{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Benchmarking the Recovery of Known Drug Targets from L1000 CRISPR KO Data: NR0B1 Version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.graph_objects as go\n",
    "import requests\n",
    "import urllib.request\n",
    "import json\n",
    "import time\n",
    "import scipy.stats as ss\n",
    "from maayanlab_bioinformatics.normalization.filter import filter_by_expr\n",
    "from maayanlab_bioinformatics.dge.characteristic_direction import characteristic_direction\n",
    "from maayanlab_bioinformatics.dge.limma_voom import limma_voom_differential_expression\n",
    "from maayanlab_bioinformatics.enrichment import enrich_crisp\n",
    "from math import log2\n",
    "from IPython.display import display, Markdown\n",
    "from random import sample\n",
    "from maayanlab_bioinformatics.plotting.bridge import bridge_plot\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import pairwise_distances\n",
    "import seaborn as sns\n",
    "from os.path import exists\n",
    "from scipy.stats import ttest_ind, ranksums\n",
    "import h5py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load in Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set KO gene\n",
    "ko_gene = 'NR0B1'\n",
    "\n",
    "# Set working directory\n",
    "l1000_data_dir = '../L1000_data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>XPR032_A375.311_96H_X1_B38:N14</th>\n",
       "      <th>XPR032_A375.311_96H_X2_B38:N14</th>\n",
       "      <th>XPR032_A375.311_96H_X3_B38:N14</th>\n",
       "      <th>XPR032_A375.311_96H_X1_B38:P20</th>\n",
       "      <th>XPR032_A375.311_96H_X3_B38:P20</th>\n",
       "      <th>XPR032_A549.311_96H_X1_B38:N14</th>\n",
       "      <th>XPR032_A549.311_96H_X2_B38:N14</th>\n",
       "      <th>XPR032_A549.311_96H_X3_B38:N14</th>\n",
       "      <th>XPR032_A549.311_96H_X1_B38:P20</th>\n",
       "      <th>XPR032_A549.311_96H_X2_B38:P20</th>\n",
       "      <th>...</th>\n",
       "      <th>XPR032_U251MG.311_96H_X2_B38:N14</th>\n",
       "      <th>XPR032_U251MG.311_96H_X3_B38:N14</th>\n",
       "      <th>XPR032_U251MG.311_96H_X1_B38:P20</th>\n",
       "      <th>XPR032_U251MG.311_96H_X2_B38:P20</th>\n",
       "      <th>XPR032_U251MG.311_96H_X3_B38:P20</th>\n",
       "      <th>XPR032_YAPC.311_96H_X4.L2_B41:N14</th>\n",
       "      <th>XPR032_YAPC.311_96H_X5.L2_B41:N14</th>\n",
       "      <th>XPR032_YAPC.311_96H_X6.L2_B41:N14</th>\n",
       "      <th>XPR032_YAPC.311_96H_X4.L2_B41:P20</th>\n",
       "      <th>XPR032_YAPC.311_96H_X6.L2_B41:P20</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symbol</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>DDR1</th>\n",
       "      <td>5.255000</td>\n",
       "      <td>5.089600</td>\n",
       "      <td>5.427650</td>\n",
       "      <td>5.166525</td>\n",
       "      <td>5.239900</td>\n",
       "      <td>5.506400</td>\n",
       "      <td>5.4724</td>\n",
       "      <td>5.4069</td>\n",
       "      <td>5.7438</td>\n",
       "      <td>5.462400</td>\n",
       "      <td>...</td>\n",
       "      <td>5.63815</td>\n",
       "      <td>5.825700</td>\n",
       "      <td>6.23110</td>\n",
       "      <td>5.26800</td>\n",
       "      <td>5.916700</td>\n",
       "      <td>6.679475</td>\n",
       "      <td>6.938175</td>\n",
       "      <td>5.278950</td>\n",
       "      <td>7.19605</td>\n",
       "      <td>7.62890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAX8</th>\n",
       "      <td>4.913075</td>\n",
       "      <td>5.132925</td>\n",
       "      <td>5.074600</td>\n",
       "      <td>4.669800</td>\n",
       "      <td>5.265650</td>\n",
       "      <td>4.016100</td>\n",
       "      <td>4.6268</td>\n",
       "      <td>4.2078</td>\n",
       "      <td>4.2377</td>\n",
       "      <td>4.310450</td>\n",
       "      <td>...</td>\n",
       "      <td>4.42210</td>\n",
       "      <td>4.625200</td>\n",
       "      <td>4.62415</td>\n",
       "      <td>4.53150</td>\n",
       "      <td>5.175500</td>\n",
       "      <td>4.408500</td>\n",
       "      <td>4.002800</td>\n",
