], + "text/plain": [ + " gene1 gene2 Connectivity\n", + "1 C0_accarnitines C2_accarnitines 1.0\n", + "2 C0_accarnitines C3_accarnitines 1.0\n", + "7 C0_accarnitines C10_accarnitines 1.0\n", + "10 C0_accarnitines C16_accarnitines 1.0\n", + "11 C0_accarnitines C18_accarnitines 1.0" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knnG.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2102" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "input_ds.shape[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now generate the graphs from the dataframes above" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "PRmatrix.to_csv('lab/data/serialization/PRmatrix.tsv', sep=\"\\t\", index=False)\n", + "fdr_pos_mat.to_csv('lab/data/serialization/fdr_pos_mat.tsv', sep=\"\\t\", index=False)\n", + "knnG.to_csv('lab/data/serialization/knnG.tsv', sep=\"\\t\", index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Graph and community study plots (from part 2 and 3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Centrality studies plots" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import scipy as sp\n", + "\n", + "# Plotting\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "with open('data/serialization/degree_data.json', 'r') as file:\n", + " degree_data = json.load(file)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# RUN on: datasci env\n", + "import matplotlib.pyplot as plt\n", + "\n", + "fig, axes=plt.subplots(nrows=1, ncols=3, figsize=(15,5))\n", + "\n", + "for i, ax in zip(range(4), axes.flat):\n", + " x = [degree_data[\"degree_pos_w\"], degree_data[\"degree_all_u\"], degree_data[\"degree_knn\"]]\n", + " y = [degree_data[\"degree_random_posw\"], degree_data[\"degree_random_allu\"], degree_data[\"degree_random_knn\"]]\n", + " sns.regplot(x=x[i], y=y[i], \n", + " ax=ax, color=['blue','lightsalmon','green'][i],\n", + " line_kws={'color':'black'}\n", + " );\n", + " ax.set_title(['pos','all','knn'][i])\n", + " ax.set(xlabel='k ('+['pos','all','knn'][i]+')', ylabel='k (random)')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# RUN on: datasci env\n", + "\"\"\"\n", + "def transform_degree(graph):\n", + " alldegs=graph.degree()\n", + " alldegs=pd.DataFrame([[key,len(list(group))] for key,group in itertools.groupby(alldegs)], columns=['k','count'])\n", + " alldegs['P(k)']=[x/alldegs['count'].sum() for x in alldegs['count']]\n", + " alldegs=alldegs.loc[:,['k','P(k)']]\n", + " alldegs.drop_duplicates(inplace=True)\n", + " alldegs.reset_index(drop=True, inplace=True)\n", + " return(alldegs)\n", + "\"\"\"\n", + "\n", + "fig, ax = plt.subplots(figsize=(7, 7))\n", + "# ax.set(yscale='log', xscale='log')\n", + "p=sp.stats.probplot(degree_data[\"degree_pos_w\"], plot=ax)\n", + "a=sp.stats.probplot(degree_data[\"degree_all_u\"], plot=ax)\n", + "k=sp.stats.probplot(degree_data[\"degree_knn\"], plot=ax)\n", + "r=sp.stats.probplot(degree_data[\"degree_random_posw\"], plot=ax)\n", + "r2=sp.stats.probplot(degree_data[\"degree_random_allu\"], plot=ax)\n", + "r3=sp.stats.probplot(degree_data[\"degree_random_knn\"], plot=ax)\n", + "\n", + "col=['blue','','peru','','green','','lightblue','','lightsalmon','','greenyellow','']\n", + "for x in np.arange(0,11,2):\n", + " ax.get_lines()[x].set_markerfacecolor(col[x])\n", + " ax.get_lines()[x].set_markeredgewidth(0)\n", + " ax.get_lines()[x+1].set_color(col[x])\n", + "\n", + "\n", + "fig.legend(labels=['pos','pos','all','all','knn','knn','pos_random','pos_random','all_random','all_random','knn_random','knn_random']);\n", + "\n", + "ax.set(xlabel='Data quantiles', ylabel='observed degree (k)')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "network_centralities_raw = pd.read_csv(\"data/serialization/network_centralities_raw.csv\", sep = \"\\t\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "centralities_list=['degree (larger ~ central)','eccentricity (smaller ~ central)','betweenness (larger ~ central)', 'closeness (larger ~ central)','eigenvector (larger ~ central)']\n", + "fig, axes=plt.subplots(nrows=5, figsize=(16,20), sharey='row')\n", + "for i, ax in zip(range(6), axes.flat):\n", + " sns.boxplot(\n", + " data=network_centralities_raw, x='graph', y=centralities_list[i], notch=True, ax=ax, showfliers=False)\n", + " ax.set_title(centralities_list[i])\n", + " ax.set(xlabel='')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When interpreting the results above, it is important to bear in mind that these networks have different network sizes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Overall, we see:\n", + "- The median degree centrality decreases from `Full network > kNNG > Positive assoc. network`.\n", + "- The median betweenness centrality tends to decrease from `Positive assoc. network > Full network > kNNG`.\n", + "- The median closeness centrality tends to decrease from `Full network > kNNG > Positive assoc. network`.\n", + "- The eccentricity is very homogeneous for the `Full network (all_u)`, and slightly lower than in the `Full network`. In turn, most nodes in the `kNNG` tend to display an eccentricity of 3-4.\n", + "\n", + "\n", + "### Questions:\n", + "- Can you explain these observations?\n", + "- Based on the plots above, which graphs do you think follow a [*small world*](https://en.wikipedia.org/wiki/Small-world_network) behavior?\n", + "\n", + "We will also explore the relationships between different centrality metrics. Because these have different interpretations, we will compute ranks for each centrality, and perform the correlations on the ranks. In the following cell we do this, and then compute correlations within the 5 metrics for the full network. One additional column is presented (`median_centrality`), that is basically the median of the ranks of the 5 other centralities. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "all_u_centcorr = pd.read_csv(\"data/serialization/all_u_centcorr.csv\", sep = \"\\t\", index_col = 0)\n", + "knn_centcorr = pd.read_csv(\"data/serialization/knn_centcorr.csv\", sep = \"\\t\", index_col = 0)\n", + "pos_w_centcorr = pd.read_csv(\"data/serialization/pos_w_centcorr.csv\", sep = \"\\t\", index_col = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# RUN on: datasci env\n", + "fig,ax=plt.subplots(nrows=3, figsize=(8,16), sharex=True)\n", + "ax=ax.flatten()\n", + "for i in range(3):\n", + " tdata=[all_u_centcorr,knn_centcorr, pos_w_centcorr][i]\n", + " tname=['all_u','knn','pos_w'][i]\n", + " sns.heatmap(tdata, cmap=\"RdBu_r\", center=0, annot=True, ax=ax[i]);\n", + " ax[i].set(title=tname)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The plot above shows that most of the centrality metrics are positively correlated in the full network and in the positively coexpression network. However, this is not the case for the KNN network.\n", + "\n", + "#### Questions\n", + "\n", + "1. How do you explain the inverse relationship between degree and most other metrics in the KNN network?\n", + "2. Why do you think that the KNN network specifically displays this opposite trend?\n", + "\n", + "The next figure may help in answering the question above. We highlight the most central nodes based on degree (red) and eccentricity (green), in addition to a random subset of 500 nodes. Of these, which do you think displays the shortest path to all other nodes?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Community studies plots" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "feat_lists = pd.read_csv(\"data/serialization/feat_lists.csv\", sep = \"\\t\")\n", + "comm_counts = pd.read_csv(\"data/serialization/comm_counts.csv\", sep = \"\\t\", index_col = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "code_folding": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# RUN on: datasci env\n", + "\n", + "#Plotting community sizes\n", + "import seaborn as sns\n", + "fig, ax = plt.subplots(figsize=(7, 4))\n", + "bar_data=comm_counts.fillna(0).T\n", + "bar_data.plot(kind='bar', stacked=True, ax=ax);\n", + "# ## number of communities in each\n", + "# for index, row in groupedvalues.iterrows():\n", + "# g.text(row.name,row.tip, round(row.total_bill,2), color='black', ha=\"center\")\n", + "ax.legend(comm_counts.index, loc='right', bbox_to_anchor=(1.15, 1));\n", + "ax.set_title('Community size')\n", + "plt.xticks(rotation=0)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "scrolled": false + }, + "source": [ + "Some of the communities are very small in the `pos` and full networks, and comprise only 2 and 3 elements. Can we really call this a community?" