compile-PPIs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Download several PPI resources and combine them into a single tsv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dhimmels/anaconda3/lib/python3.5/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    }
   ],
   "source": [
    "import zipfile\n",
    "import io\n",
    "import itertools\n",
    "\n",
    "import pandas\n",
    "import numpy\n",
    "import seaborn\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def order_genes_in_df(df):\n",
    "    \"\"\"Ensure that gene_0 is less than gene_1 and drop duplicates.\"\"\"\n",
    "    gene_0 = numpy.minimum(df.gene_0, df.gene_1)\n",
    "    gene_1 = numpy.maximum(df.gene_0, df.gene_1)\n",
    "    df.gene_0, df.gene_1 = gene_0, gene_1\n",
    "    df = df.drop_duplicates()\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Download sources\n",
    "# ! wget --timestamping --directory-prefix download/hid/ http://interactome.dfci.harvard.edu/H_sapiens/download/HI-II-14.tsv\n",
    "# ! wget --timestamping --directory-prefix download/hid/ http://interactome.dfci.harvard.edu/H_sapiens/download/HI-I-05.tsv\n",
    "# ! wget --timestamping --directory-prefix download/hid/ http://interactome.dfci.harvard.edu/H_sapiens/download/Lit-BM-13.tsv\n",
    "# ! wget --timestamping --directory-prefix download/hid/ http://interactome.dfci.harvard.edu/H_sapiens/download/Venkatesan-09.tsv\n",
    "# ! wget --timestamping --directory-prefix download/hid/ http://interactome.dfci.harvard.edu/H_sapiens/download/Yu-11.tsv\n",
    "# ! wget --timestamping --directory-prefix download/ii/ http://www.sciencemag.org/content/suppl/2015/02/18/347.6224.1257601.DC1/Datasets_S1-S4.zip\n",
    "# ! wget --timestamping --directory-prefix download/hetio/ http://het.io/disease-genes/downloads/files/protein-interactions.txt.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "columns = ['gene_0', 'gene_1', 'source']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Incomplete Interactome\n",
    "\n",
    "https://dx.doi.org/10.1126/science.1257601"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_0</th>\n",
       "      <th>gene_1</th>\n",
       "      <th>sources</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>141294</th>\n",
       "      <td>204851</td>\n",
       "      <td>2275</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141295</th>\n",
       "      <td>116173</td>\n",
       "      <td>8803</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        gene_0  gene_1 sources\n",
       "141294  204851    2275  binary\n",
       "141295  116173    8803  binary"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read dataset inside of zipfile\n",
    "with zipfile.ZipFile('download/ii/Datasets_S1-S4.zip') as zip_file:\n",
    "    with zip_file.open('data/DataS1_interactome.tsv', 'r') as read_file:\n",
    "        ii_df = pandas.read_table(read_file, comment='#', names=['gene_0', 'gene_1', 'sources'])\n",
    "\n",
    "ii_df.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_0</th>\n",
       "      <th>gene_1</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>193708</th>\n",
       "      <td>2275</td>\n",
       "      <td>204851</td>\n",
       "      <td>II_binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193709</th>\n",
       "      <td>8803</td>\n",
       "      <td>116173</td>\n",
       "      <td>II_binary</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        gene_0  gene_1     source\n",
       "193708    2275  204851  II_binary\n",
       "193709    8803  116173  II_binary"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# process so one PPI report per row\n",
    "rows = list()\n",
    "\n",
    "for i, (gene_0, gene_1, sources) in ii_df.iterrows():\n",
    "    sources = sources.split(';')\n",
    "    for source in sources:\n",
    "        row = gene_0, gene_1, 'II_{}'.format(source)\n",
    "        rows.append(row)\n",
    "\n",
    "ii_df = pandas.DataFrame(rows, columns=columns)\n",
    "ii_df = order_genes_in_df(ii_df)\n",
    "ii_df.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Human Interactome Database\n",
    "\n",
    "[Documentation](http://interactome.dfci.harvard.edu/H_sapiens/index.php?page=home) and [downloads](http://interactome.dfci.harvard.edu/H_sapiens/index.php?page=download)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2754"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HI-I-05\n",