       "      <td>4.260275</td>\n",
       "      <td>4.15570</td>\n",
       "      <td>3.35210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GUCA1A</th>\n",
       "      <td>5.193900</td>\n",
       "      <td>5.247600</td>\n",
       "      <td>4.621200</td>\n",
       "      <td>5.694850</td>\n",
       "      <td>5.050300</td>\n",
       "      <td>4.331350</td>\n",
       "      <td>4.3682</td>\n",
       "      <td>4.2829</td>\n",
       "      <td>4.3346</td>\n",
       "      <td>4.207350</td>\n",
       "      <td>...</td>\n",
       "      <td>4.98960</td>\n",
       "      <td>4.619000</td>\n",
       "      <td>4.86720</td>\n",
       "      <td>4.67185</td>\n",
       "      <td>4.904350</td>\n",
       "      <td>5.476200</td>\n",
       "      <td>5.236925</td>\n",
       "      <td>5.535125</td>\n",
       "      <td>5.46450</td>\n",
       "      <td>5.54065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EPHB3</th>\n",
       "      <td>5.674850</td>\n",
       "      <td>6.362250</td>\n",
       "      <td>6.259725</td>\n",
       "      <td>5.560650</td>\n",
       "      <td>6.506450</td>\n",
       "      <td>6.481000</td>\n",
       "      <td>6.4755</td>\n",
       "      <td>6.9908</td>\n",
       "      <td>6.4902</td>\n",
       "      <td>6.621600</td>\n",
       "      <td>...</td>\n",
       "      <td>7.15630</td>\n",
       "      <td>7.442451</td>\n",
       "      <td>7.25270</td>\n",
       "      <td>6.36430</td>\n",
       "      <td>7.246000</td>\n",
       "      <td>7.240800</td>\n",
       "      <td>7.576700</td>\n",
       "      <td>7.964250</td>\n",
       "      <td>7.88650</td>\n",
       "      <td>7.55025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ESRRA</th>\n",
       "      <td>7.840600</td>\n",
       "      <td>7.572600</td>\n",
       "      <td>7.368150</td>\n",
       "      <td>7.538750</td>\n",
       "      <td>7.622751</td>\n",
       "      <td>7.349851</td>\n",
       "      <td>7.5800</td>\n",
       "      <td>7.1017</td>\n",
       "      <td>7.1989</td>\n",
       "      <td>7.012701</td>\n",
       "      <td>...</td>\n",
       "      <td>6.22650</td>\n",
       "      <td>6.905600</td>\n",
       "      <td>6.75130</td>\n",
       "      <td>6.59240</td>\n",
       "      <td>7.017099</td>\n",
       "      <td>8.261250</td>\n",
       "      <td>8.548100</td>\n",
       "      <td>7.938550</td>\n",
       "      <td>8.24955</td>\n",
       "      <td>8.39360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        XPR032_A375.311_96H_X1_B38:N14  XPR032_A375.311_96H_X2_B38:N14  \\\n",
       "symbol                                                                   \n",
       "DDR1                          5.255000                        5.089600   \n",
       "PAX8                          4.913075                        5.132925   \n",
       "GUCA1A                        5.193900                        5.247600   \n",
       "EPHB3                         5.674850                        6.362250   \n",
       "ESRRA                         7.840600                        7.572600   \n",
       "\n",
       "        XPR032_A375.311_96H_X3_B38:N14  XPR032_A375.311_96H_X1_B38:P20  \\\n",
       "symbol                                                                   \n",
       "DDR1                          5.427650                        5.166525   \n",
       "PAX8                          5.074600                        4.669800   \n",
       "GUCA1A                        4.621200                        5.694850   \n",
       "EPHB3                         6.259725                        5.560650   \n",
       "ESRRA                         7.368150                        7.538750   \n",
       "\n",
       "        XPR032_A375.311_96H_X3_B38:P20  XPR032_A549.311_96H_X1_B38:N14  \\\n",
       "symbol                                                                   \n",
       "DDR1                          5.239900                        5.506400   \n",
       "PAX8                          5.265650                        4.016100   \n",
       "GUCA1A                        5.050300                        4.331350   \n",
