enrichment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OPC/Endothelial Enrichment Analyis from RNA-Seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import urllib\n",
    "import os\n",
    "import json\n",
    "\n",
    "import pandas\n",
    "import numpy\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "url_to_path = {\n",
    "    # Brain RNA-Seq\n",
    "    'http://web.stanford.edu/group/barres_lab/barreslab_rnaseq.xlsx':\n",
    "        os.path.join('download', 'barreslab_rnaseq.xlsx'),\n",
    "}\n",
    "\n",
    "for url, path in url_to_path.items():\n",
    "    if not os.path.exists(path):\n",
    "        urllib.request.urlretrieve(url, path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read GO annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>go_id</th>\n",
       "      <th>go_name</th>\n",
       "      <th>go_domain</th>\n",
       "      <th>tax_id</th>\n",
       "      <th>annotation_type</th>\n",
       "      <th>size</th>\n",
       "      <th>gene_ids</th>\n",
       "      <th>gene_symbols</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GO:0000002</td>\n",
       "      <td>mitochondrial genome maintenance</td>\n",
       "      <td>biological_process</td>\n",
       "      <td>10090</td>\n",
       "      <td>inferred</td>\n",
       "      <td>26</td>\n",
       "      <td>11545|16882|17258|17527|18975|19819|22059|2379...</td>\n",
       "      <td>Parp1|Lig3|Mef2a|Mpv17|Polg|Rnaseh1|Trp53|Akt3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GO:0000003</td>\n",
       "      <td>reproduction</td>\n",
       "      <td>biological_process</td>\n",
       "      <td>10090</td>\n",
       "      <td>inferred</td>\n",
       "      <td>1197</td>\n",
       "      <td>11287|11421|11430|11434|11441|11477|11480|1148...</td>\n",
       "      <td>Pzp|Ace|Acox1|Acr|Chrna7|Acvr1|Acvr2a|Ada|Adam...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        go_id                           go_name           go_domain  tax_id  \\\n",
       "0  GO:0000002  mitochondrial genome maintenance  biological_process   10090   \n",
       "1  GO:0000003                      reproduction  biological_process   10090   \n",
       "\n",
       "  annotation_type  size                                           gene_ids  \\\n",
       "0        inferred    26  11545|16882|17258|17527|18975|19819|22059|2379...   \n",
       "1        inferred  1197  11287|11421|11430|11434|11441|11477|11480|1148...   \n",
       "\n",
       "                                        gene_symbols  \n",
       "0  Parp1|Lig3|Mef2a|Mpv17|Polg|Rnaseh1|Trp53|Akt3...  \n",
       "1  Pzp|Ace|Acox1|Acr|Chrna7|Acvr1|Acvr2a|Ada|Adam...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://github.com/dhimmel/gene-ontology/raw/962a5e12f8590400c2891cde93fd6a783b26e02e/annotations/taxid_10090/GO_annotations-10090-inferred-allev.tsv'\n",
    "go_df = pandas.read_table(url)\n",
    "go_df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Process expression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "path = os.path.join('download', 'barreslab_rnaseq.xlsx')\n",
    "fpkm_df = pandas.read_excel(path, sheetname='Raw Data')"
   ]
  },
  {
   "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 symbol</th>\n",
       "      <th>Description</th>\n",
       "      <th>Astrocytes</th>\n",
       "      <th>Neuron</th>\n",
       "      <th>Oligodendrocyte Precursor Cell</th>\n",
       "      <th>Newly Formed Oligodendrocyte</th>\n",
       "      <th>Myelinating Oligodendrocytes</th>\n",
       "      <th>Microglia</th>\n",
       "      <th>Endothelial Cells</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0610005C13Rik</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.150958</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0610007C21Rik</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.891260</td>\n",
       "      <td>34.185493</td>\n",
       "      <td>71.221305</td>\n",
       "      <td>57.985927</td>\n",
       "      <td>54.006096</td>\n",
       "      <td>89.145033</td>\n",
       "      <td>63.561109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gene symbol Description  Astrocytes     Neuron  \\\n",
       "0  0610005C13Rik         NaN    0.150958   0.100000   \n",
       "1  0610007C21Rik         NaN   44.891260  34.185493   \n",
       "\n",
       "   Oligodendrocyte Precursor Cell  Newly Formed Oligodendrocyte  \\\n",