    "raul_df = pandas.read_table('download/hid/HI-I-05.tsv')\n",
    "raul_df = raul_df[['Entrez Gene IDA', 'Entrez Gene IDB']]\n",
    "raul_df['source'] = 'HI-I-05'\n",
    "raul_df.columns = columns\n",
    "raul_df = order_genes_in_df(raul_df)\n",
    "len(raul_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "239"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Venkatesan-09\n",
    "venk_df = pandas.read_table('download/hid/Venkatesan-09.tsv')\n",
    "venk_df = venk_df[['DB Entrez Gene IDA', 'AD Entrez Gene ID']]\n",
    "venk_df['source'] = 'Venkatesan-09'\n",
    "venk_df.columns = columns\n",
    "venk_df = order_genes_in_df(venk_df)\n",
    "len(venk_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1167"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Yu-11\n",
    "yu_df = pandas.read_table('download/hid/Yu-11.tsv')\n",
    "yu_df = yu_df[['Entrez Gene IDA', 'Entrez Gene IDB']]\n",
    "yu_df['source'] = 'Yu-11'\n",
    "yu_df.columns = columns\n",
    "yu_df = order_genes_in_df(yu_df)\n",
    "len(yu_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13944"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HI-II-14\n",
    "rolland_df = pandas.read_table('download/hid/HI-II-14.tsv')\n",
    "rolland_df = rolland_df[['Entrez Gene IDA', 'Entrez Gene IDB']]\n",
    "rolland_df['source'] = 'HI-II-14'\n",
    "rolland_df.columns = columns\n",
    "rolland_df = order_genes_in_df(rolland_df)\n",
    "len(rolland_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11045"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lit-BM-13\n",
    "rolland_lit_df = pandas.read_table('download/hid/Lit-BM-13.tsv')\n",
    "rolland_lit_df = rolland_lit_df[['entrez_gene_ida', 'entrez_gene_idb']]\n",
    "rolland_lit_df['source'] = 'Lit-BM-13'\n",
    "rolland_lit_df.columns = columns\n",
    "rolland_lit_df = order_genes_in_df(rolland_lit_df)\n",
    "len(rolland_lit_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hetio-dag\n",
    "\n",
    "http://het.io/disease-genes/downloads/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Entrez Gene mapping\n",
    "url = 'https://raw.githubusercontent.com/dhimmel/entrez-gene/6e133f9ef8ce51a4c5387e58a6cc97564a66cec8/data/xrefs-human.tsv'\n",
    "entrez_df = pandas.read_table(url)\n",
    "entrez_df = entrez_df[entrez_df.resource == 'HGNC'].rename(columns={'identifier': 'hgnc_id'})[['GeneID', 'hgnc_id']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "discard = {\n",
    "    'MI:0190(interaction type)',\n",
    "    'MI:0208(genetic interaction)',\n",
    "    'MI:0403(colocalization)',\n",
    "    'MI:0914(association)',\n",
    "    'MI:1110(predicted interaction)',\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98187"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hetio_df = pandas.read_table('download/hetio/protein-interactions.txt.gz', compression='gzip')\n",
    "len(hetio_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "96366"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hetio_df = hetio_df[hetio_df.interaction_type.map(lambda x: bool(set(x.split('|')) - discard))]\n",
    "len(hetio_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_0</th>\n",
       "      <th>gene_1</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>310</td>\n",
       "      <td>hetio-dag</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>310</td>\n",
       "      <td>hetio-dag</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gene_0  gene_1     source\n",
       "0       1     310  hetio-dag\n",
       "1       2     310  hetio-dag"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hetio_df = hetio_df.merge(\n",
    "    entrez_df.rename(columns={'GeneID': 'gene_0', 'hgnc_id': 'source_code'})).merge(\n",
    "    entrez_df.rename(columns={'GeneID': 'gene_1', 'hgnc_id': 'target_code'}))\n",
    "hetio_df = hetio_df[['gene_0', 'gene_1']]\n",
    "hetio_df['source'] = 'hetio-dag'\n",
    "hetio_df = order_genes_in_df(hetio_df)\n",
    "hetio_df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "319158"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df = pandas.concat([raul_df, venk_df, yu_df, rolland_df, rolland_lit_df, ii_df, hetio_df])\n",
    "combined_df = combined_df.sort_values(list(combined_df.columns))\n",
    "len(combined_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "source_to_interactions = dict()\n",
    "for i, (gene_0, gene_1, source) in combined_df.iterrows():\n",
