From 01fd7039decf2cee9c12b20de9ef915f8ce39ec5 Mon Sep 17 00:00:00 2001 From: Daphne Go Date: Sat, 11 Jan 2025 22:22:30 +0800 Subject: [PATCH] updated for metaphlan analysis code --- notebooks/statistical.ipynb | 823 +++++++++++++++++++++++++++++++++++- 1 file changed, 819 insertions(+), 4 deletions(-) diff --git a/notebooks/statistical.ipynb b/notebooks/statistical.ipynb index f917244..d3dc833 100644 --- a/notebooks/statistical.ipynb +++ b/notebooks/statistical.ipynb @@ -228,7 +228,7 @@ "source": [ "Reading File for Kraken Data\n", "\n", - "Kraken data contains results from the taxonomic run earlier, retrieving this from the results folder." + "Metaphlan data contains results from the taxonomic run earlier, retrieving this from the results folder." ] }, { @@ -252,7 +252,7 @@ "\n", "1. Converts a dataset (`kraken_data`) from **wide** format to **long** format using `pivot_longer`.\n", "\n", - "From Wide Format (Original Kraken Results):\n", + "From Wide Format (Original Metaphlan Results):\n", "| ID | Sample1 | Sample2 | Sample3 |\n", "|------|---------|---------|---------|\n", "| Taxa1 | 0.5 | 0.3 | 0.2 |\n", @@ -878,7 +878,7 @@ "outputs": [ { "data": { - "image/png": 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WRTp05NT09fuHDhhg0bWrRoERkZmZ2dnZyc/Mcff3icBVhUVLR///4OHTp4XE4h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\nI4T48ccfx4wZU7XrCtLJTtnTXCULyairsltGwsPDlYmUlJSqW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", "text/plain": [ "plot without title" ] @@ -915,6 +915,821 @@ "print(p)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Metaphlan" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Reading File for Metadata\n", + "\n", + "Metadata contains information on the samples and hospital names. The hospitals are labelled as A, B and C respectively. This is provided in the data folder." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "metadata <- read.table(\"/home/jovyan/data/metadata.txt\", sep = \"\\t\", header = TRUE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 x 23
SampleIDaliasdatecontinentcountrycountry2country3namehospitalreplicate...hospital_sectionhospital_descriptionno_of_bedslatlongsample_materialDNA_ng_.lA260_280M_Seqs_trimmedplot_name
<chr><chr><chr><chr><chr><chr><chr><chr><chr><chr>...<chr><chr><chr><dbl><dbl><chr><dbl><dbl><dbl><chr>
1BH02_S77 BH02 27_11_19West AfricaBenin Benin BENN HWW ABENN HWW AANO...pediatrySecondary hospital 200 6.49245 2.40193water 44.1151.86330.8hospital effluent
2BH03_S78 BH03 27_11_19West AfricaBenin Benin BENN HWW ABENN HWW AANO...surgery Secondary hospital 200 6.49245 2.40193water 98.5721.84431.6hospital effluent
3BFH10_S128BFH1028_11_19West AfricaBurkina FasoBurkina FasoBF HWW F BF HWW F BNO...other Health clinic 91 12.50000-1.66667water119.4771.86932.0hospital effluent
4BFH33_S151BFH3312_12_19West AfricaBurkina FasoBurkina FasoBF HWW I BF HWW I BNO...other Tertiary hospital, a national refrence hospital73312.50000-1.66667water 72.6421.85733.7hospital effluent
5FH1_S162 FH1 20_01_20Europe Finland Finland FI HWW J FI HWW J CNO...other nd nd 60.1984524.92737water 49.3001.87021.2hospital effluent
6FH2_S163 FH2 20_01_20Europe Finland Finland FI HWW K FI HWW K CNO...other nd nd 60.1984524.92737water 70.4001.88037.9hospital effluent
\n" + ], + "text/latex": [ + "A data.frame: 6 x 23\n", + "\\begin{tabular}{r|lllllllllllllllllllll}\n", + " & SampleID & alias & date & continent & country & country2 & country3 & name & hospital & replicate & ... & hospital\\_section & hospital\\_description & no\\_of\\_beds & lat & long & sample\\_material & DNA\\_ng\\_.l & A260\\_280 & M\\_Seqs\\_trimmed & plot\\_name\\\\\n", + " & & & & & & & & & & & ... & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t1 & BH02\\_S77 & BH02 & 27\\_11\\_19 & West Africa & Benin & Benin & BENN HWW A & BENN HWW A & A & NO & ... & pediatry & Secondary hospital & 200 & 6.49245 & 2.40193 & water & 44.115 & 1.863 & 30.8 & hospital effluent\\\\\n", + "\t2 & BH03\\_S78 & BH03 & 27\\_11\\_19 & West Africa & Benin & Benin & BENN HWW A & BENN HWW A & A & NO & ... & surgery & Secondary hospital & 200 & 6.49245 & 2.40193 & water & 98.572 & 1.844 & 31.6 & hospital effluent\\\\\n", + "\t3 & BFH10\\_S128 & BFH10 & 28\\_11\\_19 & West Africa & Burkina Faso & Burkina Faso & BF HWW F & BF HWW F & B & NO & ... & other & Health clinic & 91 & 12.50000 & -1.66667 & water & 119.477 & 1.869 & 32.0 & hospital effluent\\\\\n", + "\t4 & BFH33\\_S151 & BFH33 & 12\\_12\\_19 & West Africa & Burkina Faso & Burkina Faso & BF HWW I & BF HWW I & B & NO & ... & other & Tertiary hospital, a national refrence hospital & 733 & 12.50000 & -1.66667 & water & 72.642 & 1.857 & 33.7 & hospital effluent\\\\\n", + "\t5 & FH1\\_S162 & FH1 & 20\\_01\\_20 & Europe & Finland & Finland & FI HWW J & FI HWW J & C & NO & ... & other & nd & nd & 60.19845 & 24.92737 & water & 49.300 & 1.870 & 21.2 & hospital effluent\\\\\n", + "\t6 & FH2\\_S163 & FH2 & 20\\_01\\_20 & Europe & Finland & Finland & FI HWW K & FI HWW K & C & NO & ... & other & nd & nd & 60.19845 & 24.92737 & water & 70.400 & 1.880 & 37.9 & hospital effluent\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 x 23\n", + "\n", + "| | SampleID <chr> | alias <chr> | date <chr> | continent <chr> | country <chr> | country2 <chr> | country3 <chr> | name <chr> | hospital <chr> | replicate <chr> | ... ... | hospital_section <chr> | hospital_description <chr> | no_of_beds <chr> | lat <dbl> | long <dbl> | sample_material <chr> | DNA_ng_.l <dbl> | A260_280 <dbl> | M_Seqs_trimmed <dbl> | plot_name <chr> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 1 | BH02_S77 | BH02 | 27_11_19 | West Africa | Benin | Benin | BENN HWW A | BENN HWW A | A | NO | ... | pediatry | Secondary hospital | 200 | 6.49245 | 2.40193 | water | 44.115 | 1.863 | 30.8 | hospital effluent |\n", + "| 2 | BH03_S78 | BH03 | 27_11_19 | West Africa | Benin | Benin | BENN HWW A | BENN HWW A | A | NO | ... | surgery | Secondary hospital | 200 | 6.49245 | 2.40193 | water | 98.572 | 1.844 | 31.6 | hospital effluent |\n", + "| 3 | BFH10_S128 | BFH10 | 28_11_19 | West Africa | Burkina Faso | Burkina Faso | BF HWW F | BF HWW F | B | NO | ... | other | Health clinic | 91 | 12.50000 | -1.66667 | water | 119.477 | 1.869 | 32.0 | hospital effluent |\n", + "| 4 | BFH33_S151 | BFH33 | 12_12_19 | West Africa | Burkina Faso | Burkina Faso | BF HWW I | BF HWW I | B | NO | ... | other | Tertiary hospital, a national refrence hospital | 733 | 12.50000 | -1.66667 | water | 72.642 | 1.857 | 33.7 | hospital effluent |\n", + "| 5 | FH1_S162 | FH1 | 20_01_20 | Europe | Finland | Finland | FI HWW J | FI HWW J | C | NO | ... | other | nd | nd | 60.19845 | 24.92737 | water | 49.300 | 1.870 | 21.2 | hospital effluent |\n", + "| 6 | FH2_S163 | FH2 | 20_01_20 | Europe | Finland | Finland | FI HWW K | FI HWW K | C | NO | ... | other | nd | nd | 60.19845 | 24.92737 | water | 70.400 | 1.880 | 37.9 | hospital effluent |\n", + "\n" + ], + "text/plain": [ + " SampleID alias date continent country country2 country3 \n", + "1 BH02_S77 BH02 27_11_19 West Africa Benin Benin BENN HWW A\n", + "2 BH03_S78 BH03 27_11_19 West Africa Benin Benin BENN HWW A\n", + "3 BFH10_S128 BFH10 28_11_19 West Africa Burkina Faso Burkina Faso BF HWW F \n", + "4 BFH33_S151 BFH33 12_12_19 West Africa Burkina