       "0                        0.100000                      0.100000   \n",
       "1                       71.221305                     57.985927   \n",
       "\n",
       "   Myelinating Oligodendrocytes  Microglia  Endothelial Cells  \n",
       "0                      0.100000   0.100000           0.100000  \n",
       "1                     54.006096  89.145033          63.561109  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpkm_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cell-cell adhesion    525\n",
       "cell surface          767\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choose GO IDs to include\n",
    "go_ids = [\n",
    "    'GO:0098609', # cell-cell adhesion biological process\n",
    "    'GO:0009986', # cell surface cellular component\n",
    "]\n",
    "\n",
    "go_names = list()\n",
    "go_to_genes = dict()\n",
    "\n",
    "for go_id in go_ids:\n",
    "    go_genes = go_df.loc[go_df.go_id == go_id, 'gene_symbols'].iloc[0].split('|')\n",
    "    go_name = go_df.loc[go_df.go_id == go_id, 'go_name'].iloc[0]\n",
    "    go_names.append(go_name)\n",
    "    fpkm_df[go_name] = fpkm_df['Gene symbol'].isin(go_genes).astype(int)\n",
    "    go_to_genes[go_name] = go_genes\n",
    "\n",
    "with open('data/go-to-genes.json', 'wt') as write_file:\n",
    "    json.dump(go_to_genes, write_file, indent=2, sort_keys=True)\n",
    "\n",
    "# Genes with RNA-Seq data per GO term\n",
    "fpkm_df[go_names].sum('rows')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Calculate enrichments\n",
    "fpkm_df['opc_L2FC'] = numpy.log2(fpkm_df['Oligodendrocyte Precursor Cell'] / fpkm_df['Myelinating Oligodendrocytes'])\n",
    "\n",
    "non_endothelials = ['Astrocytes', 'Neuron', 'Oligodendrocyte Precursor Cell',\n",
    "                    'Newly Formed Oligodendrocyte', 'Myelinating Oligodendrocytes', 'Microglia']\n",
    "\n",
    "fpkm_df['endothelial_L2FC'] = numpy.log2(fpkm_df['Endothelial Cells'] / fpkm_df[non_endothelials].mean(axis='columns'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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A85IGI2I6pXq+l8afA27LbX9r6rtS/4LGxsYYHh4GYGBggNHRUVqtFjC39+D24u1Wq7Ws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\nrGmpEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f9974cb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpkm_df.opc_L2FC.hist(bins=30);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hiPiXiPhMRLy9HL8kIg5GxBMR8fGIuLhyzJ6IOBIRj0fENZXxHRHxWEQ8GRH7VxrTNLGe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\ndRnO2ZcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f98167780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpkm_df.endothelial_L2FC.hist(bins=30);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add dominant active catenin fold change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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 symbol</th>\n",
       "      <th>DA_Cat_L2FC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>Mog</td>\n",
       "      <td>-3.459580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>Ermn</td>\n",
       "      <td>-2.988046</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Gene symbol  DA_Cat_L2FC\n",
       "347         Mog    -3.459580\n",
       "205        Ermn    -2.988046"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = os.path.join('download', 'dominant-active-catenin-DEGs.tsv')\n",
    "dac_df = pandas.read_table(path, index_col=0)\n",
    "dac_df['DA_Cat_L2FC'] = numpy.log2(dac_df['Geom mean of intensities in class 1'] / dac_df['Geom mean of intensities in class 2'])\n",
    "# Convert fold changes from probe to gene space\n",
    "dac_df = dac_df[['Symbol', 'DA_Cat_L2FC']].groupby('Symbol').mean().reset_index()\n",
    "dac_df = dac_df.rename(columns={'Symbol': 'Gene symbol'})\n",
    "dac_df = dac_df.sort_values('DA_Cat_L2FC')\n",
    "dac_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f981ad630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dac_df.DA_Cat_L2FC.hist(bins=30);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fpkm_df = fpkm_df.merge(dac_df, how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fpkm_df = fpkm_df.sort_values('Gene symbol')\n",
    "path = os.path.join('data', 'enrichment.tsv')\n",
    "fpkm_df.to_csv(path, sep='\\t', index=False, float_format='%.5g')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}