L1000_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sys,  datetime, os\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import shuffle\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Y matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13072, 978)\n"
     ]
    }
   ],
   "source": [
    "Y_raw = pd.read_table('Input/consensus-perts.tsv')\n",
    "Y_raw.set_index('pert_id', inplace=True)\n",
    "print(Y_raw.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>10007</th>\n",
       "      <th>1001</th>\n",
       "      <th>10013</th>\n",
       "      <th>10038</th>\n",
       "      <th>10046</th>\n",
       "      <th>10049</th>\n",
       "      <th>10051</th>\n",
       "      <th>10057</th>\n",
       "      <th>10058</th>\n",
       "      <th>10059</th>\n",
       "      <th>...</th>\n",
       "      <th>9918</th>\n",
       "      <th>9924</th>\n",
       "      <th>9926</th>\n",
       "      <th>9928</th>\n",
       "      <th>993</th>\n",
       "      <th>994</th>\n",
       "      <th>9943</th>\n",
       "      <th>9961</th>\n",
       "      <th>998</th>\n",
       "      <th>9988</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pert_id</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",
       "      <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>BRD-A00100033</th>\n",
       "      <td>-0.065</td>\n",
       "      <td>3.696</td>\n",
       "      <td>-1.701</td>\n",
       "      <td>-0.156</td>\n",
       "      <td>-0.632</td>\n",
       "      <td>0.215</td>\n",
       "      <td>-0.056</td>\n",
       "      <td>-0.741</td>\n",
       "      <td>-1.280</td>\n",
       "      <td>-0.242</td>\n",
       "      <td>...</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.690</td>\n",
       "      <td>-0.256</td>\n",
       "      <td>-0.290</td>\n",
       "      <td>-0.092</td>\n",
       "      <td>-0.878</td>\n",
       "      <td>-0.949</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.070</td>\n",
       "      <td>3.351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRD-A00150179</th>\n",
       "      <td>0.179</td>\n",
       "      <td>1.264</td>\n",
       "      <td>1.399</td>\n",
       "      <td>0.492</td>\n",
       "      <td>-0.337</td>\n",
       "      <td>-0.907</td>\n",
       "      <td>0.441</td>\n",
       "      <td>-0.462</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.815</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.281</td>\n",
       "      <td>1.165</td>\n",
       "      <td>0.860</td>\n",
       "      <td>-0.505</td>\n",
       "      <td>1.617</td>\n",
       "      <td>1.024</td>\n",
       "      <td>0.230</td>\n",
       "      <td>-0.483</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRD-A00267231</th>\n",
       "      <td>-2.384</td>\n",
       "      <td>2.685</td>\n",
       "      <td>0.740</td>\n",
       "      <td>-1.937</td>\n",
       "      <td>-1.680</td>\n",
       "      <td>1.422</td>\n",
       "      <td>-1.537</td>\n",
       "      <td>-0.623</td>\n",
       "      <td>-1.370</td>\n",
       "      <td>1.558</td>\n",
       "      <td>...</td>\n",
       "      <td>0.064</td>\n",
       "      <td>-0.446</td>\n",
       "      <td>-1.440</td>\n",
       "      <td>-2.777</td>\n",
       "      <td>-0.227</td>\n",
       "      <td>-0.677</td>\n",
       "      <td>-0.113</td>\n",
       "      <td>-0.083</td>\n",
       "      <td>-1.780</td>\n",
       "      <td>-0.524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRD-A00420644</th>\n",
       "      <td>2.187</td>\n",
       "      <td>0.203</td>\n",
       "      <td>-4.654</td>\n",
       "      <td>-2.816</td>\n",
       "      <td>2.550</td>\n",
       "      <td>-0.308</td>\n",
       "      <td>-4.254</td>\n",
       "      <td>3.888</td>\n",
       "      <td>-1.853</td>\n",
       "      <td>0.478</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.109</td>\n",
       "      <td>-1.723</td>\n",
       "      <td>0.087</td>\n",
       "      <td>-3.514</td>\n",
       "      <td>-0.055</td>\n",
       "      <td>-2.989</td>\n",
       "      <td>1.203</td>\n",
       "      <td>-2.633</td>\n",
       "      <td>-0.106</td>\n",
       "      <td>2.473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRD-A00474148</th>\n",
       "      <td>-0.704</td>\n",
       "      <td>3.315</td>\n",
       "      <td>-0.463</td>\n",
       "      <td>0.713</td>\n",
       "      <td>1.250</td>\n",
       "      <td>0.956</td>\n",
       "      <td>0.030</td>\n",
       "      <td>-0.355</td>\n",
       "      <td>-0.492</td>\n",
       "      <td>-2.037</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.010</td>\n",
       "      <td>-0.195</td>\n",
       "      <td>-0.698</td>\n",
       "      <td>-1.104</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.185</td>\n",
       "      <td>-0.499</td>\n",
       "      <td>-1.028</td>\n",
       "      <td>0.513</td>\n",
       "      <td>-0.212</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 978 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               10007   1001  10013  10038  10046  10049  10051  10057  10058  \\\n",
       "pert_id                                                                        \n",
       "BRD-A00100033 -0.065  3.696 -1.701 -0.156 -0.632  0.215 -0.056 -0.741 -1.280   \n",
       "BRD-A00150179  0.179  1.264  1.399  0.492 -0.337 -0.907  0.441 -0.462  0.011   \n",
       "BRD-A00267231 -2.384  2.685  0.740 -1.937 -1.680  1.422 -1.537 -0.623 -1.370   \n",