Faso Burkina Faso BF HWW I \n", + "5 FH1_S162 FH1 20_01_20 Europe Finland Finland FI HWW J \n", + "6 FH2_S163 FH2 20_01_20 Europe Finland Finland FI HWW K \n", + " name hospital replicate ... hospital_section\n", + "1 BENN HWW A A NO ... pediatry \n", + "2 BENN HWW A A NO ... surgery \n", + "3 BF HWW F B NO ... other \n", + "4 BF HWW I B NO ... other \n", + "5 FI HWW J C NO ... other \n", + "6 FI HWW K C NO ... other \n", + " hospital_description no_of_beds lat long \n", + "1 Secondary hospital 200 6.49245 2.40193\n", + "2 Secondary hospital 200 6.49245 2.40193\n", + "3 Health clinic 91 12.50000 -1.66667\n", + "4 Tertiary hospital, a national refrence hospital 733 12.50000 -1.66667\n", + "5 nd nd 60.19845 24.92737\n", + "6 nd nd 60.19845 24.92737\n", + " sample_material DNA_ng_.l A260_280 M_Seqs_trimmed plot_name \n", + "1 water 44.115 1.863 30.8 hospital effluent\n", + "2 water 98.572 1.844 31.6 hospital effluent\n", + "3 water 119.477 1.869 32.0 hospital effluent\n", + "4 water 72.642 1.857 33.7 hospital effluent\n", + "5 water 49.300 1.870 21.2 hospital effluent\n", + "6 water 70.400 1.880 37.9 hospital effluent" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "head(metadata)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Reading File for Metaphlan Data\n", + "\n", + "Metaphlan data contains results from the taxonomic run earlier, retrieving this from the results folder." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# Skip first column as it contains the mpa version\n", + "metaphlan_data <- read.delim(\"merged_abundance_table.txt\", header = TRUE, skip =1)\n", + "# To make it simpler, we rename clade_name to ID (as clade_name refers to the taxonomic ID)\n", + "colnames(metaphlan_data)[colnames(metaphlan_data) == \"clade_name\"] <- \"ID\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Merging Metaphlan Results with Metadata\n", + "\n", + "1. Converts a dataset (`metaphlan_data`) from **wide** format to **long** format using `pivot_longer`.\n", + "\n", + "From Wide Format (Original Metaphlan Results):\n", + "| ID | Sample1 | Sample2 | Sample3 |\n", + "|------|---------|---------|---------|\n", + "| Taxa1 | 0.5 | 0.3 | 0.2 |\n", + "| Taxa2 | 0.2 | 0.1 | 0.4 |\n", + "| Taxa3 | 0.3 | 0.6 | 0.4 |\n", + "\n", + "\n", + "To Long Format \n", + "| ID | SampleID | RelativeAbundance |\n", + "|------|----------|-------------------|\n", + "| Taxa1 | Sample1 | 0.5 |\n", + "| Taxa1 | Sample2 | 0.3 |\n", + "| Taxa1 | Sample3 | 0.2 |\n", + "| Taxa2 | Sample1 | 0.2 |\n", + "| Taxa2 | Sample2 | 0.1 |\n", + "| Taxa2 | Sample3 | 0.4 |\n", + "| Taxa3 | Sample1 | 0.3 |\n", + "| Taxa3 | Sample2 | 0.6 |\n", + "| Taxa3 | Sample3 | 0.4 |\n", + "\n", + "2. Merges the reshaped data with another dataset (`metadata`) based on a common key, `SampleID`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "metaphlan_with_metadata <- metaphlan_data %>%\n", + " pivot_longer(cols = -ID, names_to = \"SampleID\", values_to = \"RelativeAbundance\") %>%\n", + " merge(metadata, by = \"SampleID\", all.x = TRUE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Adding Taxonomic Information\n", + "The taxonomic ID in the dataset typically includes hierarchical information in a format like:\n", + "\n", + "`k__Bacteria|p__Firmicutes|c__Clostridia`\n", + "\n", + "This is a hierarchical string, where each part represents a taxonomic rank (e.g., Kingdom, Phylum, Class, etc.). \n", + "\n", + "For example:\n", + "\n", + "- `k__Bacteria`: Represents Kingdom\n", + "- `p__Firmicutes`: Represents Phylum\n", + "- `c__Clostridia`: Represents Class\n", + "\n", + "\n", + "However, in many cases, only the most specific taxonomic rank (like Genus or Species) is of interest. To focus on this specific label, we need to extract the last part of the string (the most specific taxonomic level).\n", + "\n", + "For example:\n", + "\n", + "- Original: `k__Bacteria|p__Firmicutes|c__Clostridia`\n", + "- Extracted: Clostridia\n", + "\n", + "This allows you to work with more meaningful and interpretable taxonomic labels (e.g., species names) rather than the entire hierarchical string." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\t
$Class
\n", + "\t\t
\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 x 180
SampleIDaliasdatecontinentcountrycountry2country3namehospitalreplicate...BeAn_58058_virusTectivirusPandoravirus_dulcisPandoravirus_inopinatumPandoravirus_salinusTospoviridaeAckermannviridaeMyoviridaeEpsilonproteobacteriaGammaproteobacteria
<chr><chr><chr><chr><chr><chr><chr><chr><chr><chr>...<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
BFH10_S128BFH1028_11_19West AfricaBurkina FasoBurkina FasoBF HWW F BF HWW F BNO...0.00000e+000.00000e+000.0001170593.51177e-040.0003511770.00000e+000.0000000000.006087062.125440 5.82602
BFH33_S151BFH3312_12_19West AfricaBurkina FasoBurkina FasoBF HWW I BF HWW I BNO...0.00000e+006.64309e-050.0000000000.00000e+000.0000000003.32155e-040.0000000000.239550006.68607041.71240
BH02_S77 BH02 27_11_19West AfricaBenin Benin BENN HWW ABENN HWW AANO...0.00000e+000.00000e+000.0000000009.24408e-050.0000000000.00000e+000.0007395270.010630700.21021058.55210
BH03_S78 BH03 27_11_19West AfricaBenin Benin BENN HWW ABENN HWW AANO...0.00000e+004.19822e-040.0000000000.00000e+000.0001049550.00000e+000.0058775100.029072700.220302 5.78578
FH1_S162 FH1 20_01_20Europe Finland Finland FI HWW J FI HWW J CNO...0.00000e+000.00000e+000.0000000000.00000e+000.0000000007.11076e-050.0000000000.165752002.76523052.31220
FH2_S163 FH2 20_01_20Europe Finland Finland FI HWW K FI HWW K CNO...8.05431e-058.05431e-050.0000000000.00000e+000.0000000004.02716e-040.0002416290.007571055.82005026.62020
\n", + "
\n", + "\t
$Family
\n", + "\t\t
\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 x 787
SampleIDaliasdatecontinentcountrycountry2country3namehospitalreplicate...PlesiomonasCandidatus_Thioglobus_autotrophicusCandidatus_Ruthia_magnificaImmundisolibacteraceaeGallaecimonas_sp._HK-28Candidatus_Thioglobus_singularisThiolapillus_brandeumSedimenticola_thiotauriniPseudohongiella_spirulinaeThiohalobacter_thiocyanaticus
<chr><chr><chr><chr><chr><chr><chr><chr><chr><chr>...<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
BFH10_S128BFH1028_11_19West AfricaBurkina FasoBurkina FasoBF HWW F BF HWW F BNO...0.0004682355.85294e-040.0008194120.013227700.0019900001.28765e-030.0044482400.003043530.0002341180.00913059
BFH33_S151BFH3312_12_19West AfricaBurkina FasoBurkina FasoBF HWW I BF HWW I BNO...0.0154120004.65016e-040.0007307400.002657240.0020593605.97878e-040.0011293300.002391510.0013286200.00345441
BH02_S77 BH02 27_11_19West AfricaBenin Benin BENN HWW ABENN HWW AANO...0.0708097000.00000e+000.0000000000.004344720.0098911706.47086e-040.0028656700.015807400.0055464500.00406740
BH03_S78 BH03 27_11_19West AfricaBenin Benin BENN HWW ABENN HWW AANO...0.0052477701.04955e-040.0000000000.005037860.0008396441.04955e-040.0048279500.005877510.0013644200.00703202
FH1_S162 FH1 20_01_20Europe Finland Finland FI HWW J FI HWW J CNO...0.0079640501.42215e-040.0002133230.003768700.0007110767.11076e-050.0009243990.001137720.0004266460.00206212
FH2_S163 FH2 20_01_20Europe Finland Finland FI HWW K FI HWW K CNO...0.0024968408.05431e-050.0014497800.008376480.0021746604.02716e-040.0008859740.001771950.0011276000.00402716
\n", + "