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(7, 4))\n", + "sns.barplot(\n", + " data=bar_data.T.unstack().reset_index().rename(columns={'level_0':'network','level_1':'community',0:'size'}),\n", + " x='network',y='size', hue='community'\n", + " )\n", + "\n", + "ax.set(yscale='log');\n", + "ax.legend(loc='right', bbox_to_anchor=(1.15, 1));\n", + "ax.set_title('Community size')\n", + "plt.xticks(rotation=0)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Annotation studies plots" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "enriched_terms = pd.read_csv(\"data/serialization/enriched_terms.csv\", sep = \"\\t\", index_col = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(7, 4))\n", + "data_bars=pd.DataFrame(enriched_terms.groupby(['Graph','Comm'])['Term'].agg('count')).stack().reset_index().rename(columns={0:'Count'})\n", + "sns.barplot(x='Graph', y='Count', data=data_bars, hue='Comm')\n", + "ax.set_title('Number of significant Terms (Q < 0.05) per community')\n", + "ax.legend(loc='right', bbox_to_anchor=(1.15, 1));\n", + "plt.xticks(rotation=0)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "hide_input": false, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/session_topology/lab/topology_lab_part2.ipynb b/session_topology/lab/topology_lab_part2.ipynb new file mode 100644 index 00000000..8b062be4 --- /dev/null +++ b/session_topology/lab/topology_lab_part2.ipynb @@ -0,0 +1,32681 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0558db9d-6399-418d-9678-629afec5973f", + "metadata": {}, + "source": [ + "# Topology lab, part 2\n", + "\n", + "## Network - preliminary analysis" + ] + }, + { + "cell_type": "markdown", + "id": "2386c5f7-bcd0-4807-a7a2-48e99fec26af", + "metadata": {}, + "source": [ + "We will now build 4 different networks to analyse further:\n", + "- A full association network filtered using FDR-corrected P values (<0.01). This is an unweighted network.\n", + "- The subset of positively associated features, where correlation coefficient is used as weight.\n", + "- kNN-G that we will generate from the expression profile. This will be unweighted.\n", + "- A random network based on the [Erdos-Renyi model](https://en.wikipedia.org/wiki/Erd%C5%91s%E2%80%93R%C3%A9nyi_model), with the same node and edge number of each network. \n", + "\n", + "This will be a null-model for our analyses. The idea is that if a certain property found in one of our graphs is reproduced in a random graph, then we do not need to account for any other possible explanations for that feature. In other words, if a property of a graph (e.g. clustering) is not found in a random network, we can assume that it does not appear in our biological network due to randomness." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "251d82d7-9cb5-4494-a1d7-e1dbd836a480", + "metadata": {}, + "outputs": [], + "source": [ + "import igraph as ig\n", + "import pandas as pd\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e8e0e6b0-872d-430d-80b3-51b97ac4f56f", + "metadata": {}, + "outputs": [], + "source": [ + "n_nodes = 2102\n", + "PRmatrix = pd.read_csv('data/serialization/PRmatrix.tsv', sep=\"\\t\", index_col=False)\n", + "fdr_pos_mat = pd.read_csv('data/serialization/fdr_pos_mat.tsv', sep=\"\\t\", index_col=False)\n", + "knnG = pd.read_csv('data/serialization/knnG.tsv', sep=\"\\t\", index_col=False)\n", + "\n", + "### Generates each of the graphs\n", + "#positive associations, weighted\n", + "pos_w=ig.Graph.TupleList([tuple(x) for x in fdr_pos_mat.values], directed=False, edge_attrs=['w'])\n", + "\n", + "#full network, unweighted\n", + "edge_list=PRmatrix.copy().loc[PRmatrix.isin(pd.unique(fdr_pos_mat[['feat1','feat2']].values.flatten())).sum(1)==2,['feat1','feat2']].values\n", + "all_u=ig.Graph.TupleList([tuple(x) for x in edge_list], directed=False)\n", + "\n", + "#knnG, unweighted\n", + "knn=ig.Graph.TupleList([tuple(x) for x in knnG.values], directed=False)\n", + "\n", + "#random network, unweighted, node and edge number based on a network of the same size\n", + "random_posw=ig.Graph.Erdos_Renyi(n=n_nodes, m=len(fdr_pos_mat.values), directed=False, loops=False)\n", + "random_allu=ig.Graph.Erdos_Renyi(n=n_nodes, m=len(edge_list), directed=False, loops=False)\n", + "random_knn=ig.Graph.Erdos_Renyi(n=n_nodes, m=len(knnG.values), directed=False, loops=False)" + ] + }, + { + "cell_type": "markdown", + "id": "6c436eca-9989-424a-a55d-6b20aa9eb1ff", + "metadata": {}, + "source": [ + "For representation purposes we will see how a short knn graph looks - be careful in drawing the others, as they have many more edges it becomes computationally heavy." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6bcae92a-b321-425d-9705-6131e235963f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", 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} + ], + "source": [ + "import random\n", + "import numpy as np\n", + "\n", + "#random subset of knn graph for plotting\n", + "short_knn=ig.Graph.TupleList([tuple(x) for x in knnG.values[random.sample(list(np.arange(len(knnG.values))), 5000)]], directed=False)\n", + "\n", + "#This plots each graph, using degree to present node size:\n", + "short_knn.vs['degree']=short_knn.degree() \n", + "short_knn.vs['degree_size']=[(x*15)/(max(short_knn.vs['degree'])) for x in short_knn.vs['degree']] #degree is multiplied by 10 so that we can see all nodes\n", + "\n", + "layout = short_knn.layout_mds()\n", + "ig.plot(short_knn, layout=layout, vertex_color='white', edge_color='silver', vertex_size=short_knn.vs['degree_size'])" + ] + }, + { + "cell_type": "markdown", + "id": "821bc7dd-48a5-4987-8fbb-6d4c27ae8b66", + "metadata": {}, + "source": [ + "In the next table, we can see that while the same number of nodes is found in all networks, the number of edges varies greatly. We also see that the network is fully connected, which is not allways the case. If it wasn't connected, we could select the ***k*** largest connected components, and proceed the analyses with them. The largest connected component is called the *giant component*." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1111b01d-459e-47aa-8568-8c1fc9042a36", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IOStream.flush timed out\n" + ] + }, + { + "data": { + "text/html": [ + "
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node_countedge_countdiameterav_path_lengthdensityclustering_coefconnected?minimum_cut
pos_w210229354482.9330390.1329370.71453False0.0
all_u210240236072.119790.1822160.610098False0.0
knn210242040042.3200160.1903860.590294True203.0
pos_w_random210229354421.8670630.1329370.132909True218.0
all_u_random210240236021.8177840.1822160.18214True324.0
knn_random210242040021.8096140.1903860.190351True345.0
\n", + "
" + ], + "text/plain": [ + " node_count edge_count diameter av_path_length density \\\n", + "pos_w 2102 293544 8 2.933039 0.132937 \n", + "all_u 2102 402360 7 2.11979 0.182216 \n", + "knn 2102 420400 4 2.320016 0.190386 \n", + "pos_w_random 2102 293544 2 1.867063 0.132937 \n", + "all_u_random 2102 402360 2 1.817784 0.182216 \n", + "knn_random 2102 420400 2 1.809614 0.190386 \n", + "\n", + " clustering_coef connected? minimum_cut \n", + "pos_w 0.71453 False 0.0 \n", + "all_u 0.610098 False 0.0 \n", + "knn 0.590294 True 203.0 \n", + "pos_w_random 0.132909 True 218.0 \n", + "all_u_random 0.18214 True 324.0 \n", + "knn_random 0.190351 True 345.0 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#function to get graph properties, takes a few minutes to run\n", + "def graph_prop(input_graph):\n", + " ncount=nn.vcount()\n", + " ecount=nn.ecount()\n", + " diameter=nn.diameter()\n", + " av_path=nn.average_path_length()\n", + " dens=nn.density()\n", + " clustering=nn.transitivity_undirected() #this is the global clustering coefficient\n", + " conn=nn.is_connected()\n", + " min_cut=nn.mincut_value()\n", + " out=pd.DataFrame([ncount, ecount, diameter, av_path, dens, clustering, conn, min_cut],\n", + " index=['node_count','edge_count','diameter','av_path_length','density','clustering_coef','connected?','minimum_cut']).T\n", + " return(out)\n", + "\n", + "#summarizing graph properties\n", + "network_stats=pd.DataFrame()\n", + "for nn in [pos_w, all_u, knn, random_posw, random_allu, random_knn]:\n", + " network_stats=pd.concat([network_stats,graph_prop(nn)])\n", + " \n", + "network_stats.index=['pos_w','all_u','knn','pos_w_random', 'all_u_random', 'knn_random']\n", + "network_stats" + ] + }, + { + "cell_type": "markdown", + "id": "6d833cfc-f3ff-44cd-9281-03bc329d3e7b", + "metadata": {}, + "source": [ + "### Questions:\n", + "- Why is the diameter and average path length lower in the case of the full network and the random network, compared to the other two networks? What about the other random networks?\n", + "- Why do you think the clustering coefficient is lower for the knn compared with the other networks?