       "EPHB3                         6.506450                        6.481000   \n",
       "ESRRA                         7.622751                        7.349851   \n",
       "\n",
       "        XPR032_A549.311_96H_X2_B38:N14  XPR032_A549.311_96H_X3_B38:N14  \\\n",
       "symbol                                                                   \n",
       "DDR1                            5.4724                          5.4069   \n",
       "PAX8                            4.6268                          4.2078   \n",
       "GUCA1A                          4.3682                          4.2829   \n",
       "EPHB3                           6.4755                          6.9908   \n",
       "ESRRA                           7.5800                          7.1017   \n",
       "\n",
       "        XPR032_A549.311_96H_X1_B38:P20  XPR032_A549.311_96H_X2_B38:P20  ...  \\\n",
       "symbol                                                                  ...   \n",
       "DDR1                            5.7438                        5.462400  ...   \n",
       "PAX8                            4.2377                        4.310450  ...   \n",
       "GUCA1A                          4.3346                        4.207350  ...   \n",
       "EPHB3                           6.4902                        6.621600  ...   \n",
       "ESRRA                           7.1989                        7.012701  ...   \n",
       "\n",
       "        XPR032_U251MG.311_96H_X2_B38:N14  XPR032_U251MG.311_96H_X3_B38:N14  \\\n",
       "symbol                                                                       \n",
       "DDR1                             5.63815                          5.825700   \n",
       "PAX8                             4.42210                          4.625200   \n",
       "GUCA1A                           4.98960                          4.619000   \n",
       "EPHB3                            7.15630                          7.442451   \n",
       "ESRRA                            6.22650                          6.905600   \n",
       "\n",
       "        XPR032_U251MG.311_96H_X1_B38:P20  XPR032_U251MG.311_96H_X2_B38:P20  \\\n",
       "symbol                                                                       \n",
       "DDR1                             6.23110                           5.26800   \n",
       "PAX8                             4.62415                           4.53150   \n",
       "GUCA1A                           4.86720                           4.67185   \n",
       "EPHB3                            7.25270                           6.36430   \n",
       "ESRRA                            6.75130                           6.59240   \n",
       "\n",
       "        XPR032_U251MG.311_96H_X3_B38:P20  XPR032_YAPC.311_96H_X4.L2_B41:N14  \\\n",
       "symbol                                                                        \n",
       "DDR1                            5.916700                           6.679475   \n",
       "PAX8                            5.175500                           4.408500   \n",
       "GUCA1A                          4.904350                           5.476200   \n",
       "EPHB3                           7.246000                           7.240800   \n",
       "ESRRA                           7.017099                           8.261250   \n",
       "\n",
       "        XPR032_YAPC.311_96H_X5.L2_B41:N14  XPR032_YAPC.311_96H_X6.L2_B41:N14  \\\n",
       "symbol                                                                         \n",
       "DDR1                             6.938175                           5.278950   \n",
       "PAX8                             4.002800                           4.260275   \n",
       "GUCA1A                           5.236925                           5.535125   \n",
       "EPHB3                            7.576700                           7.964250   \n",
       "ESRRA                            8.548100                           7.938550   \n",
       "\n",
       "        XPR032_YAPC.311_96H_X4.L2_B41:P20  XPR032_YAPC.311_96H_X6.L2_B41:P20  \n",