    "    interactions = source_to_interactions.setdefault(source, set())\n",
    "    interactions.add(frozenset((gene_0, gene_1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'HI-I-05': 2754,\n",
       " 'HI-II-14': 13944,\n",
       " 'II_binary': 28653,\n",
       " 'II_complexes': 31276,\n",
       " 'II_kinase': 6066,\n",
       " 'II_literature': 88349,\n",
       " 'II_metabolic': 5325,\n",
       " 'II_regulatory': 1335,\n",
       " 'II_signaling': 32706,\n",
       " 'Lit-BM-13': 11045,\n",
       " 'Venkatesan-09': 239,\n",
       " 'Yu-11': 1167,\n",
       " 'hetio-dag': 96299}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Interactions per resource\n",
    "{k: len(v) for k, v in source_to_interactions.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HI-I-05</th>\n",
       "      <th>HI-II-14</th>\n",
       "      <th>II_binary</th>\n",
       "      <th>II_complexes</th>\n",
       "      <th>II_kinase</th>\n",
       "      <th>II_literature</th>\n",
       "      <th>II_metabolic</th>\n",
       "      <th>II_regulatory</th>\n",
       "      <th>II_signaling</th>\n",
       "      <th>Lit-BM-13</th>\n",
       "      <th>Venkatesan-09</th>\n",
       "      <th>Yu-11</th>\n",
       "      <th>hetio-dag</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_0</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HI-I-05</th>\n",
       "      <td>100.00</td>\n",
       "      <td>26.87</td>\n",
       "      <td>98.22</td>\n",
       "      <td>1.74</td>\n",
       "      <td>0.15</td>\n",
       "      <td>98.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>75.67</td>\n",
       "      <td>5.81</td>\n",
       "      <td>4.94</td>\n",
       "      <td>4.54</td>\n",
       "      <td>92.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HI-II-14</th>\n",
       "      <td>5.31</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.09</td>\n",
       "      <td>9.19</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.79</td>\n",
       "      <td>2.94</td>\n",
       "      <td>0.47</td>\n",
       "      <td>4.77</td>\n",
       "      <td>9.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>II_binary</th>\n",
       "      <td>9.44</td>\n",
       "      <td>48.67</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.69</td>\n",
       "      <td>49.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.04</td>\n",
       "      <td>27.42</td>\n",
       "      <td>9.96</td>\n",
       "      <td>0.83</td>\n",
       "      <td>4.07</td>\n",
       "      <td>47.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>II_complexes</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.38</td>\n",
       "      <td>1.56</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.24</td>\n",
       "      <td>23.52</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.01</td>\n",
       "      <td>4.68</td>\n",
       "      <td>2.90</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.07</td>\n",
       "      <td>9.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>II_kinase</th>\n",
       "      <td>0.07</td>\n",
       "      <td>0.20</td>\n",
       "      <td>3.25</td>\n",
       "      <td>1.24</td>\n",
       "      <td>100.00</td>\n",
       "      <td>32.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>24.81</td>\n",
       "      <td>4.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>31.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>II_literature</th>\n",
       "      <td>3.06</td>\n",
       "      <td>1.45</td>\n",
       "      <td>15.89</td>\n",
       "      <td>8.32</td>\n",
       "      <td>2.20</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.07</td>\n",
       "      <td>32.55</td>\n",
       "      <td>9.78</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.22</td>\n",
       "      <td>53.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>II_metabolic</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.66</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>II_regulatory</th>\n",
       "      <td>0.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>4.79</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>3.37</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>II_signaling</th>\n",
       "      <td>6.37</td>\n",
       "      <td>2.04</td>\n",
       "      <td>24.02</td>\n",
       "      <td>4.48</td>\n",
       "      <td>4.60</td>\n",