\n", + "- Why is the minimum cut much larger in the random network compared to the others?\n", + "- How do you think the selected *k* would influence the properties above for kNNG?" + ] + }, + { + "cell_type": "markdown", + "id": "39b41ecf-0424-4cb0-ab62-e581c6e24669", + "metadata": {}, + "source": [ + "# Centrality analysis" + ] + }, + { + "cell_type": "markdown", + "id": "1a0f4420-045d-44dc-9e66-deb9a1145a4e", + "metadata": {}, + "source": [ + "We'll look into different centrality measures:\n", + "- [Degree](https://en.wikipedia.org/wiki/Degree_(graph_theory)) - number of neighbors of a node\n", + "- [Betweenness](https://en.wikipedia.org/wiki/Betweenness_centrality) - measures how many shortest paths in the network pass through a node.\n", + "- [Closeness](https://en.wikipedia.org/wiki/Centrality#Closeness_centrality) - the average length of the shortest paths between a node and all other nodes \n", + "- [Eccentricity](https://en.wikipedia.org/wiki/Distance_(graph_theory)) - largest shortest path from a node to any other node. Nodes with high eccentricity tend to be on the periphery.\n", + "- [Eigenvector centrality](https://en.wikipedia.org/wiki/Eigenvector_centrality) - a node is more central if its neighbors show a high degree.\n", + "\n", + "Degree, Betweenness, Closeness and Eigenvector centralities may be additionally computed for the positive association network by taking into account each edge's weight. For instance, for degree this is done for each node by summing each edge's degree.\n", + "\n", + "Because the number of shortest paths in a network scales with the network size, we normalize Eccentricity and Betweenness with respect to the network size so that they can be compared between the four networks above.\n", + "\n", + "Note that many [other centrality metrics](https://en.wikipedia.org/wiki/Centrality) can be computed. For instance, [PageRank](https://en.wikipedia.org/wiki/PageRank) and [HITS](https://en.wikipedia.org/wiki/HITS_algorithm) take into account edge directionality to compute what are the most central nodes in a network. " + ] + }, + { + "cell_type": "markdown", + "id": "9eec63fb-b072-494d-848e-f62f891885ab", + "metadata": {}, + "source": [ + "**Degree distribution** \n", + "Let's start by comparing the degrees of the random network against the three other networks. From the figures below it seems that there is no relationship between the degree of the random network, and any of the three others." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "952f79ea-71ec-4e6f-9f04-cc8529170c21", + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: to/from json\n", + "degree_data = {\n", + " 'nodes': list(range(n_nodes)),\n", + " 'degree_pos_w': pos_w.degree(),\n", + " 'degree_all_u': all_u.degree(),\n", + " 'degree_knn': knn.degree(),\n", + " 'degree_random_posw': random_posw.degree(),\n", + " 'degree_random_allu': random_allu.degree(),\n", + " 'degree_random_knn': random_knn.degree(),\n", + " }" + ] + }, + { + "cell_type": "markdown", + "id": "4670397d-79c6-4465-ac8f-31968e2c98e8", + "metadata": {}, + "source": [ + "**Centrality** \n", + "We will now compare different centrality metrics between the graphs." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "7160d24c-a3a8-4b40-9207-5c1f60ea575a", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "graphs = {\"pos_w\": pos_w, \"all_u\": all_u, \"knn\": knn, \"random_posw\": random_posw, \"random_allu\": random_allu, \"random_knn\": random_knn}\n", + "centralities_list=['degree (larger ~ central)','eccentricity (smaller ~ central)','betweenness (larger ~ central)', 'closeness (larger ~ central)','eigenvector (larger ~ central)']\n", + "\n", + "def compute_all_centralities(graph, graph_name):\n", + " \"\"\"Computes centrality metrics for a graph.\n", + " \"\"\"\n", + " deg = graph.degree(loops=False)\n", + " node_n = graph.vcount()\n", + " # scaled to account for network size\n", + " ecc = [(2*x / ((node_n-1)*(node_n-2))) for x in graph.eccentricity()]\n", + " btw = [(2*x / ((node_n-1)*(node_n-2))) for x in graph.betweenness(directed=False)] \n", + " eig = graph.eigenvector_centrality(directed=False, scale=False)\n", + " # For disconnected graphs, computes closeness from the largest connected component\n", + " if(graph.is_connected()):\n", + " cls = graph.closeness(normalized=True)\n", + " else:\n", + " cls = graph.clusters(mode='WEAK').giant().closeness(normalized=True)\n", + " \n", + " df = pd.DataFrame([deg, ecc, btw, cls, eig], index = centralities_list).T\n", + " df['graph'] = graph_name\n", + " df = df.loc[:, np.append(['graph'], \n", + " df.columns[df.columns!='graph'])] # reorder columns\n", + " return df\n", + " \n", + "\n", + "network_centralities_raw=pd.DataFrame()\n", + "for index, (graph_name, graph) in enumerate(graphs.items()):\n", + " df = compute_all_centralities(graph, graph_name)\n", + " ##Adds centralities for each node in the network\n", + " graph.vs['degree'] = df['degree (larger ~ central)']\n", + " graph.vs['eccentricity'] = df['eccentricity (smaller ~ central)']\n", + " graph.vs['betweenness'] = df['betweenness (larger ~ central)']\n", + " graph.vs['closeness'] = df['closeness (larger ~ central)']\n", + " graph.vs['eigenvector'] = df['eigenvector (larger ~ central)']\n", + " network_centralities_raw = pd.concat([network_centralities_raw, df])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "361aebfa-5ff2-4123-b495-8e6e659d224d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " graph degree (larger ~ central) eccentricity (smaller ~ central) \\\n", + "0 pos_w 2.0 4.532989e-07 \n", + "1 pos_w 1.0 9.065978e-07 \n", + "2 pos_w 1.0 9.065978e-07 \n", + "3 pos_w 4.0 2.719793e-06 \n", + "4 pos_w 19.0 2.266494e-06 \n", + "\n", + " betweenness (larger ~ central) closeness (larger ~ central) \\\n", + "0 4.532989e-07 0.259888 \n", + "1 0.000000e+00 0.282556 \n", + "2 0.000000e+00 0.325719 \n", + "3 0.000000e+00 0.296716 \n", + "4 2.620360e-04 0.294630 \n", + "\n", + " eigenvector (larger ~ central) \n", + "0 0.000000e+00 \n", + "1 0.000000e+00 \n", + "2 0.000000e+00 \n", + "3 6.649338e-09 \n", + "4 1.892555e-07 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "network_centralities_raw.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "33b2488b-9cb5-4f1f-99bb-61fe6c9bd9c4", + "metadata": {}, + "outputs": [], + "source": [ + "network_centralities_raw.to_csv(\"data/serialization/network_centralities_raw.csv\", sep = \"\\t\", index = False)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "68c7de8d-cdb3-40cb-8670-3d0cd86b569b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "12612" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n_nodes * 6" + ] + }, + { + "cell_type": "markdown", + "id": "9d481436-1ab9-4b18-b24c-d7e200f0d7e9", + "metadata": {}, + "source": [ + "Let's now compute centrality ranks so that we can compare them within and between networks" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "f9465fdc-e9ee-45c2-8a21-d7bbcba1b90c", + "metadata": {}, + "outputs": [], + "source": [ + "full_centralities=pd.DataFrame()\n", + "for net in [0,1,2]:\n", + " net_in=[pos_w, all_u, knn][net]\n", + " net_nm=['pos_w', 'all_u', 'knn'][net]\n", + " temp=pd.DataFrame([net_in.vs[att] for att in ['name','degree','betweenness', 'closeness','eccentricity','eigenvector']], index=['name','degree','betweenness', 'closeness','eccentricity','eigenvector']).T\n", + " temp.columns=[x+'|'+net_nm for x in temp.columns]\n", + " temp.rename(columns={'name|'+net_nm:'name'}, inplace=True)\n", + " \n", + " ## For all but eccentricity centrality, we compute the rank in ascending mode\n", + " ## so that higher ranking means more central. we need to reverse this for eccentricity\n", + " temp.loc[:,temp.columns.str.contains('deg|bet|clos|eig')]=temp.loc[:,temp.columns.str.contains('deg|bet|clos|eig')].rank(pct=True, ascending=True)\n", + " temp.loc[:,temp.columns.str.contains('eccentricity')]=temp.loc[:,temp.columns.str.contains('eccentricity')].rank(pct=True, ascending=False\n", + " )\n", + " temp['median_centrality|'+net_nm]=temp.loc[:,temp.columns!='name'].median(1)\n", + " if(net==0):\n", + " full_centralities=temp\n", + " else:\n", + " full_centralities=pd.merge(full_centralities, temp, on='name')\n", + "full_centralities.set_index('name', inplace=True)\n", + "#full_centralities=pd.merge(full_centralities, data[['Type']], left_index=True, right_index=True, how='left')\n", + "full_centralities['median|ALL']=full_centralities.loc[:,full_centralities.columns.str.contains('median')].median(1)\n", + "\n", + "\n", + "### Correlations are computed between ranks, after inverting the rank for eccentricity\n", + "def correlations_centralities(graph_name):\n", + " \"\"\"\n", + " Returns squared correlation matrix.\n", + " \"\"\"\n", + " temp_corr=full_centralities.copy().loc[:,full_centralities.columns!='Type'].dropna().astype('float')\n", + " temp_corr=temp_corr.loc[:,temp_corr.columns.str.contains(graph_name)]\n", + " temp_corr.columns=temp_corr.columns.str.replace('\\|.+','')\n", + " temp_corr=temp_corr.corr(method='spearman')\n", + " np.fill_diagonal(temp_corr.values, np.nan)\n", + " return(temp_corr)\n", + "\n", + "all_u_centcorr=correlations_centralities('all_u')\n", + "knn_centcorr=correlations_centralities('knn')\n", + "pos_w_centcorr=correlations_centralities('pos_w')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "4fff9619-0ac2-44d8-b25c-9543087ad402", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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degree|all_ubetweenness|all_ucloseness|all_ueccentricity|all_ueigenvector|all_umedian_centrality|all_u