       "symbol                                                                        \n",
       "DDR1                              7.19605                            7.62890  \n",
       "PAX8                              4.15570                            3.35210  \n",
       "GUCA1A                            5.46450                            5.54065  \n",
       "EPHB3                             7.88650                            7.55025  \n",
       "ESRRA                             8.24955                            8.39360  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "try: \n",
    "    expr_df = pd.read_csv(f\"{l1000_data_dir}/{ko_gene}_L1000_CRISPRKO_fulldata.tsv\", sep='\\t', index_col=0)\n",
    "except: \n",
    "    l1000_data_df = pd.read_csv(f\"{l1000_data_dir}/{ko_gene}_L1000_CRISPRKO_data.tsv\", sep='\\t')\n",
    "\n",
    "    l1000_data_list = []\n",
    "    l1000_meta_list = []\n",
    "    for row in l1000_data_df.itertuples(): \n",
    "        try:\n",
    "            temp_df = pd.read_csv(row.persistent_id, sep='\\t', index_col=0)\n",
    "        except:\n",
    "            print(f\"Unable to access data from row {row.Index} at {row.persistent_id}\")\n",
    "            continue\n",
    "        for col in temp_df.columns:\n",
    "            l1000_meta_list.append([col] + l1000_data_df.loc[row.Index].tolist())\n",
    "        l1000_data_list.append(temp_df)\n",
    "    expr_df = pd.concat(l1000_data_list, axis=1)\n",
    "\n",
    "expr_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File at '../L1000_data/NR0B1_L1000_CRISPRKO_fulldata.tsv' already exists!\n"
     ]
    }
   ],
   "source": [
    "if not exists(f\"{l1000_data_dir}/{ko_gene}_L1000_CRISPRKO_fulldata.tsv\"): \n",
    "    expr_df.to_csv(f\"{l1000_data_dir}/{ko_gene}_L1000_CRISPRKO_fulldata.tsv\", sep='\\t', index=True)\n",
    "else: \n",
    "    print(f\"File at '{l1000_data_dir}/{ko_gene}_L1000_CRISPRKO_fulldata.tsv' already exists!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>tissue</th>\n",
       "      <th>disease</th>\n",
       "      <th>cell_line</th>\n",
       "      <th>pert_name</th>\n",
       "      <th>pert_time</th>\n",
       "      <th>pert_type</th>\n",
       "      <th>data_level</th>\n",
       "      <th>creation_time</th>\n",
       "      <th>persistent_id</th>\n",
       "      <th>pert_dose</th>\n",
       "      <th>batch</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>XPR032_A375.311_96H_X1_B38:N14</th>\n",
       "      <td>skin of body</td>\n",
       "      <td>melanoma</td>\n",
       "      <td>A375.311</td>\n",
       "      <td>NR0B1</td>\n",
       "      <td>96 h</td>\n",
       "      <td>CRISPR Knockout</td>\n",
       "      <td>3</td>\n",
       "      <td>2021-01-21</td>\n",
       "      <td>https://lincs-dcic.s3.amazonaws.com/LINCS-data...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>XPR032_A375.311_96H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XPR032_A375.311_96H_X2_B38:N14</th>\n",
       "      <td>skin of body</td>\n",
       "      <td>melanoma</td>\n",
       "      <td>A375.311</td>\n",
       "      <td>NR0B1</td>\n",
       "      <td>96 h</td>\n",
       "      <td>CRISPR Knockout</td>\n",
       "      <td>3</td>\n",
       "      <td>2021-01-21</td>\n",
       "      <td>https://lincs-dcic.s3.amazonaws.com/LINCS-data...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>XPR032_A375.311_96H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XPR032_A375.311_96H_X3_B38:N14</th>\n",
       "      <td>skin of body</td>\n",
       "      <td>melanoma</td>\n",
       "      <td>A375.311</td>\n",
       "      <td>NR0B1</td>\n",
       "      <td>96 h</td>\n",
       "      <td>CRISPR Knockout</td>\n",
       "      <td>3</td>\n",
       "      <td>2021-01-21</td>\n",
       "      <td>https://lincs-dcic.s3.amazonaws.com/LINCS-data...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>XPR032_A375.311_96H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XPR032_A375.311_96H_X1_B38:P20</th>\n",
       "      <td>skin of body</td>\n",
       "      <td>melanoma</td>\n",