       "      <td>87.94</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.14</td>\n",
       "      <td>100.00</td>\n",
       "      <td>15.51</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.31</td>\n",
       "      <td>88.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lit-BM-13</th>\n",
       "      <td>1.45</td>\n",
       "      <td>3.71</td>\n",
       "      <td>25.83</td>\n",
       "      <td>8.20</td>\n",
       "      <td>2.41</td>\n",
       "      <td>78.21</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.14</td>\n",
       "      <td>45.93</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.57</td>\n",
       "      <td>85.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venkatesan-09</th>\n",
       "      <td>56.90</td>\n",
       "      <td>27.20</td>\n",
       "      <td>99.58</td>\n",
       "      <td>1.26</td>\n",
       "      <td>0.00</td>\n",
       "      <td>96.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>41.84</td>\n",
       "      <td>2.51</td>\n",
       "      <td>100.00</td>\n",
       "      <td>5.44</td>\n",
       "      <td>96.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yu-11</th>\n",
       "      <td>10.71</td>\n",
       "      <td>56.98</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1.89</td>\n",
       "      <td>0.09</td>\n",
       "      <td>16.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.74</td>\n",
       "      <td>5.40</td>\n",
       "      <td>1.11</td>\n",
       "      <td>100.00</td>\n",
       "      <td>45.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hetio-dag</th>\n",
       "      <td>2.64</td>\n",
       "      <td>1.36</td>\n",
       "      <td>14.05</td>\n",
       "      <td>2.97</td>\n",
       "      <td>1.96</td>\n",
       "      <td>49.33</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.14</td>\n",
       "      <td>30.14</td>\n",
       "      <td>9.84</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.55</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               HI-I-05  HI-II-14  II_binary  II_complexes  II_kinase  \\\n",
       "source_0                                                               \n",
       "HI-I-05         100.00     26.87      98.22          1.74       0.15   \n",
       "HI-II-14          5.31    100.00     100.00          0.85       0.09   \n",
       "II_binary         9.44     48.67     100.00          1.70       0.69   \n",
       "II_complexes      0.15      0.38       1.56        100.00       0.24   \n",
       "II_kinase         0.07      0.20       3.25          1.24     100.00   \n",
       "II_literature     3.06      1.45      15.89          8.32       2.20   \n",
       "II_metabolic      0.00      0.04       0.08          0.32       0.00   \n",
       "II_regulatory     0.07      0.00       0.82          0.15       0.15   \n",
       "II_signaling      6.37      2.04      24.02          4.48       4.60   \n",
       "Lit-BM-13         1.45      3.71      25.83          8.20       2.41   \n",
       "Venkatesan-09    56.90     27.20      99.58          1.26       0.00   \n",
       "Yu-11            10.71     56.98     100.00          1.89       0.09   \n",
       "hetio-dag         2.64      1.36      14.05          2.97       1.96   \n",
       "\n",
       "               II_literature  II_metabolic  II_regulatory  II_signaling  \\\n",
       "source_0                                                                  \n",
       "HI-I-05                98.15          0.00           0.04         75.67   \n",
       "HI-II-14                9.19          0.01           0.00          4.79   \n",
       "II_binary              49.01          0.01           0.04         27.42   \n",
       "II_complexes           23.52          0.05           0.01          4.68   \n",
       "II_kinase              32.00          0.00           0.03         24.81   \n",
       "II_literature         100.00          0.04           0.07         32.55   \n",
       "II_metabolic            0.66        100.00           0.00          0.24   \n",
       "II_regulatory           4.79          0.00         100.00          3.37   \n",
       "II_signaling           87.94          0.04           0.14        100.00   \n",
       "Lit-BM-13              78.21          0.04           0.14         45.93   \n",
       "Venkatesan-09          96.65          0.00           0.00         41.84   \n",