\n", + "\t
$Genus
\n", + "\t\t
\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 x 1316
SampleIDaliasdatecontinentcountrycountry2country3namehospitalreplicate...NautiliaPlesiomonas_shigelloidesPhytobacter_sp._SCO41WenyingzhuangiaWeeksellaImmundisolibacterWinogradskyellaZunongwangiaFlavobacteriaceae_bacteriumZobellia
<chr><chr><chr><chr><chr><chr><chr><chr><chr><chr>...<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
BFH10_S128BFH1028_11_19West AfricaBurkina FasoBurkina FasoBF HWW F BF HWW F BNO...0.0015217700.0004682350.0003511770.0009364710.0003511770.013227700.002926470.0016388200.0004682355.85294e-04
BFH33_S151BFH3312_12_19West AfricaBurkina FasoBurkina FasoBF HWW I BF HWW I BNO...0.0010628900.0154120000.0026572400.0011293300.0135519000.002657240.003852990.0009300330.0012621909.96464e-04
BH02_S77 BH02 27_11_19West AfricaBenin Benin BENN HWW ABENN HWW AANO...0.0000000000.0708097000.0296735000.0002773230.0002773230.004344720.001386610.0002773230.0000000009.24408e-05
BH03_S78 BH03 27_11_19West AfricaBenin Benin BENN HWW ABENN HWW AANO...0.0013644200.0052477700.0007346880.0001049550.0005247770.005037860.004198220.0003148660.0003148663.14866e-04
FH1_S162 FH1 20_01_20Europe Finland Finland FI HWW J FI HWW J CNO...0.0002844300.0079640500.0021332300.0019199100.0127283000.003768700.006044150.0005688610.0011377204.26646e-04
FH2_S163 FH2 20_01_20Europe Finland Finland FI HWW K FI HWW K CNO...0.0001610860.0024968400.0035439000.0023357500.0089402900.008376480.007812680.0012886900.0009665178.85974e-04
\n", + "
\n", + "\t
$Kingdom
\n", + "\t\t
\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 x 29
SampleIDaliasdatecontinentcountrycountry2country3namehospitalreplicate...DNA_ng_.lA260_280M_Seqs_trimmedplot_nameTaxonomic_LevelTaxonomic_LabelArchaeaBacteriaEukaryotaViruses
<chr><chr><chr><chr><chr><chr><chr><chr><chr><chr>...<dbl><dbl><dbl><chr><int><chr><dbl><dbl><dbl><dbl>
BFH10_S128BFH1028_11_19West AfricaBurkina FasoBurkina FasoBF HWW F BF HWW F BNO...119.4771.86932.0hospital effluent1Kingdom2.539830096.65340.7530400.0537300
BFH33_S151BFH3312_12_19West AfricaBurkina FasoBurkina FasoBF HWW I BF HWW I BNO... 72.6421.85733.7hospital effluent1Kingdom0.426819098.44330.8032160.3266410
BH02_S77 BH02 27_11_19West AfricaBenin Benin BENN HWW ABENN HWW AANO... 44.1151.86330.8hospital effluent1Kingdom0.133947098.85840.5408710.4668260
BH03_S78 BH03 27_11_19West AfricaBenin Benin BENN HWW ABENN HWW AANO... 98.5721.84431.6hospital effluent1Kingdom0.316336098.52210.8607400.3008020
FH1_S162 FH1 20_01_20Europe Finland Finland FI HWW J FI HWW J CNO... 49.3001.87021.2hospital effluent1Kingdom0.047997696.42363.0981600.4302720
FH2_S163 FH2 20_01_20Europe Finland Finland FI HWW K FI HWW K CNO... 70.4001.88037.9hospital effluent1Kingdom0.122828097.29172.5495900.0358417
\n", + "
\n", + "\t
$Order
\n", + "\t\t
\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 x 523
SampleIDaliasdatecontinentcountrycountry2country3namehospitalreplicate...NautilialesCandidatus_ThioglobusCandidatus_RuthiaImmundisolibacteralesLegionellalesPseudohongiellaGallaecimonasThiohalobacterSedimenticolaThiolapillus
<chr><chr><chr><chr><chr><chr><chr><chr><chr><chr>...<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
BFH10_S128BFH1028_11_19West AfricaBurkina FasoBurkina FasoBF HWW F BF HWW F BNO...0.0015217700.0018729400.0008194120.013227700.010535300.0002341180.0019900000.009130590.003043530.004448240
BFH33_S151BFH3312_12_19West AfricaBurkina FasoBurkina FasoBF HWW I BF HWW I BNO...0.0010628900.0011957600.0007307400.002657240.008436730.0013286200.0020593600.003454410.002391510.001129330
BH02_S77 BH02 27_11_19West AfricaBenin Benin BENN HWW ABENN HWW AANO...0.0000000000.0006470860.0000000000.004344720.047976800.0055464500.0098911700.004067400.015807400.002865670
BH03_S78 BH03 27_11_19West AfricaBenin Benin BENN HWW ABENN HWW AANO...0.0013644200.0002099110.0000000000.005037860.008081570.0013644200.0008396440.007032020.005877510.004827950
FH1_S162 FH1 20_01_20Europe Finland Finland FI HWW J FI HWW J CNO...0.0002844300.0002133230.0002133230.003768700.005333070.0004266460.0007110760.002062120.001137720.000924399
FH2_S163 FH2 20_01_20Europe Finland Finland FI HWW K FI HWW K CNO...0.0001610860.0004832590.0014497800.008376480.007329420.0011276000.0021746600.004027160.001771950.000885974
\n", + "
\n", + "\t
$Phylum
\n", + "\t\t
\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 x 96
SampleIDaliasdatecontinentcountrycountry2country3namehospitalreplicate...TectiviridaeBeihai_rhabdo-like_virus_2Hubei_picorna-like_virus_45Hubei_sobemo-like_virus_38PandoravirusPseudomonas_phage_vB_PaeP_Tr60_Ab31Wenling_nido-like_virus_1uncultured_crAssphageBunyaviralesCaudovirales
<chr><chr><chr><chr><chr><chr><chr><chr><chr><chr>...<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
BFH10_S128BFH1028_11_19West AfricaBurkina FasoBurkina FasoBF HWW F BF HWW F BNO...0.00000e+000.00000e+000.0001170590.00000e+008.19412e-040.00000e+000.0000000000.009715890.00000e+000.0420241
BFH33_S151BFH3312_12_19West AfricaBurkina FasoBurkina FasoBF HWW I BF HWW I BNO...6.64309e-056.64309e-050.0000000006.64309e-050.00000e+000.00000e+000.0000000000.007174543.32155e-040.3184030
BH02_S77 BH02 27_11_19West AfricaBenin Benin BENN HWW ABENN HWW AANO...0.00000e+000.00000e+000.0000000000.00000e+009.24408e-050.00000e+000.0000000000.000000000.00000e+000.4659020
BH03_S78 BH03 27_11_19West AfricaBenin Benin BENN HWW ABENN HWW AANO...4.19822e-040.00000e+000.0000000000.00000e+001.04955e-040.00000e+000.0002099110.190914000.00000e+000.1085240
FH1_S162 FH1 20_01_20Europe Finland Finland FI HWW J FI HWW J CNO...0.00000e+000.00000e+000.0000000000.00000e+000.00000e+007.11076e-050.0000000000.134678007.11076e-050.2921810
FH2_S163 FH2 20_01_20Europe Finland Finland FI HWW K FI HWW K CNO...8.05431e-050.00000e+000.0000000000.00000e+000.00000e+000.00000e+000.0000000000.011115004.02716e-040.0222299
\n", + "
\n", + "
\n" + ], + "text/latex": [ + "\\begin{description}\n", + "\\item[\\$Class] A tibble: 6 x 180\n", + "\\begin{tabular}{lllllllllllllllllllll}\n", + " SampleID & alias & date & continent & country & country2 & country3 & name & hospital & replicate & ... & BeAn\\_58058\\_virus & Tectivirus & Pandoravirus\\_dulcis & Pandoravirus\\_inopinatum & Pandoravirus\\_salinus & Tospoviridae & Ackermannviridae & Myoviridae & Epsilonproteobacteria & Gammaproteobacteria\\\\\n", + " & & & & & & & & & & ... & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t BFH10\\_S128 & BFH10 & 28\\_11\\_19 & West Africa & Burkina Faso & Burkina Faso & BF HWW F & BF HWW F & B & NO & ... & 0.00000e+00 & 0.00000e+00 & 0.000117059 & 3.51177e-04 & 0.000351177 & 0.00000e+00 & 0.000000000 & 0.00608706 & 2.125440 & 5.82602\\\\\n", + "\t BFH33\\_S151 & BFH33 & 12\\_12\\_19 & West Africa & Burkina Faso & Burkina Faso & BF HWW I & BF HWW I & B & NO & ... & 0.00000e+00 & 6.64309e-05 & 0.000000000 & 0.00000e+00 & 0.000000000 & 3.32155e-04 & 0.000000000 & 0.23955000 & 6.686070 & 41.71240\\\\\n", + "\t BH02\\_S77 & BH02 & 27\\_11\\_19 & West Africa & Benin & Benin & BENN HWW A & BENN HWW A & A & NO & ... & 0.00000e+00 & 0.00000e+00 & 0.000000000 & 9.24408e-05 & 0.000000000 & 0.00000e+00 & 0.000739527 & 0.01063070 & 0.210210 & 58.55210\\\\\n", + "\t BH03\\_S78 & BH03 & 27\\_11\\_19 & West Africa & Benin & Benin & BENN HWW A & BENN HWW A & A & NO & ... & 0.00000e+00 & 4.19822e-04 & 0.000000000 & 0.00000e+00 & 0.000104955 & 0.00000e+00 & 0.005877510 & 0.02907270 & 0.220302 & 5.78578\\\\\n", + "\t FH1\\_S162 & FH1 & 20\\_01\\_20 & Europe & Finland & Finland & FI HWW J & FI HWW J & C & NO & ... & 0.00000e+00 & 0.00000e+00 & 0.000000000 & 0.00000e+00 & 0.000000000 & 7.11076e-05 & 0.000000000 & 0.16575200 & 2.765230 & 52.31220\\\\\n", + "\t FH2\\_S163 & FH2 & 20\\_01\\_20 & Europe & Finland & Finland & FI HWW K & FI HWW K & C & NO & ... & 8.05431e-05 & 8.05431e-05 & 0.000000000 & 0.00000e+00 & 0.000000000 & 4.02716e-04 & 0.000241629 & 0.00757105 & 5.820050 & 26.62020\\\\\n", + "\\end{tabular}\n", + "\n", + "\\item[\\$Family] A tibble: 6 x 787\n", + "\\begin{tabular}{lllllllllllllllllllll}\n", + " SampleID & alias & date & continent & country & country2 & country3 & name & hospital & replicate & ... & Plesiomonas & Candidatus\\_Thioglobus\\_autotrophicus & Candidatus\\_Ruthia\\_magnifica & Immundisolibacteraceae & Gallaecimonas\\_sp.