degree|all_uNaN0.5664370.6605160.3614030.9490160.945971
betweenness|all_u0.566437NaN0.3325360.3559120.5078160.627798
closeness|all_u0.6605160.332536NaN0.2213820.7261210.763778
eccentricity|all_u0.3614030.3559120.221382NaN0.3608120.440496
eigenvector|all_u0.9490160.5078160.7261210.360812NaN0.950041
median_centrality|all_u0.9459710.6277980.7637780.4404960.950041NaN
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" + ], + "text/plain": [ + " degree|all_u betweenness|all_u closeness|all_u \\\n", + "degree|all_u NaN 0.566437 0.660516 \n", + "betweenness|all_u 0.566437 NaN 0.332536 \n", + "closeness|all_u 0.660516 0.332536 NaN \n", + "eccentricity|all_u 0.361403 0.355912 0.221382 \n", + "eigenvector|all_u 0.949016 0.507816 0.726121 \n", + "median_centrality|all_u 0.945971 0.627798 0.763778 \n", + "\n", + " eccentricity|all_u eigenvector|all_u \\\n", + "degree|all_u 0.361403 0.949016 \n", + "betweenness|all_u 0.355912 0.507816 \n", + "closeness|all_u 0.221382 0.726121 \n", + "eccentricity|all_u NaN 0.360812 \n", + "eigenvector|all_u 0.360812 NaN \n", + "median_centrality|all_u 0.440496 0.950041 \n", + "\n", + " median_centrality|all_u \n", + "degree|all_u 0.945971 \n", + "betweenness|all_u 0.627798 \n", + "closeness|all_u 0.763778 \n", + "eccentricity|all_u 0.440496 \n", + "eigenvector|all_u 0.950041 \n", + "median_centrality|all_u NaN " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_u_centcorr" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "98a6b2bb-a56a-4bd4-a148-08761e369294", + "metadata": {}, + "outputs": [], + "source": [ + "all_u_centcorr.to_csv(\"data/serialization/all_u_centcorr.csv\", sep = \"\\t\", index = True)\n", + "knn_centcorr.to_csv(\"data/serialization/knn_centcorr.csv\", sep = \"\\t\", index = True)\n", + "pos_w_centcorr.to_csv(\"data/serialization/pos_w_centcorr.csv\", sep = \"\\t\", index = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "1b81d072-7567-40f3-860d-511323c8aa25", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['degree|pos_w', 'betweenness|pos_w', 'closeness|pos_w',\n", + " 'eccentricity|pos_w', 'eigenvector|pos_w',\n", + " 'median_centrality|pos_w', 'degree|all_u', 'betweenness|all_u',\n", + " 'closeness|all_u', 'eccentricity|all_u', 'eigenvector|all_u',\n", + " 'median_centrality|all_u', 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}, + "output_type": "execute_result" + } + ], + "source": [ + "knn_centralities=full_centralities.loc[:,full_centralities.columns.str.contains('knn')]\n", + "knn_centralities=knn_centralities.loc[:,knn_centralities.columns!='median_centrality|knn']\n", + "knn_centralities.columns=knn_centralities.columns.str.replace('\\\\|.+','')\n", + "\n", + "##top nodes based on eccentricity\n", + "knn_top_ecc=knn_centralities['eccentricity|knn'].sort_values(ascending=False).index.values[:1]\n", + "\n", + "##top nodes based on degree\n", + "knn_top_deg=knn_centralities['degree|knn'].sort_values(ascending=False).index.values[:1]\n", + "\n", + "##random nodes to help visualize\n", + "##warning, do not increase this value much higher than 500, or you may have problems rendering this image\n", + "knn_others=knn_centralities.sample(500).index.values\n", + "\n", + "## full list\n", + "node_list=np.append(np.append(knn_top_deg, knn_top_ecc), knn_others)\n", + "\n", + "## subsets nodes\n", + "knn_to_draw=knn.subgraph(knn.vs.select([x.index for x in knn.vs if x['name'] in node_list]))\n", + "knn_to_draw.vs['color']=['red' if x['name'] in knn_top_deg else 'green' if x['name'] in knn_top_ecc else 'white' for x in knn_to_draw.vs]\n", + "\n", + "layout = knn_to_draw.layout_auto()\n", + "ig.plot(knn_to_draw, layout=layout, vertex_color=knn_to_draw.vs['color'], edge_color='silver', vertex_size=7)" + ] + }, + { + "cell_type": "markdown", + "id": "b998e8d7-e35c-4bf2-a030-0c8d57bf66ff", + "metadata": {}, + "source": [ + "__Plots and questions__\n", + "\n", + "You can revert now or later to part 1 for plots and questions!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "06d7ca51-e3c6-4605-93d5-efefa2151899", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "db6055e1-338a-4d7b-87a6-02c08c66f1a0", + "metadata": {}, + "source": [ + "# Community analysis" + ] + }, + { + "cell_type": "markdown", + "id": "e908a03b-f18b-4ba4-be00-83d9a568e140", + "metadata": {}, + "source": [ + "Node communities may be identified based on different metrics including [Modularity](https://en.wikipedia.org/wiki/Modularity_(networks)) or [Density](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.84.016114). We will look at community detection through modularity." + ] + }, + { + "cell_type": "markdown", + "id": "05a58a14-b8da-436a-bc85-2776f8544e3f", + "metadata": {}, + "source": [ + "## Modularity of a small graph\n", + "\n", + "Recall that the Modularity of a community is given by $$Q = \\frac{1}{2m} \\sum_{c}(e_c - \\frac{K_c^2}{2m})$$\n", + "\n", + "where $e_c$ is the number of edges in community $c$, and $\\frac{K_c^2}{2m}$ is the expected number of edges in the community given the $K_c$ sum of degrees of its nodes, for a network with $m$ edges. This will correspond to\n", + "\n", + "$$Q = \\frac{1}{2m} \\sum_{ij}[A_{ij} - \\frac{k_i k_j}{2m}\\delta(c_i,c_j)]$$\n", + "\n", + "where $A_ij$ is the Adjacency between nodes $i$ and $j$, $k_i$ and $k_j$ are their degree, and $\\delta(c_i,c_j)$ is the [Kronecker delta](https://en.wikipedia.org/wiki/Kronecker_delta), defined as 1 if nodes $i$ and $j$ are in the same community, or 0 if they are not. Let's examine the following small network, with communities given by the two colors: red `[A,B,C,D,H]` and blue `[E,F,G]`." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "ce354c63-8629-475b-b5da-5aeb6dd43778", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": { + "image/svg+xml": { + "isolated": true + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "g = ig.Graph([(0,1), (0,2), (1,2), (0,3), (2,3), (1,3), (2,4), (4,5), (5,6), (4,6), (4,7), (5,7), (6,7)])\n", + "\n", + "g.vs[\"name\"] = [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\",\"H\"]\n", + "g.vs[\"module\"] = [\"f\", \"f\", \"f\", \"f\", \"m\", \"m\",\"m\", \"f\"]\n", + "\n", + "\n", + "layout = g.layout_circle()\n", + "g.vs[\"label\"] = g.vs[\"name\"]\n", + "color_dict = {\"m\": \"cyan\", \"f\": \"pink\"}\n", + "g.vs[\"color\"] = [color_dict[module] for module in g.vs[\"module\"]]\n", + "ig.plot(g, layout = layout, bbox = (300, 300), margin = 50)" + ] + }, + { + "cell_type": "markdown", + "id": "93b32822-ce60-4512-b232-568a4170e723", + "metadata": {}, + "source": [ + "For this network, we have the following adjacency matrix." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "4b14759c-7323-4112-8c19-fbc0aad8c78e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " A B C D E F G H\n", + "A 0 1 1 1 0 0 0 0\n", + "B 1 0 1 1 0 0 0 0\n", + "C 1 1 0 1 1 0 0 0\n", + "D 1 1 1 0 0 0 0 0\n", + "E 0 0 1 0 0 1 1 1\n", + "F 0 0 0 0 1 0 1 1\n", + "G 0 0 0 0 1 1 0 1\n", + "H 0 0 0 0 1 1 1 0" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame( g.get_adjacency(), index=g.vs[\"name\"], columns=g.vs[\"name\"] )" + ] + }, + { + "cell_type": "markdown", + "id": "fcb3932c-bf55-458a-bfd1-0bd0b5e0442c", + "metadata": {}, + "source": [ + "We will compute the modularity of this network given the 2 communities above, herein identified as communities `1` and `2`." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "b78e7ea0-6d6d-4846-8425-94536e9bcead", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.16568047337278102" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def modularity(graph, membership):\n", + " \"\"\"\n", + " Computes the modularity of `graph` for a given module `membership`.\n", + " \"\"\"\n", + " m=len(graph.es.indices) #edge number\n", + " A=pd.DataFrame(graph.get_adjacency()) #adjacency matrix\n", + " Q=[] \n", + " for i in A.index:\n", + " for j in A.columns:\n", + " if(membership[i]==membership[j]):\n", + " deltaij=1\n", + " else:\n", + " deltaij=0\n", + " Q=np.append(Q, (A.loc[i,j]-(g.vs[i].degree()*g.vs[j].degree())/(2*m))*deltaij)\n", + " Q=(1/(2*m))*np.sum(Q)\n", + " out=Q\n", + " return(out)\n", + "\n", + "modularity(g, [1,1,1,1,2,2,2,1])" + ] + }, + { + "cell_type": "markdown", + "id": "af45b651-a9f5-4abe-ae97-7c06731cc031", + "metadata": {}, + "source": [ + "As this is a very small network, we can take a brute-force approach and examine all possible membership combinations that will yield the highest possible modularity." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "53279fe9-8964-4556-b9bf-68f27d6eaf6d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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comm1comm2membQ