       "BRD-A00420644  2.187  0.203 -4.654 -2.816  2.550 -0.308 -4.254  3.888 -1.853   \n",
       "BRD-A00474148 -0.704  3.315 -0.463  0.713  1.250  0.956  0.030 -0.355 -0.492   \n",
       "\n",
       "               10059  ...     9918   9924   9926   9928    993    994   9943  \\\n",
       "pert_id               ...                                                      \n",
       "BRD-A00100033 -0.242  ...    0.082  0.690 -0.256 -0.290 -0.092 -0.878 -0.949   \n",
       "BRD-A00150179  0.815  ...   -1.281  1.165  0.860 -0.505  1.617  1.024  0.230   \n",
       "BRD-A00267231  1.558  ...    0.064 -0.446 -1.440 -2.777 -0.227 -0.677 -0.113   \n",
       "BRD-A00420644  0.478  ...   -2.109 -1.723  0.087 -3.514 -0.055 -2.989  1.203   \n",
       "BRD-A00474148 -2.037  ...   -1.010 -0.195 -0.698 -1.104  0.088  0.185 -0.499   \n",
       "\n",
       "                9961    998   9988  \n",
       "pert_id                             \n",
       "BRD-A00100033  0.066  0.070  3.351  \n",
       "BRD-A00150179 -0.483  0.880  0.200  \n",
       "BRD-A00267231 -0.083 -1.780 -0.524  \n",
       "BRD-A00420644 -2.633 -0.106  2.473  \n",
       "BRD-A00474148 -1.028  0.513 -0.212  \n",
       "\n",
       "[5 rows x 978 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check distribution of scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.065"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array = []\n",
    "for i in list(Y_raw.columns):\n",
    "    for e in list(Y_raw.index):\n",
    "        array.append(Y_raw.loc[e,i])\n",
    "array[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a531af898>"
      ]
     },
     "execution_count": 34,
     "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.distplot(array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create classifier matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if |score| is greater than 2.33, 0.01 statisically significant to change and new df created with this classification\n",
    "def changer(x):\n",
    "    if x >= 2.33:\n",
    "        return 1.0\n",
    "    elif x <= -2.33:\n",
    "        return -1.0\n",
    "    else:\n",
    "        return 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = pd.DataFrame(0, index = Y_raw.index, columns = Y_raw.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>10007</th>\n",
       "      <th>1001</th>\n",
       "      <th>10013</th>\n",
       "      <th>10038</th>\n",
       "      <th>10046</th>\n",
       "      <th>10049</th>\n",
       "      <th>10051</th>\n",
       "      <th>10057</th>\n",
       "      <th>10058</th>\n",
       "      <th>10059</th>\n",
       "      <th>...</th>\n",
       "      <th>9918</th>\n",
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       "      <th>9926</th>\n",
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       "      <th>9961</th>\n",
       "      <th>998</th>\n",
       "      <th>9988</th>\n",
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       "    <tr>\n",
       "      <th>pert_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BRD-A00100033</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRD-A00267231</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>-1.0</td>\n",
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       "    <tr>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 978 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               10007  1001  10013  10038  10046  10049  10051  10057  10058  \\\n",
       "pert_id                                                                       \n",
       "BRD-A00100033    0.0   1.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "BRD-A00150179    0.0   0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "BRD-A00267231   -1.0   1.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "BRD-A00420644    0.0   0.0   -1.0   -1.0    1.0    0.0   -1.0    1.0    0.0   \n",
       "BRD-A00474148    0.0   1.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "               10059  ...   9918  9924  9926  9928  993  994  9943  9961  998  \\\n",
       "pert_id               ...                                                       \n",
       "BRD-A00100033    0.0  ...    0.0   0.0   0.0   0.0  0.0  0.0   0.0   0.0  0.0   \n",
       "BRD-A00150179    0.0  ...    0.0   0.0   0.0   0.0  0.0  0.0   0.0   0.0  0.0   \n",
       "BRD-A00267231    0.0  ...    0.0   0.0   0.0  -1.0  0.0  0.0   0.0   0.0  0.0   \n",
       "BRD-A00420644    0.0  ...    0.0   0.0   0.0  -1.0  0.0 -1.0   0.0  -1.0  0.0   \n",
       "BRD-A00474148    0.0  ...    0.0   0.0   0.0   0.0  0.0  0.0   0.0   0.0  0.0   \n",
       "\n",
       "               9988  \n",
       "pert_id              \n",
       "BRD-A00100033   1.0  \n",
       "BRD-A00150179   0.0  \n",
       "BRD-A00267231   0.0  \n",
       "BRD-A00420644   1.0  \n",
       "BRD-A00474148   0.0  \n",
       "\n",
       "[5 rows x 978 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for pert in Y_raw.index:\n",
    "    for sig in Y_raw.columns:\n",
    "        Y.loc[pert, sig] = changer(Y_raw.loc[pert,sig])\n",
    "Y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save classification matrix\n",
    "filename = 'Output/L1000_ClassificationMat_%s.tsv.zip'% str(datetime.date.today())[0:7].replace('-', '_')\n",
    "Y.to_csv(filename, sep='\\t', compression='gzip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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