       "Yu-11                  16.28          0.00           0.00          8.74   \n",
       "hetio-dag              49.33          0.04           0.14         30.14   \n",
       "\n",
       "               Lit-BM-13  Venkatesan-09   Yu-11  hetio-dag  \n",
       "source_0                                                    \n",
       "HI-I-05             5.81           4.94    4.54      92.16  \n",
       "HI-II-14            2.94           0.47    4.77       9.41  \n",
       "II_binary           9.96           0.83    4.07      47.21  \n",
       "II_complexes        2.90           0.01    0.07       9.13  \n",
       "II_kinase           4.39           0.00    0.02      31.09  \n",
       "II_literature       9.78           0.26    0.22      53.77  \n",
       "II_metabolic        0.08           0.00    0.00       0.68  \n",
       "II_regulatory       1.20           0.00    0.00       9.89  \n",
       "II_signaling       15.51           0.31    0.31      88.74  \n",
       "Lit-BM-13         100.00           0.05    0.57      85.82  \n",
       "Venkatesan-09       2.51         100.00    5.44      96.65  \n",
       "Yu-11               5.40           1.11  100.00      45.42  \n",
       "hetio-dag           9.84           0.24    0.55     100.00  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What percent of the interactions from the row source are in the interactions from the column source\n",
    "rows = []\n",
    "for source_0, source_1 in itertools.product(source_to_interactions, repeat=2):\n",
    "    source_0_genes = source_to_interactions[source_0]\n",
    "    intersect = source_0_genes & source_to_interactions[source_1]\n",
    "    rows.append((source_0, source_1, len(intersect) / len(source_0_genes)))\n",
    "\n",
    "overlap_df = pandas.DataFrame(rows, columns=['source_0', 'source_1', 'coverage'])\n",
    "overlap_df = overlap_df.pivot('source_0', 'source_1', 'coverage')\n",
    "numpy.round(overlap_df * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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A+Wy/mfdfEbgLWKevsheSZgP+AOxk+8+SRgGnACJdhLu77YfKvvMQQqig8/um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\nHSc6pxBCCB3n/wP9COTwo1bgmgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17f6929898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = seaborn.heatmap(overlap_df * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "combined_df.to_csv('data/ppi-sources.tsv', index=False, sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `hetio-ind` subset of interactions\n",
    "\n",
    "http://dx.doi.org/10.15363/thinklab.d85"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Sources to include PPIs from\n",
    "sources = {'HI-I-05', 'Venkatesan-09', 'Yu-11', 'HI-II-14', 'Lit-BM-13', 'II_binary', 'II_literature', 'hetio-dag'}\n",
    "# Sources that qualify a PPI as unbiased\n",
    "unbiased = {'HI-I-05', 'Venkatesan-09', 'Yu-11', 'HI-II-14'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_0</th>\n",
       "      <th>gene_1</th>\n",
       "      <th>sources</th>\n",
       "      <th>unbiased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>310</td>\n",
       "      <td>hetio-dag</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1026</td>\n",
       "      <td>hetio-dag</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gene_0  gene_1    sources  unbiased\n",
       "0       1     310  hetio-dag         0\n",
       "1       1    1026  hetio-dag         0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Condense to gene pairs\n",
    "hetio_ind_df = combined_df[combined_df.source.isin(sources)]\n",
    "hetio_ind_df = hetio_ind_df.groupby(['gene_0', 'gene_1']).apply(\n",
    "    lambda df: pandas.Series({\n",
    "            'sources': '|'.join(sorted(set(df.source))),\n",
    "            'unbiased': int(bool(set(df.source) & unbiased))\n",
    "        })).reset_index()\n",
    "hetio_ind_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "151729"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(hetio_ind_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    135203\n",
       "1     16526\n",
       "Name: unbiased, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hetio_ind_df.unbiased.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hetio_ind_df.to_csv('data/ppi-hetio-ind.tsv', index=False, sep='\\t')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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