       "      <td>A375.311</td>\n",
       "      <td>NR0B1</td>\n",
       "      <td>96 h</td>\n",
       "      <td>CRISPR Knockout</td>\n",
       "      <td>3</td>\n",
       "      <td>2021-01-21</td>\n",
       "      <td>https://lincs-dcic.s3.amazonaws.com/LINCS-data...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>XPR032_A375.311_96H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XPR032_A375.311_96H_X3_B38:P20</th>\n",
       "      <td>skin of body</td>\n",
       "      <td>melanoma</td>\n",
       "      <td>A375.311</td>\n",
       "      <td>NR0B1</td>\n",
       "      <td>96 h</td>\n",
       "      <td>CRISPR Knockout</td>\n",
       "      <td>3</td>\n",
       "      <td>2021-01-21</td>\n",
       "      <td>https://lincs-dcic.s3.amazonaws.com/LINCS-data...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>XPR032_A375.311_96H</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      tissue   disease cell_line pert_name  \\\n",
       "id                                                                           \n",
       "XPR032_A375.311_96H_X1_B38:N14  skin of body  melanoma  A375.311     NR0B1   \n",
       "XPR032_A375.311_96H_X2_B38:N14  skin of body  melanoma  A375.311     NR0B1   \n",
       "XPR032_A375.311_96H_X3_B38:N14  skin of body  melanoma  A375.311     NR0B1   \n",
       "XPR032_A375.311_96H_X1_B38:P20  skin of body  melanoma  A375.311     NR0B1   \n",
       "XPR032_A375.311_96H_X3_B38:P20  skin of body  melanoma  A375.311     NR0B1   \n",
       "\n",
       "                               pert_time        pert_type  data_level  \\\n",
       "id                                                                      \n",
       "XPR032_A375.311_96H_X1_B38:N14      96 h  CRISPR Knockout           3   \n",
       "XPR032_A375.311_96H_X2_B38:N14      96 h  CRISPR Knockout           3   \n",
       "XPR032_A375.311_96H_X3_B38:N14      96 h  CRISPR Knockout           3   \n",
       "XPR032_A375.311_96H_X1_B38:P20      96 h  CRISPR Knockout           3   \n",
       "XPR032_A375.311_96H_X3_B38:P20      96 h  CRISPR Knockout           3   \n",
       "\n",
       "                               creation_time  \\\n",
       "id                                             \n",
       "XPR032_A375.311_96H_X1_B38:N14    2021-01-21   \n",
       "XPR032_A375.311_96H_X2_B38:N14    2021-01-21   \n",
       "XPR032_A375.311_96H_X3_B38:N14    2021-01-21   \n",
       "XPR032_A375.311_96H_X1_B38:P20    2021-01-21   \n",
       "XPR032_A375.311_96H_X3_B38:P20    2021-01-21   \n",
       "\n",
       "                                                                    persistent_id  \\\n",
       "id                                                                                  \n",
       "XPR032_A375.311_96H_X1_B38:N14  https://lincs-dcic.s3.amazonaws.com/LINCS-data...   \n",
       "XPR032_A375.311_96H_X2_B38:N14  https://lincs-dcic.s3.amazonaws.com/LINCS-data...   \n",
       "XPR032_A375.311_96H_X3_B38:N14  https://lincs-dcic.s3.amazonaws.com/LINCS-data...   \n",
       "XPR032_A375.311_96H_X1_B38:P20  https://lincs-dcic.s3.amazonaws.com/LINCS-data...   \n",
       "XPR032_A375.311_96H_X3_B38:P20  https://lincs-dcic.s3.amazonaws.com/LINCS-data...   \n",
       "\n",
       "                                pert_dose                batch  \n",
       "id                                                              \n",
       "XPR032_A375.311_96H_X1_B38:N14        NaN  XPR032_A375.311_96H  \n",
       "XPR032_A375.311_96H_X2_B38:N14        NaN  XPR032_A375.311_96H  \n",
       "XPR032_A375.311_96H_X3_B38:N14        NaN  XPR032_A375.311_96H  \n",
       "XPR032_A375.311_96H_X1_B38:P20        NaN  XPR032_A375.311_96H  \n",
       "XPR032_A375.311_96H_X3_B38:P20        NaN  XPR032_A375.311_96H  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "try: \n",
    "    meta_df = pd.read_csv(f\"{l1000_data_dir}/{ko_gene}_L1000_CRISPRKO_metadata.tsv\", sep='\\t', index_col=0)\n",
    "except:\n",
    "    meta_df = pd.DataFrame(l1000_meta_list, columns=['id'] + l1000_data_df.columns.tolist()).set_index('id')\n",
    "if 'batch' not in meta_df.columns:\n",
