Main_Operator.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t:: Module `pygraphviz` not available.\n",
      "\t   You don't need it unless you want to export dot files.\n",
      "\t:: No MySQL support.\n",
      "\t:: No MySQL support.\n",
      "\n",
      "\t:: Module `pysftp` not available. \n",
      "        Only downloading of a small number of resources \n",
      "        relies on this module.\n",
      "        Please install by PIP if it is necessary for you.\n",
      "    \t:: Module `fabric` not available.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/moonstrider/anaconda3/envs/iDS/lib/python3.6/site-packages/matplotlib/backends/tkagg.py:10: MatplotlibDeprecationWarning: \n",
      "The matplotlib.backends.tkagg module is deprecated.\n",
      "  \"3.0\", \"The matplotlib.backends.tkagg module is deprecated.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t:: Module `hcluster` not available.\n"
     ]
    }
   ],
   "source": [
    "from imports import *\n",
    "from utils import *\n",
    "from BackEndGame import *\n",
    "from FrontEndGame import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## We load network from the binary file - this is obviously unhandy for cross-platform compitability of our app\n",
    "## We will have to find a way to store networks in non-binary formats - either adjacency matrix + independent json\n",
    "## files with nodes properties or some existing graph format\n",
    "pa = pickle.load(open('/home/moonstrider/PycharmProjects/DrawBio-v0.1/Resources/Network_pypath_burn.by', 'br'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/moonstrider/GutHub/Plexus/BackEndGame.py:83: RuntimeWarning: Couldn't reach some vertices at structural_properties.c:740\n",
      "  self.parent.current_game_network_filt.get_shortest_paths(cuid))  ### get farthest points in current subgraph\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/moonstrider/GutHub/Plexus/BackEndGame.py:120: RuntimeWarning: Couldn't reach some vertices at structural_properties.c:740\n",
      "  self.all_paths = self.net.get_shortest_paths(ind)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "887 all unconnected partners\n",
      "New-edge: TBL1X ---> INTS11 False\n",
      "New-edge: TBL1X ---> ALG13 False\n",
      "New-edge: TBL1X ---> SKP1 Correct\n"
     ]
    }
   ],
   "source": [
    "root=tk.Tk()\n",
    "gui=Gui(root, pa)\n",
    "root.mainloop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdb92c29b70>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(gui.init_pa.graph.vs()['betwenness_z'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import optimize\n",
    "def f(x,a,b,c):\n",
    "    return a * np.exp(b * x) + c "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "coef, cov =optimize.curve_fit(f, xdata=pa.graph.vs()['betwenness_z'], ydata=pa.graph.vs()['Ref_number'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "f2 = lambda x: f(x,*coef) \n",
    "a = (np.log(np.array(pa.graph.vs()['Ref_number']))-np.log(np.array(list(map(f2, pa.graph.vs()['betwenness_z'])))))\n",
    "b = a/a.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fdb92ada4e0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(pa.graph.vs()['betwenness_z'], pa.graph.vs()['Ref_number'])\n",
    "plt.scatter(pa.graph.vs()['betwenness_z'], list(map(f2, pa.graph.vs()['betwenness_z'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fdb92ab0b70>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.DataFrame([pa.graph.vs()['Ref_number'],  pa.graph.vs().degree()], index=['Refs','Degree']).T\n",
    "sns.lmplot(x='Refs',y='Degree',data=data, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image\n",
    "from IPython.display import display\n",
    "layout = pa_filt.layout_fruchterman_reingold(repulserad = pa_filt.vcount() ** 2.8, \n",
    "    maxiter = 1000, area = pa_filt.vcount() ** 2.3)\n",
    "plot2 = igraph.plot(pa_filt, layout = layout, vertex_size = 0.5,\n",
    "    vertex_border_width = 0, edge_width = 0.2, edge_color = '#33333377', vertex_label_size = 9)\n",
    "plot2.save('Filtered_Graph_01.png')\n",
    "display(Image('Filtered_Graph_01.png'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "        Downloading data from QuickGO -- busy:  96%|█████████▌| 1.81k/1.90k [00:01<00:00, 976it/s]\n",
      "        Downloading data from QuickGO -- busy:  97%|█████████▋| 1.84k/1.90k [00:02<00:00, 957it/s]\n",
      "        Downloading data from QuickGO -- busy:  97%|█████████▋| 1.83k/1.90k [00:02<00:00, 953it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['P']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "        Downloading `goa_P.gaf.gz` from ftp.ebi.ac.uk -- failed: : 0.00it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\tERROR\n",
      "\tPycURL error: (9, 'Server denied you to change to the given directory')\n",
      "\n",
      "\tERROR\n",
      "\tPycURL error: (9, 'Server denied you to change to the given directory')\n",
      "\n",
      "\tERROR\n",