0[A, B, C, D][E, F, G, H](1, 1, 1, 1, 2, 2, 2, 2)0.423077
0[E, F, G, H][A, B, C, D](2, 2, 2, 2, 1, 1, 1, 1)0.423077
0[A, B, C, D, E][F, G, H](1, 1, 1, 1, 1, 2, 2, 2)0.221893
0[A, B, D][C, E, F, G, H](1, 1, 2, 1, 2, 2, 2, 2)0.221893
0[F, G, H][A, B, C, D, E](2, 2, 2, 2, 2, 1, 1, 1)0.221893
0[C, E, F, G, H][A, B, D](2, 2, 1, 2, 1, 1, 1, 1)0.221893
0[A, B, C, D, H][E, F, G](1, 1, 1, 1, 2, 2, 2, 1)0.16568
0[B, C, D][A, E, F, G, H](2, 1, 1, 1, 2, 2, 2, 2)0.16568
0[D, E, F, G, H][A, B, C](2, 2, 2, 1, 1, 1, 1, 1)0.16568
0[A, E, F, G, H][B, C, D](1, 2, 2, 2, 1, 1, 1, 1)0.16568
\n", + "
" + ], + "text/plain": [ + " comm1 comm2 memb Q\n", + "0 [A, B, C, D] [E, F, G, H] (1, 1, 1, 1, 2, 2, 2, 2) 0.423077\n", + "0 [E, F, G, H] [A, B, C, D] (2, 2, 2, 2, 1, 1, 1, 1) 0.423077\n", + "0 [A, B, C, D, E] [F, G, H] (1, 1, 1, 1, 1, 2, 2, 2) 0.221893\n", + "0 [A, B, D] [C, E, F, G, H] (1, 1, 2, 1, 2, 2, 2, 2) 0.221893\n", + "0 [F, G, H] [A, B, C, D, E] (2, 2, 2, 2, 2, 1, 1, 1) 0.221893\n", + "0 [C, E, F, G, H] [A, B, D] (2, 2, 1, 2, 1, 1, 1, 1) 0.221893\n", + "0 [A, B, C, D, H] [E, F, G] (1, 1, 1, 1, 2, 2, 2, 1) 0.16568\n", + "0 [B, C, D] [A, E, F, G, H] (2, 1, 1, 1, 2, 2, 2, 2) 0.16568\n", + "0 [D, E, F, G, H] [A, B, C] (2, 2, 2, 1, 1, 1, 1, 1) 0.16568\n", + "0 [A, E, F, G, H] [B, C, D] (1, 2, 2, 2, 1, 1, 1, 1) 0.16568" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import itertools\n", + "\n", + "all_memberships=[x for x in itertools.product([1,2], repeat=8)]\n", + "\n", + "\n", + "membership_modularity=pd.DataFrame()\n", + "for membership in all_memberships:\n", + " group1=[g.vs['name'][x] for x in range(len(g.vs['name'])) if membership[x]==1]\n", + " group2=[g.vs['name'][x] for x in range(len(g.vs['name'])) if membership[x]==2]\n", + " out=pd.Series([group1, group2, membership, modularity(g, membership)], index=['comm1', 'comm2','memb','Q'])\n", + " membership_modularity=pd.concat([membership_modularity, out], axis = 1)\n", + " \n", + "membership_modularity=membership_modularity.T.sort_values('Q', ascending=False)\n", + "membership_modularity.head(10)" + ] + }, + { + "cell_type": "markdown", + "id": "9f437e71-c326-40fa-8382-97ca5930042c", + "metadata": {}, + "source": [ + "We can see that the 2 top hits that maximize modularity define the same communities as `[A,B,C,D]` and `[E,F,G,H]`. For larger networks we cannot use a brute-force approach, and instead rely on the 2-pass Louvain algorithm, which has since been improved with the Leiden algorithm." + ] + }, + { + "cell_type": "markdown", + "id": "c2d23f08-6b7a-4c00-b3b0-d6a2685ffbac", + "metadata": {}, + "source": [ + "## Modularity of gene-metabolite networks" + ] + }, + { + "cell_type": "markdown", + "id": "a4f3cf6a-a2d8-45e2-a00d-1e65a81dc41c", + "metadata": {}, + "source": [ + "Below, we perform the community analysis on the 4 networks. We will perform one additional community analysis by considering the edge weights from the positively associated network. Importantly, this method searches for the largest possible communities for our network, which may not always be the desired. Alternative models such as the [Constant Potts Model](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.84.016114) allow you to identify smaller communities. Should we know that our data has special feature classes, we can compare whether the communities identify those classes by examining them individually, and increasing the resolution if needed.\n", + "\n", + "- We will use the leidenalg package, https://leidenalg.readthedocs.io/en/stable/install.html" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "e09f36a5-4c99-4566-99dc-fabcd0a95a79", + "metadata": {}, + "outputs": [], + "source": [ + "import leidenalg\n", + "\n", + "pos_comm = leidenalg.find_partition(pos_w, leidenalg.ModularityVertexPartition)\n", + "pos_w_comm = leidenalg.find_partition(pos_w, leidenalg.ModularityVertexPartition, weights='w')\n", + "all_comm = leidenalg.find_partition(all_u, leidenalg.ModularityVertexPartition)\n", + "knn_comm = leidenalg.find_partition(knn, leidenalg.ModularityVertexPartition)\n", + "random_all = leidenalg.find_partition(random_allu, leidenalg.ModularityVertexPartition)\n", + "random_posw = leidenalg.find_partition(random_posw, leidenalg.ModularityVertexPartition)\n", + "random_knn = leidenalg.find_partition(random_knn, leidenalg.ModularityVertexPartition)" + ] + }, + { + "cell_type": "markdown", + "id": "6d081877-540f-4a90-9a20-0af23b7601d8", + "metadata": {}, + "source": [ + "Predictably, we can see that the modularity score of any of the networks is substantially larger than that of the random network." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "dbaa8728-1476-462a-822e-6454cd81225f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.591" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.round(pos_comm.modularity,3)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "e33b6e02-9ed9-4bfe-a342-505404f28d32", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.591" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.round(pos_w_comm.modularity,3)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "1213939c-4106-4d85-8d69-91cac88c7c5c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.427" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.round(all_comm.modularity,3)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "3241c213-0893-4bce-a34e-0f2921785769", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.591" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.round(knn_comm.modularity,3)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "c1d0d68f-4591-4f68-996a-df7555b70584", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.038" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.round(random_all.modularity,3)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "c688e232-6437-42c2-85ae-3075df5aabca", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.047" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.round(random_posw.modularity,3)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "94a14080-c2d2-4eab-94e5-fcff51838f35", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.037" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.round(random_knn.modularity,3)" + ] + }, + { + "cell_type": "markdown", + "id": "0bbc1962-acfe-4207-9023-2f5ddf87fe7d", + "metadata": {}, + "source": [ + "Comparing the different communities by size:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "f2c71da9-5db2-4fb9-93b1-61d17b5e725c", + "metadata": {}, + "outputs": [], + "source": [ + "def get_community_table():\n", + " comm_counts=pd.DataFrame()\n", + " feat_lists=pd.DataFrame()\n", + " for i in [0,1,2,3]:\n", + " graph=[pos_w,pos_w,all_u,knn][i]\n", + " comm=[pos_comm,pos_w_comm,all_comm,knn_comm][i]\n", + " name=['pos','pos_w','all','knn'][i]\n", + " temp=pd.DataFrame(list(zip(graph.vs['name'],[x+1 for x in comm.membership]))).rename(columns={0:'feat',1:'community'})\n", + " counts=pd.DataFrame(temp.groupby('community')['feat'].agg(len))\n", + " counts.columns=[name]\n", + " comm_counts=pd.concat([comm_counts, counts], axis = 1)\n", + " \n", + " gl=pd.DataFrame(temp.groupby('community')['feat'].apply(list)).reset_index()\n", + " gl['community']=['c'+str(i) for i in gl['community']]\n", + " gl['network']=name\n", + " gl=gl.loc[:,['network','community','feat']]\n", + " feat_lists=pd.concat([feat_lists, gl])\n", + " \n", + " comm_counts.index=['c'+str(i) for i in comm_counts.index]\n", + " return([comm_counts,feat_lists])\n", + "\n", + "comm_counts, feat_lists = get_community_table()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "056bfb70-d8bc-44cc-92bd-2c5ee757346d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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4posc5[MT-ND2, MT-ATP6, MT-ND5, MT-ND4, MT-CO2, MT-A...