\\_HK-28 & Candidatus\\_Thioglobus\\_singularis & Thiolapillus\\_brandeum & Sedimenticola\\_thiotaurini & Pseudohongiella\\_spirulinae & Thiohalobacter\\_thiocyanaticus\\\\\n", + " & & & & & & & & & & ... & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t BFH10\\_S128 & BFH10 & 28\\_11\\_19 & West Africa & Burkina Faso & Burkina Faso & BF HWW F & BF HWW F & B & NO & ... & 0.000468235 & 5.85294e-04 & 0.000819412 & 0.01322770 & 0.001990000 & 1.28765e-03 & 0.004448240 & 0.00304353 & 0.000234118 & 0.00913059\\\\\n", + "\t BFH33\\_S151 & BFH33 & 12\\_12\\_19 & West Africa & Burkina Faso & Burkina Faso & BF HWW I & BF HWW I & B & NO & ... & 0.015412000 & 4.65016e-04 & 0.000730740 & 0.00265724 & 0.002059360 & 5.97878e-04 & 0.001129330 & 0.00239151 & 0.001328620 & 0.00345441\\\\\n", + "\t BH02\\_S77 & BH02 & 27\\_11\\_19 & West Africa & Benin & Benin & BENN HWW A & BENN HWW A & A & NO & ... & 0.070809700 & 0.00000e+00 & 0.000000000 & 0.00434472 & 0.009891170 & 6.47086e-04 & 0.002865670 & 0.01580740 & 0.005546450 & 0.00406740\\\\\n", + "\t BH03\\_S78 & BH03 & 27\\_11\\_19 & West Africa & Benin & Benin & BENN HWW A & BENN HWW A & A & NO & ... & 0.005247770 & 1.04955e-04 & 0.000000000 & 0.00503786 & 0.000839644 & 1.04955e-04 & 0.004827950 & 0.00587751 & 0.001364420 & 0.00703202\\\\\n", + "\t FH1\\_S162 & FH1 & 20\\_01\\_20 & Europe & Finland & Finland & FI HWW J & FI HWW J & C & NO & ... & 0.007964050 & 1.42215e-04 & 0.000213323 & 0.00376870 & 0.000711076 & 7.11076e-05 & 0.000924399 & 0.00113772 & 0.000426646 & 0.00206212\\\\\n", + "\t FH2\\_S163 & FH2 & 20\\_01\\_20 & Europe & Finland & Finland & FI HWW K & FI HWW K & C & NO & ... & 0.002496840 & 8.05431e-05 & 0.001449780 & 0.00837648 & 0.002174660 & 4.02716e-04 & 0.000885974 & 0.00177195 & 0.001127600 & 0.00402716\\\\\n", + "\\end{tabular}\n", + "\n", + "\\item[\\$Genus] A tibble: 6 x 1316\n", + "\\begin{tabular}{lllllllllllllllllllll}\n", + " SampleID & alias & date & continent & country & country2 & country3 & name & hospital & replicate & ... & Nautilia & Plesiomonas\\_shigelloides & Phytobacter\\_sp.\\_SCO41 & Wenyingzhuangia & Weeksella & Immundisolibacter & Winogradskyella & Zunongwangia & Flavobacteriaceae\\_bacterium & Zobellia\\\\\n", + " & & & & & & & & & & ... & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t BFH10\\_S128 & BFH10 & 28\\_11\\_19 & West Africa & Burkina Faso & Burkina Faso & BF HWW F & BF HWW F & B & NO & ... & 0.001521770 & 0.000468235 & 0.000351177 & 0.000936471 & 0.000351177 & 0.01322770 & 0.00292647 & 0.001638820 & 0.000468235 & 5.85294e-04\\\\\n", + "\t BFH33\\_S151 & BFH33 & 12\\_12\\_19 & West Africa & Burkina Faso & Burkina Faso & BF HWW I & BF HWW I & B & NO & ... & 0.001062890 & 0.015412000 & 0.002657240 & 0.001129330 & 0.013551900 & 0.00265724 & 0.00385299 & 0.000930033 & 0.001262190 & 9.96464e-04\\\\\n", + "\t BH02\\_S77 & BH02 & 27\\_11\\_19 & West Africa & Benin & Benin & BENN HWW A & BENN HWW A & A & NO & ... & 0.000000000 & 0.070809700 & 0.029673500 & 0.000277323 & 0.000277323 & 0.00434472 & 0.00138661 & 0.000277323 & 0.000000000 & 9.24408e-05\\\\\n", + "\t BH03\\_S78 & BH03 & 27\\_11\\_19 & West Africa & Benin & Benin & BENN HWW A & BENN HWW A & A & NO & ... & 0.001364420 & 0.005247770 & 0.000734688 & 0.000104955 & 0.000524777 & 0.00503786 & 0.00419822 & 0.000314866 & 0.000314866 & 3.14866e-04\\\\\n", + "\t FH1\\_S162 & FH1 & 20\\_01\\_20 & Europe & Finland & Finland & FI HWW J & FI HWW J & C & NO & ... & 0.000284430 & 0.007964050 & 0.002133230 & 0.001919910 & 0.012728300 & 0.00376870 & 0.00604415 & 0.000568861 & 0.001137720 & 4.26646e-04\\\\\n", + "\t FH2\\_S163 & FH2 & 20\\_01\\_20 & Europe & Finland & Finland & FI HWW K & FI HWW K & C & NO & ... & 0.000161086 & 0.002496840 & 0.003543900 & 0.002335750 & 0.008940290 & 0.00837648 & 0.00781268 & 0.001288690 & 0.000966517 & 8.85974e-04\\\\\n", + "\\end{tabular}\n", + "\n", + "\\item[\\$Kingdom] A tibble: 6 x 29\n", + "\\begin{tabular}{lllllllllllllllllllll}\n", + " SampleID & alias & date & continent & country & country2 & country3 & name & hospital & replicate & ... & DNA\\_ng\\_.l & A260\\_280 & M\\_Seqs\\_trimmed & plot\\_name & Taxonomic\\_Level & Taxonomic\\_Label & Archaea & Bacteria & Eukaryota & Viruses\\\\\n", + " & & & & & & & & & & ... & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t BFH10\\_S128 & BFH10 & 28\\_11\\_19 & West Africa & Burkina Faso & Burkina Faso & BF HWW F & BF HWW F & B & NO & ... & 119.477 & 1.869 & 32.0 & hospital effluent & 1 & Kingdom & 2.5398300 & 96.6534 & 0.753040 & 0.0537300\\\\\n", + "\t BFH33\\_S151 & BFH33 & 12\\_12\\_19 & West Africa & Burkina Faso & Burkina Faso & BF HWW I & BF HWW I & B & NO & ... & 72.642 & 1.857 & 33.7 & hospital effluent & 1 & Kingdom & 0.4268190 & 98.4433 & 0.803216 & 0.3266410\\\\\n", + "\t BH02\\_S77 & BH02 & 27\\_11\\_19 & West Africa & Benin & Benin & BENN HWW A & BENN HWW A & A & NO & ... & 44.115 & 1.863 & 30.8 & hospital effluent & 1 & Kingdom & 0.1339470 & 98.8584 & 0.540871 & 0.4668260\\\\\n", + "\t BH03\\_S78 & BH03 & 27\\_11\\_19 & West Africa & Benin & Benin & BENN HWW A & BENN HWW A & A & NO & ... & 98.572 & 1.844 & 31.6 & hospital effluent & 1 & Kingdom & 0.3163360 & 98.5221 & 0.860740 & 0.3008020\\\\\n", + "\t FH1\\_S162 & FH1 & 20\\_01\\_20 & Europe & Finland & Finland & FI HWW J & FI HWW J & C & NO & ... & 49.300 & 1.870 & 21.2 & hospital effluent & 1 & Kingdom & 0.0479976 & 96.4236 & 3.098160 & 0.4302720\\\\\n", + "\t FH2\\_S163 & FH2 & 20\\_01\\_20 & Europe & Finland & Finland & FI HWW K & FI HWW K & C & NO & ... & 70.400 & 1.880 & 37.9 & hospital effluent & 1 & Kingdom & 0.1228280 & 97.2917 & 2.549590 & 0.0358417\\\\\n", + "\\end{tabular}\n", + "\n", + "\\item[\\$Order] A tibble: 6 x 523\n", + "\\begin{tabular}{lllllllllllllllllllll}\n", + " SampleID & alias & date & continent & country & country2 & country3 & name & hospital & replicate & ... & Nautiliales & Candidatus\\_Thioglobus & Candidatus\\_Ruthia & Immundisolibacterales & Legionellales & Pseudohongiella & Gallaecimonas & Thiohalobacter & Sedimenticola & Thiolapillus\\\\\n", + " & & & & & & & & & & ... & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t BFH10\\_S128 & BFH10 & 28\\_11\\_19 & West Africa & Burkina Faso & Burkina Faso & BF HWW F & BF HWW F & B & NO & ... & 0.001521770 & 0.001872940 & 0.000819412 & 0.01322770 & 0.01053530 & 0.000234118 & 0.001990000 & 0.00913059 & 0.00304353 & 0.004448240\\\\\n", + "\t BFH33\\_S151 & BFH33 & 12\\_12\\_19 & West Africa & Burkina Faso & Burkina Faso & BF HWW I & BF HWW I & B & NO & ... & 0.001062890 & 0.001195760 & 0.000730740 & 0.00265724 & 0.00843673 & 0.001328620 & 0.002059360 & 0.00345441 & 0.00239151 & 0.001129330\\\\\n", + "\t BH02\\_S77 & BH02 & 27\\_11\\_19 & West Africa & Benin & Benin & BENN HWW A & BENN HWW A & A & NO & ... & 0.000000000 & 0.000647086 & 0.000000000 & 0.00434472 & 0.04797680 & 0.005546450 & 0.009891170 & 0.00406740 & 0.01580740 & 0.002865670\\\\\n", + "\t BH03\\_S78 & BH03 & 27\\_11\\_19 & West Africa & Benin & Benin & BENN HWW A & BENN HWW A & A & NO & ... & 0.001364420 & 0.000209911 & 0.000000000 & 0.00503786 & 0.00808157 & 0.001364420 & 0.000839644 & 0.00703202 & 0.00587751 & 0.004827950\\\\\n", + "\t FH1\\_S162 & FH1 & 20\\_01\\_20 & Europe & Finland & Finland & FI HWW J & FI HWW J & C & NO & ... & 0.000284430 & 0.000213323 & 0.000213323 & 0.00376870 & 0.00533307 & 0.000426646 & 0.000711076 & 0.00206212 & 0.00113772 & 0.000924399\\\\\n", + "\t FH2\\_S163 & FH2 & 20\\_01\\_20 & Europe & Finland & Finland & FI HWW K & FI HWW K & C & NO & ... & 0.000161086 & 0.000483259 & 0.001449780 & 0.00837648 & 0.00732942 & 0.001127600 & 0.002174660 & 0.00402716 & 0.00177195 & 0.000885974\\\\\n", + "\\end{tabular}\n", + "\n", + "\\item[\\$Phylum] A tibble: 6 x 96\n", + "\\begin{tabular}{lllllllllllllllllllll}\n", + " SampleID & alias & date & continent & country & country2 & country3 & name & hospital & replicate & ... & Tectiviridae & Beihai\\_rhabdo-like\\_virus\\_2 & Hubei\\_picorna-like\\_virus\\_45 & Hubei\\_sobemo-like\\_virus\\_38 & Pandoravirus & Pseudomonas\\_phage\\_vB\\_PaeP\\_Tr60\\_Ab31 & Wenling\\_nido-like\\_virus\\_1 & uncultured\\_crAssphage & Bunyavirales & Caudovirales\\\\\n", + " & & & & & & & & & & ... & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t BFH10\\_S128 & BFH10 & 28\\_11\\_19 & West Africa & Burkina Faso & Burkina Faso & BF HWW F & BF HWW F & B & NO & ... & 0.00000e+00 & 0.00000e+00 & 0.000117059 & 0.00000e+00 & 8.19412e-04 & 0.00000e+00 & 0.000000000 & 0.00971589 & 0.00000e+00 & 0.0420241\\\\\n", + "\t BFH33\\_S151 & BFH33 & 12\\_12\\_19 & West Africa & Burkina Faso & Burkina Faso & BF HWW I & BF HWW I & B & NO & ... & 6.64309e-05 & 6.64309e-05 & 0.000000000 & 6.64309e-05 & 0.00000e+00 & 0.00000e+00 & 0.000000000 & 0.00717454 & 3.32155e-04 & 0.3184030\\\\\n", + "\t BH02\\_S77 & BH02 & 27\\_11\\_19 & West Africa & Benin & Benin & BENN HWW A & BENN HWW A & A & NO & ... & 0.00000e+00 & 0.00000e+00 & 0.000000000 & 0.00000e+00 & 9.24408e-05 & 0.00000e+00 & 0.000000000 & 0.00000000 & 0.00000e+00 & 0.4659020\\\\\n", + "\t BH03\\_S78 & BH03 & 27\\_11\\_19 & West Africa & Benin & Benin & BENN HWW A & BENN HWW A & A & NO & ... & 4.19822e-04 & 0.00000e+00 & 0.000000000 & 0.00000e+00 & 1.04955e-04 & 0.00000e+00 & 0.000209911 & 0.19091400 & 0.00000e+00 & 0.1085240\\\\\n", + "\t FH1\\_S162 & FH1 & 20\\_01\\_20 & Europe & Finland & Finland & FI HWW J & FI HWW J & C & NO & ... & 0.00000e+00 & 0.00000e+00 & 0.000000000 & 0.00000e+00 & 0.00000e+00 & 7.11076e-05 & 0.000000000 & 0.13467800 & 7.11076e-05 & 0.2921810\\\\\n", + "\t FH2\\_S163 & FH2 & 20\\_01\\_20 & Europe & Finland & Finland & FI HWW K & FI HWW K & C & NO & ... & 8.05431e-05 & 0.00000e+00 & 0.000000000 & 0.00000e+00 & 0.00000e+00 & 0.00000e+00 & 0.000000000 & 0.01111500 & 4.02716e-04 & 0.0222299\\\\\n", + "\\end{tabular}\n", + "\n", + "\\end{description}\n" + ], + "text/markdown": [ + "$Class\n", + ": \n", + "A tibble: 6 x 180\n", + "\n", + "| SampleID <chr> | alias <chr> | date <chr> | continent <chr> | country <chr> | country2 <chr> | country3 <chr> | name <chr> | hospital <chr> | replicate <chr> | ... ... | BeAn_58058_virus <dbl> | Tectivirus <dbl> | Pandoravirus_dulcis <dbl> | Pandoravirus_inopinatum <dbl> | Pandoravirus_salinus <dbl> | Tospoviridae <dbl> | Ackermannviridae <dbl> | Myoviridae <dbl> | Epsilonproteobacteria <dbl> | Gammaproteobacteria <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| BFH10_S128 | BFH10 | 28_11_19 | West Africa | Burkina Faso | Burkina Faso | BF HWW F | BF HWW F | B | NO | ... | 0.00000e+00 | 0.00000e+00 | 0.000117059 | 3.51177e-04 | 0.000351177 | 0.00000e+00 | 0.000000000 | 0.00608706 | 2.125440 | 5.82602 |\n", + "| BFH33_S151 | BFH33 | 12_12_19 | West Africa | Burkina Faso | Burkina Faso | BF HWW I | BF HWW I | B | NO | ... | 0.00000e+00 | 6.64309e-05 | 0.000000000 | 0.00000e+00 | 0.000000000 | 3.32155e-04 | 0.000000000 | 0.23955000 | 6.686070 | 41.71240 |\n", + "| BH02_S77 | BH02 | 27_11_19 | West Africa | Benin | Benin | BENN HWW A | BENN HWW A | A | NO | ... | 0.00000e+00 | 0.00000e+00 | 0.000000000 | 9.24408e-05 | 0.000000000 | 0.00000e+00 | 0.000739527 | 0.01063070 | 0.210210 | 58.55210 |\n", + "| BH03_S78 | BH03 | 27_11_19 | West Africa | Benin | Benin | BENN HWW A | BENN HWW A | A | NO | ... | 0.00000e+00 | 4.19822e-04 | 0.000000000 | 0.00000e+00 | 0.000104955 | 0.00000e+00 | 0.005877510 | 0.02907270 | 0.220302 | 5.78578 |\n", + "| FH1_S162 | FH1 | 20_01_20 | Europe | Finland | Finland | FI HWW J | FI HWW J | C | NO | ... | 0.00000e+00 | 0.00000e+00 | 0.000000000 | 0.00000e+00 | 0.000000000 | 7.11076e-05 | 0.000000000 | 0.16575200 | 2.765230 | 52.31220 |\n", + "| FH2_S163 | FH2 | 20_01_20 | Europe | Finland | Finland | FI HWW K | FI HWW K | C | NO | ... | 8.05431e-05 | 8.05431e-05 | 0.000000000 | 0.00000e+00 | 0.000000000 | 4.02716e-04 | 0.000241629 | 0.00757105 | 5.820050 | 26.62020 |\n", + "\n", + "\n", + "$Family\n", + ": \n", + "A tibble: 6 x 787\n", + "\n", + "| SampleID <chr> | alias <chr> | date <chr> | continent <chr> | country <chr> | country2 <chr> | country3 <chr> | name <chr> | hospital <chr> | replicate <chr> | ... ... | Plesiomonas <dbl> | Candidatus_Thioglobus_autotrophicus <dbl> | Candidatus_Ruthia_magnifica <dbl> | Immundisolibacteraceae <dbl> | Gallaecimonas_sp._HK-28 <dbl> | Candidatus_Thioglobus_singularis <dbl> | Thiolapillus_brandeum <dbl> | Sedimenticola_thiotaurini <dbl> | Pseudohongiella_spirulinae <dbl> | Thiohalobacter_thiocyanaticus <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| BFH10_S128 | BFH10 | 28_11_19 | West Africa | Burkina Faso | Burkina Faso | BF HWW F | BF HWW F | B | NO | ... | 0.000468235 | 5.85294e-04 | 0.000819412 | 0.01322770 | 0.001990000 | 1.28765e-03 | 0.004448240 | 0.00304353 | 0.000234118 | 0.00913059 |\n", + "| BFH33_S151 | BFH33 | 12_12_19 | West Africa | Burkina Faso | Burkina Faso | BF HWW I | BF HWW I | B | NO | ... | 0.015412000 | 4.65016e-04 | 0.000730740 | 0.00265724 | 0.002059360 | 5.97878e-04 | 0.001129330 | 0.00239151 | 0.001328620 | 0.00345441 |\n", + "| BH02_S77 | BH02 | 27_11_19 | West Africa | Benin | Benin | BENN HWW A | BENN HWW A | A | NO | ... | 0.070809700 | 0.00000e+00 | 0.000000000 | 0.00434472 | 0.009891170 | 6.47086e-04 | 0.002865670 | 0.01580740 | 0.005546450 | 0.00406740 |\n", + "| BH03_S78 | BH03 | 27_11_19 | West Africa | Benin | Benin | BENN HWW A | BENN HWW A | A | NO | ... | 0.005247770 | 1.04955e-04 | 0.000000000 | 0.00503786 | 0.000839644 | 1.04955e-04 | 0.004827950 | 0.00587751 | 0.001364420 | 0.00703202 |\n", + "| FH1_S162 | FH1 | 