Expression.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "---\n",
    "title: Expression Data\n",
    "weight: 70\n",
    "authors:\n",
    "- kellrott\n",
    "- adamstruck\n",
    "tags:\n",
    "- tcga\n",
    "- gene expression\n",
    "created_at: 2018-05-09\n",
    "updated_at: 2020-01-14\n",
    "tldr: Build a pandas matrix of expression data\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas\n",
    "import gripql\n",
    "conn = gripql.Connection(\"https://bmeg.io/api\", credential_file=\"bmeg_credentials.json\")\n",
    "G = conn.graph(\"rc5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download gene expression values from TCGA-READ cohort and build matrix with submitter id as label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO]\t2020-01-14 14:06:01,252\t177 results received in 35 seconds\n"
     ]
    }
   ],
   "source": [
    "c = G.query().V(\"Project:TCGA-READ\").out(\"cases\").out(\"samples\").as_(\"sample\")\n",
    "c = c.out(\"aliquots\").out(\"gene_expressions\").as_(\"exp\")\n",
    "c = c.render( [\"$sample._data.gdc_attributes.submitter_id\", \"$exp._data.values\"])\n",
    "\n",
    "data = {}\n",
    "for row in c.execute(stream=True):\n",
    "    data[row[0]] = row[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take the data we downloaded and turn it into a Pandas data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples = pandas.DataFrame(data).transpose().fillna(0.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take a look at the top corner of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ENSG00000000003</th>\n",
       "      <th>ENSG00000000005</th>\n",
       "      <th>ENSG00000000419</th>\n",
       "      <th>ENSG00000000457</th>\n",
       "      <th>ENSG00000000460</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>TCGA-G5-6233-01A</td>\n",
       "      <td>30.212803</td>\n",
       "      <td>0.063489</td>\n",
       "      <td>87.317567</td>\n",
       "      <td>6.237288</td>\n",
       "      <td>5.518520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TCGA-AG-4021-01A</td>\n",
       "      <td>80.356975</td>\n",
       "      <td>3.621759</td>\n",
       "      <td>48.649980</td>\n",
       "      <td>8.770929</td>\n",
       "      <td>8.975365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TCGA-EI-6514-01A</td>\n",
       "      <td>142.160017</td>\n",
       "      <td>2.460405</td>\n",
       "      <td>82.308962</td>\n",
       "      <td>4.144567</td>\n",
       "      <td>3.251904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TCGA-AG-3725-01A</td>\n",
       "      <td>81.611032</td>\n",
       "      <td>4.730710</td>\n",
       "      <td>56.783610</td>\n",
       "      <td>4.896992</td>\n",
       "      <td>4.208633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TCGA-AG-3725-11A</td>\n",
       "      <td>78.390851</td>\n",
       "      <td>1.037580</td>\n",
       "      <td>51.193885</td>\n",
       "      <td>6.455982</td>\n",
       "      <td>2.212233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ENSG00000000003  ENSG00000000005  ENSG00000000419  \\\n",
       "TCGA-G5-6233-01A        30.212803         0.063489        87.317567   \n",
       "TCGA-AG-4021-01A        80.356975         3.621759        48.649980   \n",
       "TCGA-EI-6514-01A       142.160017         2.460405        82.308962   \n",
       "TCGA-AG-3725-01A        81.611032         4.730710        56.783610   \n",
       "TCGA-AG-3725-11A        78.390851         1.037580        51.193885   \n",
       "\n",
       "                  ENSG00000000457  ENSG00000000460  \n",
       "TCGA-G5-6233-01A         6.237288         5.518520  \n",
       "TCGA-AG-4021-01A         8.770929         8.975365  \n",
       "TCGA-EI-6514-01A         4.144567         3.251904  \n",
       "TCGA-AG-3725-01A         4.896992         4.208633  \n",
       "TCGA-AG-3725-11A         6.455982         2.212233  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples.iloc[:5,:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take a quick look to see the top expressing samples for the gene `ENSG00000000003`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TCGA-DC-5869-01A    281.005900\n",
       "TCGA-DC-6683-01A    258.010380\n",
       "TCGA-AF-3913-01A    254.599210\n",
       "TCGA-EF-5831-01A    253.774315\n",
       "TCGA-DC-6157-01A    251.170374\n",
       "Name: ENSG00000000003, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples[\"ENSG00000000003\"].sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11ceef450>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(samples['ENSG00000000003'], color=\"g\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}