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" + ], + "text/plain": [ + " pos pos_w all knn\n", + "c1 907 902 696.0 515.0\n", + "c2 555 556 657.0 487.0\n", + "c3 507 506 611.0 487.0\n", + "c4 119 122 133.0 346.0\n", + "c5 9 11 3.0 267.0\n", + "c6 3 3 2.0 NaN\n", + "c7 2 2 NaN NaN" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comm_counts" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "39c020fe-81c3-4fd5-b51e-9dffe7bb9f20", + "metadata": {}, + "outputs": [], + "source": [ + "feat_lists.to_csv(\"data/serialization/feat_lists.csv\", sep = \"\\t\", index = False)\n", + "comm_counts.to_csv(\"data/serialization/comm_counts.csv\", sep = \"\\t\", index = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "25f52b2e-ebef-490f-a680-3528eff197a0", + "metadata": {}, + "outputs": [], + "source": [ + "# RUN on: igraph\n", + "degree_data['all_u_names'] = list(all_u.copy().vs['name'])\n", + "\n", + "import json\n", + "with open('data/serialization/degree_data.json', 'w') as file:\n", + " json.dump(degree_data, file)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e9f93d33-ff5c-4b64-bba1-6cd3813c2a95", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/session_topology/lab/topology_lab_part3.ipynb b/session_topology/lab/topology_lab_part3.ipynb new file mode 100644 index 00000000..1e9e71bb --- /dev/null +++ b/session_topology/lab/topology_lab_part3.ipynb @@ -0,0 +1,1007 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "32a985e0-b190-45f5-833b-37e1f5b3b17d", + "metadata": {}, + "source": [ + "# Topology lab, part 3\n", + "\n", + "## Functional analysis" + ] + }, + { + "cell_type": "markdown", + "id": "2de2a2de-56ca-48f1-82e1-25d22b832f0f", + "metadata": {}, + "source": [ + "In order to perform functional enrichment, we will extract each of the communities and perform a hypergeometric test **on the genes** to understand whether they are particularly enriched in specific biological functions. \n", + "We will use [enrichr](https://gseapy.readthedocs.io/en/master/gseapy_example.html#2.-Enrichr-Example) to perform the gene set enrichment analysis. As background we will use the full list of genes that were quantified.\n", + "\n", + "We will look at 3 gene set libraries. Should you have other kinds of data, enrichr allows you to define your own feature sets and perform a similar analysis. The challenge is in identifying comprehensive and well curated gene sets." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "807100bd-159a-4681-9532-22286b61e5c8", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import pandas as pd\n", + "import gseapy as gp\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5d3f8892-7d57-4e99-b5e4-6e0d1d034325", + "metadata": {}, + "outputs": [], + "source": [ + "with open('data/serialization/degree_data.json', 'r') as file:\n", + " degree_data = json.load(file)\n", + "\n", + "feat_lists = pd.read_csv(\"data/serialization/feat_lists.csv\", sep = \"\\t\")\n", + "comm_counts = pd.read_csv(\"data/serialization/comm_counts.csv\", sep = \"\\t\", index_col = 0)\n", + "data=pd.read_csv('data/met_genes.tsv', sep=\"\\t\", index_col=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1c45b7b9-49ff-4aa5-a2ed-f6d249294567", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['HDSigDB_Human_2021', 'HumanCyc_2015', 'HumanCyc_2016', 'Human_Gene_Atlas', 'Human_Phenotype_Ontology', 'KEGG_2019_Human', 'KEGG_2021_Human', 'NIH_Funded_PIs_2017_Human_AutoRIF', 'NIH_Funded_PIs_2017_Human_GeneRIF', 'NURSA_Human_Endogenous_Complexome', 'RNAseq_Automatic_GEO_Signatures_Human_Down', 'RNAseq_Automatic_GEO_Signatures_Human_Up', 'Tissue_Protein_Expression_from_Human_Proteome_Map', 'WikiPathway_2021_Human', 'WikiPathway_2023_Human', 'WikiPathways_2019_Human', 'WikiPathways_2024_Human']\n", + "['GO_Biological_Process_2013', 'GO_Biological_Process_2015', 'GO_Biological_Process_2017', 'GO_Biological_Process_2017b', 'GO_Biological_Process_2018', 'GO_Biological_Process_2021', 'GO_Biological_Process_2023']\n", + "['KEGG_2013', 'KEGG_2015', 'KEGG_2016', 'KEGG_2019_Human', 'KEGG_2019_Mouse', 'KEGG_2021_Human']\n", + "['OMIM_Disease']\n" + ] + } + ], + "source": [ + "# extract all the available human libraries in enrichr\n", + "libraries = gp.get_library_name()\n", + "print([lib for lib in libraries if 'Human' in lib])\n", + "print([lib for lib in libraries if 'GO_Biological_Process' in lib])\n", + "print([lib for lib in libraries if 'KEGG' in lib])\n", + "print([lib for lib in libraries if 'OMIM_Disease' in lib])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b3087500-9fcd-4aba-8d05-6326e1b65822", + "metadata": { + "code_folding": [], + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Analyzing pos network | Comm: c1/7 | BP: GO_Biological_Process_2023\n", + "Error for: pos c1 GO_Biological_Process_2023\n", + "Analyzing pos network | Comm: c1/7 | BP: KEGG_2021_Human\n", + "Error for: pos c1 KEGG_2021_Human\n", + "Analyzing pos network | Comm: c1/7 | BP: OMIM_Disease\n", + "Error for: pos c1 OMIM_Disease\n", + "Analyzing pos network | Comm: c2/7 | BP: GO_Biological_Process_2023\n", + "Analyzing pos network | Comm: c2/7 | BP: KEGG_2021_Human\n", + "Analyzing pos network | Comm: c2/7 | BP: OMIM_Disease\n", + "Error for: pos c2 OMIM_Disease\n", + "Analyzing pos network | Comm: c3/7 | BP: GO_Biological_Process_2023\n", + "Analyzing pos network | Comm: c3/7 | BP: KEGG_2021_Human\n", + "Analyzing pos network | Comm: c3/7 | BP: OMIM_Disease\n", + "Error for: pos c3 OMIM_Disease\n", + "Analyzing pos_w network | Comm: c1/7 | BP: GO_Biological_Process_2023\n", + "Error for: pos_w c1 GO_Biological_Process_2023\n", + "Analyzing pos_w network | Comm: c1/7 | BP: KEGG_2021_Human\n", + "Error for: pos_w c1 KEGG_2021_Human\n", + "Analyzing pos_w network | Comm: c1/7 | BP: OMIM_Disease\n", + "Error for: pos_w c1 OMIM_Disease\n", + "Analyzing pos_w network | Comm: c2/7 | BP: GO_Biological_Process_2023\n", + "Analyzing pos_w network | Comm: c2/7 | BP: KEGG_2021_Human\n", + "Analyzing pos_w network | Comm: c2/7 | BP: OMIM_Disease\n", + "Error for: pos_w c2 OMIM_Disease\n", + "Analyzing pos_w network | Comm: c3/7 | BP: GO_Biological_Process_2023\n", + "Analyzing pos_w network | Comm: c3/7 | BP: KEGG_2021_Human\n", + "Analyzing pos_w network | Comm: c3/7 | BP: OMIM_Disease\n", + "Error for: pos_w c3 OMIM_Disease\n", + "Analyzing all network | Comm: c1/6 | BP: GO_Biological_Process_2023\n", + "Analyzing all network | Comm: c1/6 | BP: KEGG_2021_Human\n", + "Analyzing all network | Comm: c1/6 | BP: OMIM_Disease\n", + "Error for: all c1 OMIM_Disease\n", + "Analyzing all network | Comm: c2/6 | BP: GO_Biological_Process_2023\n", + "Analyzing all network | Comm: c2/6 | BP: KEGG_2021_Human\n", + "Analyzing all network | Comm: c2/6 | BP: OMIM_Disease\n", + "Error for: all c2 OMIM_Disease\n", + "Analyzing all network | Comm: c3/6 | BP: GO_Biological_Process_2023\n", + "Analyzing all network | Comm: c3/6 | BP: KEGG_2021_Human\n", + "Analyzing all network | Comm: c3/6 | BP: OMIM_Disease\n", + "Error for: all c3 OMIM_Disease\n", + "Analyzing knn network | Comm: c1/5 | BP: GO_Biological_Process_2023\n", + "Error for: knn c1 GO_Biological_Process_2023\n", + "Analyzing knn network | Comm: c1/5 | BP: KEGG_2021_Human\n", + "Error for: knn c1 KEGG_2021_Human\n", + "Analyzing knn network | Comm: c1/5 | BP: OMIM_Disease\n", + "Error for: knn c1 OMIM_Disease\n", + "Analyzing knn network | Comm: c2/5 | BP: GO_Biological_Process_2023\n", + "Analyzing knn network | Comm: c2/5 | BP: KEGG_2021_Human\n", + "Analyzing knn network | Comm: c2/5 | BP: OMIM_Disease\n", + "Error for: knn c2 OMIM_Disease\n", + "Analyzing knn network | Comm: c3/5 | BP: GO_Biological_Process_2023\n", + "Analyzing knn network | Comm: c3/5 | BP: KEGG_2021_Human\n", + "Error for: knn c3 KEGG_2021_Human\n", + "Analyzing knn network | Comm: c3/5 | BP: OMIM_Disease\n", + "Error for: knn c3 OMIM_Disease\n", + "Analyzing knn network | Comm: c4/5 | BP: GO_Biological_Process_2023\n", + "Analyzing knn network | Comm: c4/5 | BP: KEGG_2021_Human\n", + "Analyzing knn network | Comm: c4/5 | BP: OMIM_Disease\n", + "Error for: knn c4 OMIM_Disease\n", + "Analyzing knn network | Comm: c5/5 | BP: GO_Biological_Process_2023\n", + "Analyzing knn network | Comm: c5/5 | BP: KEGG_2021_Human\n", + "Analyzing knn network | Comm: c5/5 | BP: OMIM_Disease\n", + "Error for: knn c5 OMIM_Disease\n" + ] + } + ], + "source": [ + "import ast\n", + "import gseapy as gp\n", + "\n", + "# we will search 3 libraries for significantly enriched gene sets\n", + "gene_sets = ['GO_Biological_Process_2023','KEGG_2021_Human','OMIM_Disease']\n", + "background=[x for x in degree_data['all_u_names'] if x in data.loc[data.Type=='genes'].index]\n", + "all_genes=data.loc[data.Type=='genes'].index\n", + "\n", + "def perform_enrich(network):\n", + " temp=feat_lists.copy()\n", + " temp=temp.loc[temp['network']==network]\n", + " output_enrichr=pd.DataFrame()\n", + " for comm in temp['community'].values:\n", + " # the dataframe holds the gene lists as strings, we parse them into lists\n", + " gl_string =temp.loc[temp['community']==comm, 'feat'].values[0]\n", + " gl = ast.literal_eval(gl_string)\n", + " gl=list([x for x in gl if x in all_genes])\n", + " # if the list is small we don't do the test\n", + " if(len(gl)<30):\n", + " continue\n", + " for bp in gene_sets:\n", + " print('Analyzing '+network+' network | Comm: '+comm+'/'+str(len(temp.index))+' | BP: '+bp)\n", + " # When P-values are under thresholds the program exits\n", + " # We avoid this by using a try catch block\n", + " try:\n", + " enr=gp.enrichr(\n", + " gene_list=gl,\n", + " gene_sets=bp,\n", + " background=background,\n", + " outdir='Enrichr',\n", + " format='png'\n", + " )\n", + " results=enr.results.sort_values('Adjusted P-value', ascending=True)\n", + " results=results.loc[results['Adjusted P-value']<0.05,]\n", + " results['BP']=bp\n", + " results['Comm']=comm\n", + " results['Graph']=network\n", + " output_enrichr=pd.concat([output_enrichr, results])\n", + " except Exception as e:\n", + " #print(f\"An unexpected error occurred: {e}\")\n", + " print(\"Error for:\", network, comm, bp)\n", + " return(output_enrichr)\n", + "\n", + "all_enriched=pd.DataFrame()\n", + "#perform_enrich('pos')\n", + "for net in ['pos', 'pos_w', 'all', 'knn']: \n", + " all_enriched=pd.concat([all_enriched,perform_enrich(net)])" + ] + }, + { + "cell_type": "markdown", + "id": "f3d74f98-b97d-4f6f-8500-6416306cf814", + "metadata": {}, + "source": [ + "From the output of the cell above you can readily see that some communities such as `c1` display no enriched terms from either [GO](http://geneontology.org/), [KEGG](https://www.genome.jp/kegg/) or [OMIM](https://www.omim.org/).\n", + "\n", + "Running the command above not only gives you the results after significance testing (Q<0.05), but it also outputs some preliminary barplots with the statistically significant results (found under `/Enrichr/`). For instance:\n", + "\n", + "> \"GO\n", + "> \"KEGG" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "88d49bd1-a188-4ccf-8c20-24680d8dd2e3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Gene_setTermP-valueAdjusted P-valueOld P-valueOld adjusted P-valueOdds RatioCombined ScoreGenesBPCommGraph
0GO_Biological_Process_2023Cytoplasmic Translation (GO:0002181)6.596737e-211.768585e-170010.248988476.247014EIF4A2;RPL4;RPL5;RPL30;RPL3;RPL32;RPL31;RPL34;...GO_Biological_Process_2023c2pos
1GO_Biological_Process_2023Translation (GO:0006412)2.464224e-203.303293e-17008.477737382.768320RPL4;RPL5;RPL30;RPL3;RPL32;RPL31;RPL34;RPLP1;R...GO_Biological_Process_2023c2pos
2GO_Biological_Process_2023Macromolecule Biosynthetic Process (GO:0009059)7.129468e-206.371368e-17008.330309367.262217RPL4;RPL5;RPL30;RPL3;RPL32;RPL31;RPL34;RPLP1;R...GO_Biological_Process_2023c2pos
3GO_Biological_Process_2023Peptide Biosynthetic Process (GO:0043043)2.043715e-191.369800e-16008.693117374.102635RPL4;RPL5;RPL30;RPL3;RPL32;RPL31;RPL34;RPLP1;R...GO_Biological_Process_2023c2pos
4GO_Biological_Process_2023Gene Expression (GO:0010467)3.477726e-191.864757e-16006.725773285.863776RPL4;RPL5;RPL30;RPL3;RPL32;RPL31;RPL34;RPLP1;H...GO_Biological_Process_2023c2pos
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" + ], + "text/plain": [ + " Gene_set \\\n", + "0 GO_Biological_Process_2023 \n", + "1 GO_Biological_Process_2023 \n", + "2 GO_Biological_Process_2023 \n", + "3 GO_Biological_Process_2023 \n", + "4 GO_Biological_Process_2023 \n", + "\n", + " Term P-value \\\n", + "0 Cytoplasmic Translation (GO:0002181) 6.596737e-21 \n", + "1 Translation (GO:0006412) 2.464224e-20 \n", + "2 Macromolecule Biosynthetic Process (GO:0009059) 7.129468e-20 \n", + "3 Peptide Biosynthetic Process (GO:0043043) 2.043715e-19 \n", + "4 Gene Expression (GO:0010467) 3.477726e-19 \n", + "\n", + " Adjusted P-value Old P-value Old adjusted P-value Odds Ratio \\\n", + "0 1.768585e-17 0 0 10.248988 \n", + "1 3.303293e-17 0 0 8.477737 \n", + "2 6.371368e-17 0 0 8.330309 \n", + "3 1.369800e-16 0 0 8.693117 \n", + "4 1.864757e-16 0 0 6.725773 \n", + "\n", + " Combined Score Genes \\\n", + "0 476.247014 EIF4A2;RPL4;RPL5;RPL30;RPL3;RPL32;RPL31;RPL34;... \n", + "1 382.768320 RPL4;RPL5;RPL30;RPL3;RPL32;RPL31;RPL34;RPLP1;R... \n", + "2 367.262217 RPL4;RPL5;RPL30;RPL3;RPL32;RPL31;RPL34;RPLP1;R... \n", + "3 374.102635 RPL4;RPL5;RPL30;RPL3;RPL32;RPL31;RPL34;RPLP1;R... \n", + "4 285.863776 RPL4;RPL5;RPL30;RPL3;RPL32;RPL31;RPL34;RPLP1;H... \n", + "\n", + " BP Comm Graph \n", + "0 GO_Biological_Process_2023 c2 pos \n", + "1 GO_Biological_Process_2023 c2 pos \n", + "2 GO_Biological_Process_2023 c2 pos \n", + "3 GO_Biological_Process_2023 c2 pos \n", + "4 GO_Biological_Process_2023 c2 pos " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_enriched.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "8355ba33-6037-470f-b32c-ba9226bef98f", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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GraphCommTermAdjusted P-value
0posc2Cytoplasmic Translation (GO:0002181)16.752374
1posc2Translation (GO:0006412)16.481053
2posc2Macromolecule Biosynthetic Process (GO:0009059)16.195767
3posc2Peptide Biosynthetic Process (GO:0043043)15.863343
4posc2Gene Expression (GO:0010467)15.729378
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" + ], + "text/plain": [ + " Graph Comm Term \\\n", + "0 pos c2 Cytoplasmic Translation (GO:0002181) \n", + "1 pos c2 Translation (GO:0006412) \n", + "2 pos c2 Macromolecule Biosynthetic Process (GO:0009059) \n", + "3 pos c2 Peptide Biosynthetic Process (GO:0043043) \n", + "4 pos c2 Gene Expression (GO:0010467) \n", + "\n", + " Adjusted P-value \n", + "0 16.752374 \n", + "1 16.481053 \n", + "2 16.195767 \n", + "3 15.863343 \n", + "4 15.729378 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "enriched_terms=all_enriched.loc[:,['Graph','Comm','Term','Adjusted P-value']].copy()\n", + "enriched_terms['Adjusted P-value']=-1*np.log10(enriched_terms['Adjusted P-value'])\n", + "enriched_terms.head()" + ] + }, + { + "cell_type": "markdown", + "id": "25665e6d-4d54-4329-b183-0ecedc33756c", + "metadata": {}, + "source": [ + "fig, ax = plt.subplots(figsize=(7, 4))\n", + "data_bars=pd.DataFrame(enriched_terms.groupby(['Graph','Comm'])['Term'].agg('count')).stack().reset_index().rename(columns={0:'Count'})\n", + "sns.barplot(x='Graph', y='Count', data=data_bars, hue='Comm')\n", + "ax.set_title('Number of significant Terms (Q < 0.05) per community')\n", + "ax.legend(loc='right', bbox_to_anchor=(1.15, 1));\n", + "plt.xticks(rotation=0)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "9ac1e8e6-2b83-4058-9846-7351234d078f", + "metadata": {}, + "source": [ + "Note that some of these communities are very big, which explains the big number of biological processes found above." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a59b4831-feb7-4121-ac25-dc62a09f4139", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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c1c2c3c4c5c6c7
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pos_w902.0556.0506.0122.011.03.02.0
all696.0657.0611.0133.03.02.00.0
knn515.0487.0487.0346.0267.00.00.0