      "\tPycURL error: (9, 'Server denied you to change to the given directory')\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "'NoneType' object is not iterable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-7a2efaa458e0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_go\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'P'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;31m# get the GO BP terms for AKT1:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mpa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'AKT1'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'go'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'P'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# get the GO annotation:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mpa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgo_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/iDS/lib/python3.6/site-packages/pypath/main.py\u001b[0m in \u001b[0;36mload_go\u001b[0;34m(self, organism)\u001b[0m\n\u001b[1;32m  10960\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  10961\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m> 10962\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0morganism\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGOAnnotation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morganism\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m  10963\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  10964\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_go\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morganism\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/iDS/lib/python3.6/site-packages/pypath/go.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, organism, ontology)\u001b[0m\n\u001b[1;32m    303\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0montology\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0montology\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mGeneOntology\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    304\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 305\u001b[0;31m         \u001b[0mannot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgo_annotations_goa\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morganism\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0morganism\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    306\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mannot\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'C'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    307\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mannot\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'F'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/iDS/lib/python3.6/site-packages/pypath/dataio.py\u001b[0m in \u001b[0;36mgo_annotations_goa\u001b[0;34m(organism)\u001b[0m\n\u001b[1;32m   3629\u001b[0m     \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcurl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCurl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msilent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlarge\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3630\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3631\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3632\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3633\u001b[0m         \u001b[0mline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'ascii'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not iterable"
     ]
    }
   ],
   "source": [
    "## GO terms can be used as additional information (hints?)\n",
    "## though using them complicates everythin quite heavily\n",
    "pa.load_go(['P'])\n",
    "pa.gs('AKT1')['go']['P']\n",
    "pa.go_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = gui.STATS.stats_proteins\n",
    "res = []\n",
    "for v in pa.gss(df.index):\n",
    "    res.append(v['name'])\n",
    "len(res)==len(df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gseapy\n",
    "### We can use gsea later using perfomance score as signal\n",
    "rt = gseapy.enrichr(gene_list=res, description='pathway_analysis', gene_sets='KEGG_2016', outdir='test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pypath import gsea\n",
    "# login with an MSigDB user (Not sure my user is valid):\n",
    "g = gsea.GSEA('culpritgene@email.org', mapper = pa.mapper)\n",
    "g.show_collections()\n",
    "g.load_collection('CGP')\n",
    "#  genesets are loaded into g.sets\n",
    "enr = gsea.GSEABinaryEnrichmentSet(basic_set = pa.graph.vs['name'], gsea = res)\n",
    "enr.new_set(list(notch.up()))\n",
    "enr.top_genesets()\n",
    "enr.top_geneset_ids()\n",
    "# this way same can be done with methods of the PyPath() object:\n",
    "pa.init_gsea('culpritgene@email.org')\n",
    "pa.add_genesets(['CP:KEGG'])\n",
    "enr = pa.geneset_enrichment(list(notch.up()), alpha = 0.2)"
   ]
  }
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