20_01_20 | Europe | Finland | Finland | FI HWW J | FI HWW J | C | NO | ... | 0.007964050 | 1.42215e-04 | 0.000213323 | 0.00376870 | 0.000711076 | 7.11076e-05 | 0.000924399 | 0.00113772 | 0.000426646 | 0.00206212 |\n", + "| FH2_S163 | FH2 | 20_01_20 | Europe | Finland | Finland | FI HWW K | FI HWW K | C | NO | ... | 0.002496840 | 8.05431e-05 | 0.001449780 | 0.00837648 | 0.002174660 | 4.02716e-04 | 0.000885974 | 0.00177195 | 0.001127600 | 0.00402716 |\n", + "\n", + "\n", + "$Genus\n", + ": \n", + "A tibble: 6 x 1316\n", + "\n", + "| SampleID <chr> | alias <chr> | date <chr> | continent <chr> | country <chr> | country2 <chr> | country3 <chr> | name <chr> | hospital <chr> | replicate <chr> | ... ... | Nautilia <dbl> | Plesiomonas_shigelloides <dbl> | Phytobacter_sp._SCO41 <dbl> | Wenyingzhuangia <dbl> | Weeksella <dbl> | Immundisolibacter <dbl> | Winogradskyella <dbl> | Zunongwangia <dbl> | Flavobacteriaceae_bacterium <dbl> | Zobellia <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| BFH10_S128 | BFH10 | 28_11_19 | West Africa | Burkina Faso | Burkina Faso | BF HWW F | BF HWW F | B | NO | ... | 0.001521770 | 0.000468235 | 0.000351177 | 0.000936471 | 0.000351177 | 0.01322770 | 0.00292647 | 0.001638820 | 0.000468235 | 5.85294e-04 |\n", + "| BFH33_S151 | BFH33 | 12_12_19 | West Africa | Burkina Faso | Burkina Faso | BF HWW I | BF HWW I | B | NO | ... | 0.001062890 | 0.015412000 | 0.002657240 | 0.001129330 | 0.013551900 | 0.00265724 | 0.00385299 | 0.000930033 | 0.001262190 | 9.96464e-04 |\n", + "| BH02_S77 | BH02 | 27_11_19 | West Africa | Benin | Benin | BENN HWW A | BENN HWW A | A | NO | ... | 0.000000000 | 0.070809700 | 0.029673500 | 0.000277323 | 0.000277323 | 0.00434472 | 0.00138661 | 0.000277323 | 0.000000000 | 9.24408e-05 |\n", + "| BH03_S78 | BH03 | 27_11_19 | West Africa | Benin | Benin | BENN HWW A | BENN HWW A | A | NO | ... | 0.001364420 | 0.005247770 | 0.000734688 | 0.000104955 | 0.000524777 | 0.00503786 | 0.00419822 | 0.000314866 | 0.000314866 | 3.14866e-04 |\n", + "| FH1_S162 | FH1 | 20_01_20 | Europe | Finland | Finland | FI HWW J | FI HWW J | C | NO | ... | 0.000284430 | 0.007964050 | 0.002133230 | 0.001919910 | 0.012728300 | 0.00376870 | 0.00604415 | 0.000568861 | 0.001137720 | 4.26646e-04 |\n", + "| FH2_S163 | FH2 | 20_01_20 | Europe | Finland | Finland | FI HWW K | FI HWW K | C | NO | ... | 0.000161086 | 0.002496840 | 0.003543900 | 0.002335750 | 0.008940290 | 0.00837648 | 0.00781268 | 0.001288690 | 0.000966517 | 8.85974e-04 |\n", + "\n", + "\n", + "$Kingdom\n", + ": \n", + "A tibble: 6 x 29\n", + "\n", + "| SampleID <chr> | alias <chr> | date <chr> | continent <chr> | country <chr> | country2 <chr> | country3 <chr> | name <chr> | hospital <chr> | replicate <chr> | ... ... | DNA_ng_.l <dbl> | A260_280 <dbl> | M_Seqs_trimmed <dbl> | plot_name <chr> | Taxonomic_Level <int> | Taxonomic_Label <chr> | Archaea <dbl> | Bacteria <dbl> | Eukaryota <dbl> | Viruses <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| BFH10_S128 | BFH10 | 28_11_19 | West Africa | Burkina Faso | Burkina Faso | BF HWW F | BF HWW F | B | NO | ... | 119.477 | 1.869 | 32.0 | hospital effluent | 1 | Kingdom | 2.5398300 | 96.6534 | 0.753040 | 0.0537300 |\n", + "| BFH33_S151 | BFH33 | 12_12_19 | West Africa | Burkina Faso | Burkina Faso | BF HWW I | BF HWW I | B | NO | ... | 72.642 | 1.857 | 33.7 | hospital effluent | 1 | Kingdom | 0.4268190 | 98.4433 | 0.803216 | 0.3266410 |\n", + "| BH02_S77 | BH02 | 27_11_19 | West Africa | Benin | Benin | BENN HWW A | BENN HWW A | A | NO | ... | 44.115 | 1.863 | 30.8 | hospital effluent | 1 | Kingdom | 0.1339470 | 98.8584 | 0.540871 | 0.4668260 |\n", + "| BH03_S78 | BH03 | 27_11_19 | West Africa | Benin | Benin | BENN HWW A | BENN HWW A | A | NO | ... | 98.572 | 1.844 | 31.6 | hospital effluent | 1 | Kingdom | 0.3163360 | 98.5221 | 0.860740 | 0.3008020 |\n", + "| FH1_S162 | FH1 | 20_01_20 | Europe | Finland | Finland | FI HWW J | FI HWW J | C | NO | ... | 49.300 | 1.870 | 21.2 | hospital effluent | 1 | Kingdom | 0.0479976 | 96.4236 | 3.098160 | 0.4302720 |\n", + "| FH2_S163 | FH2 | 20_01_20 | Europe | Finland | Finland | FI HWW K | FI HWW K | C | NO | ... | 70.400 | 1.880 | 37.9 | hospital effluent | 1 | Kingdom | 0.1228280 | 97.2917 | 2.549590 | 0.0358417 |\n", + "\n", + "\n", + "$Order\n", + ": \n", + "A tibble: 6 x 523\n", + "\n", + "| SampleID <chr> | alias <chr> | date <chr> | continent <chr> | country <chr> | country2 <chr> | country3 <chr> | name <chr> | hospital <chr> | replicate <chr> | ... ... | Nautiliales <dbl> | Candidatus_Thioglobus <dbl> | Candidatus_Ruthia <dbl> | Immundisolibacterales <dbl> | Legionellales <dbl> | Pseudohongiella <dbl> | Gallaecimonas <dbl> | Thiohalobacter <dbl> | Sedimenticola <dbl> | Thiolapillus <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| BFH10_S128 | BFH10 | 28_11_19 | West Africa | Burkina Faso | Burkina Faso | BF HWW F | BF HWW F | B | NO | ... | 0.001521770 | 0.001872940 | 0.000819412 | 0.01322770 | 0.01053530 | 0.000234118 | 0.001990000 | 0.00913059 | 0.00304353 | 0.004448240 |\n", + "| BFH33_S151 | BFH33 | 12_12_19 | West Africa | Burkina Faso | Burkina Faso | BF HWW I | BF HWW I | B | NO | ... | 0.001062890 | 0.001195760 | 0.000730740 | 0.00265724 | 0.00843673 | 0.001328620 | 0.002059360 | 0.00345441 | 0.00239151 | 0.001129330 |\n", + "| BH02_S77 | BH02 | 27_11_19 | West Africa | Benin | Benin | BENN HWW A | BENN HWW A | A | NO | ... | 0.000000000 | 0.000647086 | 0.000000000 | 0.00434472 | 0.04797680 | 0.005546450 | 0.009891170 | 0.00406740 | 0.01580740 | 0.002865670 |\n", + "| BH03_S78 | BH03 | 27_11_19 | West Africa | Benin | Benin | BENN HWW A | BENN HWW A | A | NO | ... | 0.001364420 | 0.000209911 | 0.000000000 | 0.00503786 | 0.00808157 | 0.001364420 | 0.000839644 | 0.00703202 | 0.00587751 | 0.004827950 |\n", + "| FH1_S162 | FH1 | 20_01_20 | Europe | Finland | Finland | FI HWW J | FI HWW J | C | NO | ... | 0.000284430 | 0.000213323 | 0.000213323 | 0.00376870 | 0.00533307 | 0.000426646 | 0.000711076 | 0.00206212 | 0.00113772 | 0.000924399 |\n", + "| FH2_S163 | FH2 | 20_01_20 | Europe | Finland | Finland | FI HWW K | FI HWW K | C | NO | ... | 0.000161086 | 0.000483259 | 0.001449780 | 0.00837648 | 0.00732942 | 0.001127600 | 0.002174660 | 0.00402716 | 0.00177195 | 0.000885974 |\n", + "\n", + "\n", + "$Phylum\n", + ": \n", + "A tibble: 6 x 96\n", + "\n", + "| SampleID <chr> | alias <chr> | date <chr> | continent <chr> | country <chr> | country2 <chr> | country3 <chr> | name <chr> | hospital <chr> | replicate <chr> | ... ... | Tectiviridae <dbl> | Beihai_rhabdo-like_virus_2 <dbl> | Hubei_picorna-like_virus_45 <dbl> | Hubei_sobemo-like_virus_38 <dbl> | Pandoravirus <dbl> | Pseudomonas_phage_vB_PaeP_Tr60_Ab31 <dbl> | Wenling_nido-like_virus_1 <dbl> | uncultured_crAssphage <dbl> | Bunyavirales <dbl> | Caudovirales <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| BFH10_S128 | BFH10 | 28_11_19 | West Africa | Burkina Faso | Burkina Faso | BF HWW F | BF HWW F | B | NO | ... | 0.00000e+00 | 0.00000e+00 | 0.000117059 | 0.00000e+00 | 8.19412e-04 | 0.00000e+00 | 0.000000000 | 0.00971589 | 0.00000e+00 | 0.0420241 |\n", + "| BFH33_S151 | BFH33 | 12_12_19 | West Africa | Burkina Faso | Burkina Faso | BF HWW I | BF HWW I | B | NO | ... | 6.64309e-05 | 6.64309e-05 | 0.000000000 | 6.64309e-05 | 0.00000e+00 | 0.00000e+00 | 0.000000000 | 0.00717454 | 3.32155e-04 | 0.3184030 |\n", + "| BH02_S77 | BH02 | 27_11_19 | West Africa | Benin | Benin | BENN HWW A | BENN HWW A | A | NO | ... | 0.00000e+00 | 0.00000e+00 | 0.000000000 | 0.00000e+00 | 9.24408e-05 | 0.00000e+00 | 0.000000000 | 0.00000000 | 0.00000e+00 | 0.4659020 |\n", + "| BH03_S78 | BH03 | 27_11_19 | West Africa | Benin | Benin | BENN HWW A | BENN HWW A | A | NO | ... | 4.19822e-04 | 0.00000e+00 | 0.000000000 | 0.00000e+00 | 1.04955e-04 | 0.00000e+00 | 0.000209911 | 0.19091400 | 0.00000e+00 | 0.1085240 |\n", + "| FH1_S162 | FH1 | 20_01_20 | Europe | Finland | Finland | FI HWW J | FI HWW J | C | NO | ... | 0.00000e+00 | 0.00000e+00 | 0.000000000 | 0.00000e+00 | 0.00000e+00 | 7.11076e-05 | 0.000000000 | 0.13467800 | 7.11076e-05 | 0.2921810 |\n", + "| FH2_S163 | FH2 | 20_01_20 | Europe | Finland | Finland | FI HWW K | FI HWW K | C | NO | ... | 8.05431e-05 | 0.00000e+00 | 0.000000000 | 0.00000e+00 | 0.00000e+00 | 0.00000e+00 | 0.000000000 | 0.01111500 | 4.02716e-04 | 0.0222299 |\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "$Class\n", + "\u001b[90m# A tibble: 6 x 180\u001b[39m\n", + " SampleID alias date continent country country2 country3 name hospital\n", + " \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \n", + "\u001b[90m1\u001b[39m BFH10_S128 BFH10 28_11_19 West Africa Burkin~ Burkina~ BF HWW F BF H~ B \n", + "\u001b[90m2\u001b[39m BFH33_S151 BFH33 12_12_19 West Africa Burkin~ Burkina~ BF HWW I BF H~ B \n", + "\u001b[90m3\u001b[39m BH02_S77 BH02 27_11_19 West Africa Benin Benin BENN HW~ BENN~ A \n", + "\u001b[90m4\u001b[39m BH03_S78 BH03 27_11_19 West Africa Benin Benin BENN HW~ BENN~ A \n", + "\u001b[90m5\u001b[39m FH1_S162 FH1 20_01_20 Europe Finland Finland FI HWW J FI H~ C \n", + "\u001b[90m6\u001b[39m FH2_S163 FH2 20_01_20 Europe Finland Finland FI HWW K FI H~ C \n", + "\u001b[90m# i 171 more variables: replicate , HWW , most_used_ab. ,\u001b[39m\n", + "\u001b[90m# description , hospital_section , hospital_description ,\u001b[39m\n", + "\u001b[90m# no_of_beds , lat , long , sample_material ,\u001b[39m\n", + "\u001b[90m# DNA_ng_.l , A260_280 , M_Seqs_trimmed , plot_name ,\u001b[39m\n", + "\u001b[90m# Taxonomic_Level , Taxonomic_Label , Candidatus_Korarchaeum ,\u001b[39m\n", + "\u001b[90m# Candidatus_Micrarchaeota_archaeon_Mia14 , Thermoprotei ,\u001b[39m\n", + "\u001b[90m# Saprospiria , Sphingobacteriia , ...\u001b[39m\n", + "\n", + "$Family\n", + "\u001b[90m# A tibble: 6 x 787\u001b[39m\n", + " SampleID alias date continent country country2 country3 name hospital\n", + " \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \n", + "\u001b[90m1\u001b[39m BFH10_S128 BFH10 28_11_19 West Africa Burkin~ Burkina~ BF HWW F BF H~ B \n", + "\u001b[90m2\u001b[39m BFH33_S151 BFH33 12_12_19 West Africa Burkin~ Burkina~ BF HWW I BF H~ B \n", + "\u001b[90m3\u001b[39m BH02_S77 BH02 27_11_19 West Africa Benin Benin BENN HW~ BENN~ A \n", + "\u001b[90m4\u001b[39m BH03_S78 BH03 27_11_19 West Africa Benin Benin BENN HW~ BENN~ A \n", + "\u001b[90m5\u001b[39m FH1_S162 FH1 20_01_20 Europe Finland Finland FI HWW J FI H~ C \n", + "\u001b[90m6\u001b[39m FH2_S163 FH2 20_01_20 Europe Finland Finland FI HWW K FI H~ C \n", + "\u001b[90m# i 778 more variables: replicate , HWW , most_used_ab. ,\u001b[39m\n", + "\u001b[90m# description , hospital_section , hospital_description ,\u001b[39m\n", + "\u001b[90m# no_of_beds , lat , long , sample_material ,\u001b[39m\n", + "\u001b[90m# DNA_ng_.l , A260_280 , M_Seqs_trimmed , plot_name ,\u001b[39m\n", + "\u001b[90m# Taxonomic_Level , Taxonomic_Label , Mycobacteriaceae ,\u001b[39m\n", + "\u001b[90m# Ichthyobacteriaceae , Candidatus_Sulcia ,\u001b[39m\n", + "\u001b[90m# Candidatus_Walczuchella , Acidilobaceae , ...\u001b[39m\n", + "\n", + "$Genus\n", + "\u001b[90m# A tibble: 6 x 1,316\u001b[39m\n", + " SampleID alias date continent country country2 country3 name hospital\n", + " \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \n", + "\u001b[90m1\u001b[39m BFH10_S128 BFH10 28_11_19 West Africa Burkin~ Burkina~ BF HWW F BF H~ B \n", + "\u001b[90m2\u001b[39m BFH33_S151 BFH33 12_12_19 West Africa Burkin~ Burkina~ BF HWW I BF H~ B \n", + "\u001b[90m3\u001b[39m BH02_S77 BH02 27_11_19 West Africa Benin Benin BENN HW~ BENN~ A \n", + "\u001b[90m4\u001b[39m BH03_S78 BH03 27_11_19 West Africa Benin Benin BENN HW~ BENN~ A \n", + "\u001b[90m5\u001b[39m FH1_S162 FH1 20_01_20 Europe Finland Finland FI HWW J FI H~ C \n", + "\u001b[90m6\u001b[39m FH2_S163 FH2 20_01_20 Europe Finland Finland FI HWW K FI H~ C \n", + "\u001b[90m# i 1,307 more variables: replicate , HWW , most_used_ab. ,\u001b[39m\n", + "\u001b[90m# description , hospital_section , hospital_description ,\u001b[39m\n", + "\u001b[90m# no_of_beds , lat , long , sample_material ,\u001b[39m\n", + "\u001b[90m# DNA_ng_.l , A260_280 , M_Seqs_trimmed , plot_name ,\u001b[39m\n", + "\u001b[90m# Taxonomic_Level , Taxonomic_Label ,\u001b[39m\n", + "\u001b[90m# Flavobacteriaceae_bacterium_MAR_2010_188 , Legionella ,\u001b[39m\n", + "\u001b[90m# Flavobacteriaceae_bacterium_UJ101 , Rhizobium , ...\u001b[39m\n", + "\n", + "$Kingdom\n", + "\u001b[90m# A tibble: 6 x 29\u001b[39m\n", + " SampleID alias date continent country country2 country3 name hospital\n", + " \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \n", + "\u001b[90m1\u001b[39m BFH10_S128 BFH10 28_11_19 West Africa Burkin~ Burkina~ BF HWW F BF H~ B \n", + "\u001b[90m2\u001b[39m BFH33_S151 BFH33 12_12_19 West Africa Burkin~ Burkina~ BF HWW I BF H~ B \n", + "\u001b[90m3\u001b[39m BH02_S77 BH02 27_11_19 West Africa Benin Benin BENN HW~ BENN~ A \n", + "\u001b[90m4\u001b[39m BH03_S78 BH03 27_11_19 West Africa Benin Benin BENN HW~ BENN~ A \n", + "\u001b[90m5\u001b[39m FH1_S162 FH1 20_01_20 Europe Finland Finland FI HWW J FI H~ C \n", + "\u001b[90m6\u001b[39m FH2_S163 FH2 20_01_20 Europe Finland Finland FI HWW K FI H~ C \n", + "\u001b[90m# i 20 more variables: replicate , HWW , most_used_ab. ,\u001b[39m\n", + "\u001b[90m# description , hospital_section , hospital_description ,\u001b[39m\n", + "\u001b[90m# no_of_beds , lat , long , sample_material ,\u001b[39m\n", + "\u001b[90m# DNA_ng_.l , A260_280 , M_Seqs_trimmed , plot_name ,\u001b[39m\n", + "\u001b[90m# Taxonomic_Level , Taxonomic_Label , Archaea ,\u001b[39m\n", + "\u001b[90m# Bacteria , Eukaryota , Viruses \u001b[39m\n", + "\n", + "$Order\n", + "\u001b[90m# A tibble: 6 x 523\u001b[39m\n", + " SampleID alias date continent country country2 country3 name hospital\n", + " \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \n", + "\u001b[90m1\u001b[39m BFH10_S128 BFH10 28_11_19 West Africa Burkin~ Burkina~ BF HWW F BF H~ B \n", + "\u001b[90m2\u001b[39m BFH33_S151 BFH33 12_12_19 West Africa Burkin~ Burkina~ BF HWW I BF H~ B \n", + "\u001b[90m3\u001b[39m BH02_S77 BH02 27_11_19 West Africa Benin Benin BENN HW~ BENN~ A \n", + "\u001b[90m4\u001b[39m BH03_S78 BH03 27_11_19 West Africa Benin Benin BENN HW~ BENN~ A \n", + "\u001b[90m5\u001b[39m FH1_S162 FH1 20_01_20 Europe Finland Finland FI HWW J FI H~ C \n", + "\u001b[90m6\u001b[39m FH2_S163 FH2 20_01_20 Europe Finland Finland FI HWW K FI H~ C \n", + "\u001b[90m# i 514 more variables: replicate , HWW , most_used_ab. ,\u001b[39m\n", + "\u001b[90m# description , hospital_section , hospital_description ,\u001b[39m\n", + "\u001b[90m# no_of_beds , lat , long , sample_material ,\u001b[39m\n", + "\u001b[90m# DNA_ng_.l , A260_280 , M_Seqs_trimmed , plot_name ,\u001b[39m\n", + "\u001b[90m# Taxonomic_Level , Taxonomic_Label ,\u001b[39m\n", + "\u001b[90m# Candidatus_Korarchaeum_cryptofilum , Acidilobales ,\u001b[39m\n", + "\u001b[90m# Saprospirales , Desulfurococcales , Methylococcales , ...