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" + ], + "text/plain": [ + " c1 c2 c3 c4 c5 c6 c7\n", + "pos 907.0 555.0 507.0 119.0 9.0 3.0 2.0\n", + "pos_w 902.0 556.0 506.0 122.0 11.0 3.0 2.0\n", + "all 696.0 657.0 611.0 133.0 3.0 2.0 0.0\n", + "knn 515.0 487.0 487.0 346.0 267.0 0.0 0.0" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "###Number of genes/community\n", + "# We skipped communities with <30 genes\n", + "comm_counts.fillna(0).T" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "7c7a28ff-4f92-46e7-90cb-4473a1887c83", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Term
GraphComm
allc166
c218
c33
knnc299
c31
c4130
c515
posc241
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pos_wc241
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" + ], + "text/plain": [ + " Term\n", + "Graph Comm \n", + "all c1 66\n", + " c2 18\n", + " c3 3\n", + "knn c2 99\n", + " c3 1\n", + " c4 130\n", + " c5 15\n", + "pos c2 41\n", + " c3 132\n", + "pos_w c2 41\n", + " c3 132" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame(enriched_terms.groupby(['Graph','Comm'])['Term'].agg('count'))" + ] + }, + { + "cell_type": "markdown", + "id": "6ac5efc7-beec-43d4-93bd-e8f8f2041a5d", + "metadata": {}, + "source": [ + "We can now find whether the Full Network, Positively associated, and Positively associated weighted, show any common terms among their biggest communities. We do not compare with kNN-G as this shows very homogeneous and different communities than the other two networks" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "74a6b6fa-a86a-4c41-abd6-9171e91be1cd", + "metadata": {}, + "outputs": [], + "source": [ + "#Finding consensus\n", + "temp=enriched_terms.copy()\n", + "temp['comm_term']=temp.Comm+'_'+temp.Term\n", + "temp=temp.loc[:,['Graph','comm_term']]\n", + "\n", + "consensus=pd.DataFrame()\n", + "consensus=pd.concat([consensus, temp.loc[temp['Graph']=='pos']])\n", + "consensus=pd.merge(consensus,\n", + " temp.loc[temp['Graph']=='pos_w'], on=\"comm_term\", how='outer', suffixes=['pos','pos_w'])\n", + "consensus=pd.merge(consensus, \n", + " temp.loc[temp['Graph']=='all'], on=\"comm_term\", how='outer').rename(columns={'Graph':'all'})\n", + "\n", + "consensus=consensus.loc[consensus.isna().sum(1)==0].loc[:,['comm_term','Graphpos','Graphpos_w','all']]" + ] + }, + { + "cell_type": "markdown", + "id": "cedb1bcd-7b9d-4079-ba1b-1e22ddcba06f", + "metadata": {}, + "source": [ + "Among the biggest communities we find several biological processes (55) that are simultaneously identified in the same community in the three graphs (`Full`, `Pos assoc`, and `Pos assoc weighted`)." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "5329c661-90d6-4ec3-bccf-4478e0a69469", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "comm\n", + "c2 18\n", + "Name: comm_term, dtype: int64" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "consensus['comm']=[x[0] for x in consensus.comm_term.str.split('_')]\n", + "consensus.groupby('comm')['comm_term'].agg('count')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "f4cd809a-b962-4bb2-bd12-53bfc54bd88c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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comm_termGraphposGraphpos_wallcomm
69c2_Circulatory System Development (GO:0072359)pospos_wallc2
70c2_Coronavirus diseasepospos_wallc2
71c2_Cytoplasmic Translation (GO:0002181)pospos_wallc2
73c2_ECM-receptor interactionpospos_wallc2
74c2_Focal adhesionpospos_wallc2
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" + ], + "text/plain": [ + " comm_term Graphpos Graphpos_w all \\\n", + "69 c2_Circulatory System Development (GO:0072359) pos pos_w all \n", + "70 c2_Coronavirus disease pos pos_w all \n", + "71 c2_Cytoplasmic Translation (GO:0002181) pos pos_w all \n", + "73 c2_ECM-receptor interaction pos pos_w all \n", + "74 c2_Focal adhesion pos pos_w all \n", + "\n", + " comm \n", + "69 c2 \n", + "70 c2 \n", + "71 c2 \n", + "73 c2 \n", + "74 c2 " + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "consensus.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "40f46462-7df7-46d6-ae80-29f315108574", + "metadata": {}, + "outputs": [], + "source": [ + "enriched_terms.to_csv(\"data/serialization/enriched_terms.csv\", sep = \"\\t\", index = False)" + ] + }, + { + "cell_type": "markdown", + "id": "f95746b9-f812-4222-a3e7-8104936e04e6", + "metadata": {}, + "source": [ + "**Questions** \n", + "- Would you exclude any communities based on its size?\n", + "- Having identified these communities, how would you try to validate them?\n", + "- Would you now determine the relevant community to investigate further?\n" + ] + }, + { + "cell_type": "markdown", + "id": "8032ee30-adf6-4969-8f49-f75995c833cf", + "metadata": {}, + "source": [ + "If you want to export communities to use them in other sections." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0cc66bb2-527e-40bf-a708-f643157d0969", + "metadata": { + "code_folding": [] + }, + "outputs": [], + "source": [ + "# Compiles gene lists per community. We need Ensembl ids for further analyses\n", + "# TODO: the code bellow is not working as it requires all three environments, or lots of files being saved\n", + "# Homework?\n", + "# Requires running the community detection from the previous section\n", + "patlas=pd.read_csv('lab/data/proteinatlas.tsv', sep=\"\\t\").loc[:,['Ensembl','Gene']]\n", + "def get_ensembl():\n", + " comm_counts=pd.DataFrame()\n", + " gene_lists=pd.DataFrame()\n", + " for i in [0,1,2,3]:\n", + " graph=[\"pos_w\",\"pos_w\",\"all_u\",\"knn\"][i]\n", + " comm=[pos_comm,pos_w_comm,all_comm,knn_comm][i]\n", + " name=['pos','pos_w','all','knn'][i]\n", + " temp=pd.DataFrame(list(zip(graph.vs['name'],[x+1 for x in comm.membership]))).rename(columns={0:'gene',1:'community'})\n", + "\n", + " gl=pd.DataFrame(temp.groupby('community')['gene'].apply(list)).reset_index()\n", + " gl['community']=['c'+str(i) for i in gl['community']]\n", + " gl['network']=name\n", + " gl=gl.loc[:,['network','community','gene']]\n", + " gene_lists=pd.concat([gene_lists, gl])\n", + "\n", + " gene_communities=gene_lists\n", + " gene_mat=pd.DataFrame()\n", + " for net in gene_communities['network'].unique():\n", + " temp=gene_communities.copy().loc[gene_communities['network']==net,]\n", + " for comm in temp['community'].unique():\n", + " gl=list(temp.copy().loc[temp['community']==comm,'gene'])[0]\n", + " el=[patlas.loc[patlas['Gene']==x,'Ensembl'].iloc[0] for x in gl if x in patlas['Gene'].values]\n", + "\n", + " df=pd.DataFrame([net,comm,gl,el]).T\n", + " df.columns=['network','community','Gene','Ensembl']\n", + " gene_mat=pd.concat([gene_mat, df])\n", + " \n", + " return(gene_mat)\n", + "\n", + "get_ensembl().to_csv('data/gene_communities.tsv', sep=\"\\t\", index=False)" + ] + }, + { + "cell_type": "markdown", + "id": "a2bb7494-6808-459c-b16c-04524f21fe77", + "metadata": {}, + "source": [ + "# Conclusion" + ] + }, + { + "cell_type": "markdown", + "id": "9c9692a3-2be5-46ee-bee3-c1d7fff22abc", + "metadata": {}, + "source": [ + "Here we've performed some network analyses based on a met-gene association network. We've explored different centrality measures to characterize the networks, and identified the communities of genes in these networks. We have also used gene set enrichment analysis to characterize these communities based on the genes, but it remains to show whether similar results would be attained if we considered the metabolites in each community. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/session_topology/topology_env.yml b/session_topology/topology_env.yml index 2a7894ed..683acbd9 100644 --- a/session_topology/topology_env.yml +++ b/session_topology/topology_env.yml @@ -1,9 +1,9 @@ -name: topology +name: datasci channels: - conda-forge - defaults dependencies: - - python=3.9 + - python=3.12 - scikit-learn - pandas - numpy @@ -11,8 +11,32 @@ dependencies: - statsmodels - matplotlib - seaborn - - ipykernel - - pip + - jupyterlab + +--- +name: igraph +channels: + - conda-forge + - defaults +dependencies: + - python=3.12 + - pandas + - numpy + - jupyterlab + - leidenalg + - pycairo + - pip: + - igraph + +--- +name: gseapy +channels: + - conda-forge + - defaults +dependencies: + - python=3.12 + - pandas + - numpy + - jupyterlab - pip: - - gseapy - - igraph \ No newline at end of file + - gseapy \ No newline at end of file