\u001b[39m\n", + "\n", + "$Phylum\n", + "\u001b[90m# A tibble: 6 x 96\u001b[39m\n", + " SampleID alias date continent country country2 country3 name hospital\n", + " \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \n", + "\u001b[90m1\u001b[39m BFH10_S128 BFH10 28_11_19 West Africa Burkin~ Burkina~ BF HWW F BF H~ B \n", + "\u001b[90m2\u001b[39m BFH33_S151 BFH33 12_12_19 West Africa Burkin~ Burkina~ BF HWW I BF H~ B \n", + "\u001b[90m3\u001b[39m BH02_S77 BH02 27_11_19 West Africa Benin Benin BENN HW~ BENN~ A \n", + "\u001b[90m4\u001b[39m BH03_S78 BH03 27_11_19 West Africa Benin Benin BENN HW~ BENN~ A \n", + "\u001b[90m5\u001b[39m FH1_S162 FH1 20_01_20 Europe Finland Finland FI HWW J FI H~ C \n", + "\u001b[90m6\u001b[39m FH2_S163 FH2 20_01_20 Europe Finland Finland FI HWW K FI H~ C \n", + "\u001b[90m# i 87 more variables: replicate , HWW , most_used_ab. ,\u001b[39m\n", + "\u001b[90m# description , hospital_section , hospital_description ,\u001b[39m\n", + "\u001b[90m# no_of_beds , lat , long , sample_material ,\u001b[39m\n", + "\u001b[90m# DNA_ng_.l , A260_280 , M_Seqs_trimmed , plot_name ,\u001b[39m\n", + "\u001b[90m# Taxonomic_Level , Taxonomic_Label ,\u001b[39m\n", + "\u001b[90m# Candidatus_Korarchaeota , Candidatus_Micrarchaeota ,\u001b[39m\n", + "\u001b[90m# Crenarchaeota , Caldiserica , Euryarchaeota , ...\u001b[39m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Add taxonomic information (level and label)\n", + "metaphlan_with_metadata$Taxonomic_Level <- sapply(strsplit(as.character(metaphlan_with_metadata$ID), \"\\\\|\"), length)\n", + "metaphlan_with_metadata$Taxonomic_Label <- sapply(metaphlan_with_metadata$ID, function(x) {\n", + " labels <- c(\"Kingdom\", \"Phylum\", \"Class\", \"Order\", \"Family\", \"Genus\", \"Species\")\n", + " level <- length(strsplit(x, \"\\\\|\")[[1]])\n", + " if (level <= 7) labels[level] else \"Other\"\n", + "})\n", + "\n", + "# Extract descriptive ID and remove \"Other\" labels\n", + "metaphlan_with_metadata$ID <- sapply(metaphlan_with_metadata$ID, function(x) sub(\".*__\", \"\", tail(strsplit(x, \"\\\\|\")[[1]], 1)))\n", + "metaphlan_with_metadata <- metaphlan_with_metadata[metaphlan_with_metadata$Taxonomic_Label != \"Other\", ]\n", + "\n", + "# Split data by taxonomic label and reshape into wide format\n", + "wide_data <- metaphlan_with_metadata %>%\n", + " split(.$Taxonomic_Label) %>%\n", + " purrr::map(~ pivot_wider(.x, names_from = ID, values_from = RelativeAbundance))\n", + " \n", + "\n", + "wide_genus_data <- wide_data$Genus\n", + "wide_species_data <- wide_data$Species\n", + "wide_phylum_data <- wide_data$Phylum\n", + "\n", + "# View the reshaped wide data\n", + "head(wide_data)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Principal Component Analysis (PCA) on Genus Data\n", + "In this section, we will perform Principal Component Analysis (PCA) on the wide genus data and visualize the results. PCA is a technique used to reduce the dimensionality of data while retaining as much variability as possible. This will allow us to identify patterns in the data based on the first two principal components." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`select(c(18: ncol(wide_genus_data)))`: This selects columns starting from the 18th column to the last column in the wide_genus_data dataset. These columns represent the numerical data (i.e., the relative abundances of different genera)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "numerical <- wide_genus_data%>% select(c(18: ncol(wide_genus_data)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `clr()` function is applied to the numerical data. CLR transformation converts the relative abundances into log-transformed values, accounting for compositional data and improving the performance of PCA." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "numerical <- clr(numerical)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We perform PCA using the `rda()` function from the vegan package. The result will be a set of principal components that explain the variance in the genus data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "pca_result <- rda(numerical)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the PCA has been computed, we extract the PCA scores for the samples (also known as sites). These scores represent the projections of each sample along the principal components." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# Extract PCA scores for the samples (sites)\n", + "pca_scores <- scores(pca_result, display = \"sites\")\n", + "pca_data <- as.data.frame(pca_scores)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Add Metadata to PCA Data\n", + "We now add additional metadata, such as the hospital and SampleID, to the PCA scores data frame. This will allow us to visualize how these variables relate to the PCA results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "pca_data$hospital <- wide_genus_data$hospital\n", + "pca_data$SampleID <- wide_genus_data$SampleID" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Visualize PCA Results\n", + "\n", + "Finally, we visualize the PCA results using ggplot2. We plot the first two principal components (PC1 and PC2) and color the points by the hospital variable. We also add labels for each sample using SampleID." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "p <- ggplot(pca_data, aes(x = PC1, y = PC2, label = SampleID, group = hospital)) +\n", + " geom_point(aes(color = hospital), size = 4, alpha = 0.8) + \n", + " geom_text(size = 4) + \n", + " coord_fixed(ratio = 1) + # Ensures equal scaling for x and y axes\n", + " theme(text = element_text(family = \"Palatino\"),\n", + " legend.title = element_blank(),\n", + " legend.text = element_text(size = 16),\n", + " legend.key.size = unit(1, 'cm'),\n", + " legend.position = \"bottom\",\n", + " legend.background = element_rect(color = \"#ffffff\"),\n", + " legend.direction = \"horizontal\",\n", + " axis.text.x = element_text(size = 26, angle = 0, hjust = 0.5),\n", + " axis.text.y = element_text(size = 26),\n", + " axis.title.x = element_text(size = 26), \n", + " axis.title.y = element_text(size = 26), \n", + " plot.margin = unit(c(0.5, 0.5, 0.5, 0.5), \"cm\")) +\n", + " scale_x_continuous(expand = expansion(mult = c(0.1, 0.1))) + # Adds 10% padding\n", + " scale_y_continuous(expand = expansion(mult = c(0.1, 0.1))) # Adds 10% padding\n", + "\n", + "print(p)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1448,7 +2263,7 @@ "outputs": [ { "data": { - "image/png": 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+bMmdnZ2f4uBHXDoEGDbrzxRiGE2+3Ozs7eunXrlClTfv755+nTp5dv1qVLl44d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AUAmCHQAAgEoQ7AAAAFSCYAcAAKASBDsA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", + "image/png": 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", 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