    "    meta_df['batch'] = meta_df.index.map(lambda x: '_'.join(x.split('_')[:3]))\n",
    "meta_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File at '../L1000_data/NR0B1_L1000_CRISPRKO_metadata.tsv' already exists!\n"
     ]
    }
   ],
   "source": [
    "if not exists(f\"{l1000_data_dir}/{ko_gene}_L1000_CRISPRKO_metadata.tsv\"): \n",
    "    meta_df.to_csv(f\"{l1000_data_dir}/{ko_gene}_L1000_CRISPRKO_metadata.tsv\", sep='\\t', index=True)\n",
    "else: \n",
    "    print(f\"File at '{l1000_data_dir}/{ko_gene}_L1000_CRISPRKO_metadata.tsv' already exists!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batches = meta_df['batch'].unique()\n",
    "len(batches)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load in Control Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctrl_data_df = pd.read_csv(f\"{l1000_data_dir}/L1000_Controls.tsv\", sep='\\t')\n",
    "ctrl_data_df = ctrl_data_df[ctrl_data_df['batch'].isin(batches)]\n",
    "set(batches).difference(ctrl_data_df['batch'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>local_id</th>\n",
       "      <th>persistent_id</th>\n",
       "      <th>batch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10777</th>\n",
       "      <td>L1000_LINCS_DCIC_2021_XPR032_A375.311_96H_D10_...</td>\n",
       "      <td>https://lincs-dcic.s3.amazonaws.com/LINCS-data...</td>\n",
       "      <td>XPR032_A375.311_96H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10778</th>\n",
       "      <td>L1000_LINCS_DCIC_2021_XPR032_A375.311_96H_E14_...</td>\n",
       "      <td>https://lincs-dcic.s3.amazonaws.com/LINCS-data...</td>\n",
       "      <td>XPR032_A375.311_96H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10779</th>\n",
       "      <td>L1000_LINCS_DCIC_2021_XPR032_A375.311_96H_F21_...</td>\n",
       "      <td>https://lincs-dcic.s3.amazonaws.com/LINCS-data...</td>\n",
       "      <td>XPR032_A375.311_96H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10780</th>\n",
       "      <td>L1000_LINCS_DCIC_2021_XPR032_A375.311_96H_G09_...</td>\n",
       "      <td>https://lincs-dcic.s3.amazonaws.com/LINCS-data...</td>\n",
       "      <td>XPR032_A375.311_96H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10781</th>\n",
       "      <td>L1000_LINCS_DCIC_2021_XPR032_A375.311_96H_H16_...</td>\n",
       "      <td>https://lincs-dcic.s3.amazonaws.com/LINCS-data...</td>\n",
       "      <td>XPR032_A375.311_96H</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                local_id  \\\n",
       "10777  L1000_LINCS_DCIC_2021_XPR032_A375.311_96H_D10_...   \n",
       "10778  L1000_LINCS_DCIC_2021_XPR032_A375.311_96H_E14_...   \n",
       "10779  L1000_LINCS_DCIC_2021_XPR032_A375.311_96H_F21_...   \n",
       "10780  L1000_LINCS_DCIC_2021_XPR032_A375.311_96H_G09_...   \n",
       "10781  L1000_LINCS_DCIC_2021_XPR032_A375.311_96H_H16_...   \n",
       "\n",
       "                                           persistent_id                batch  \n",
       "10777  https://lincs-dcic.s3.amazonaws.com/LINCS-data...  XPR032_A375.311_96H  \n",
       "10778  https://lincs-dcic.s3.amazonaws.com/LINCS-data...  XPR032_A375.311_96H  \n",
       "10779  https://lincs-dcic.s3.amazonaws.com/LINCS-data...  XPR032_A375.311_96H  \n",
       "10780  https://lincs-dcic.s3.amazonaws.com/LINCS-data...  XPR032_A375.311_96H  \n",
       "10781  https://lincs-dcic.s3.amazonaws.com/LINCS-data...  XPR032_A375.311_96H  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctrl_data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "try: \n",
    "    ctrl_expr_df = pd.read_csv(f\"{l1000_data_dir}/{ko_gene}_L1000_Controls_fulldata.tsv\", sep='\\t', index_col=0)\n",
    "    ctrl_meta_df = pd.read_csv(f\"{l1000_data_dir}/{ko_gene}_L1000_Controls_metadata.tsv\", sep='\\t', index_col=0)\n",
    "except:\n",
    "    ctrl_data_list = []\n",
    "    ctrl_meta_list = []\n",
    "    for row in ctrl_data_df.itertuples():\n",
    "        try: \n",
    "            temp_df = pd.read_csv(row.persistent_id, sep='\\t', index_col=0)\n",
    "        except:\n",
    "            print(f\"Unable to access data from row {row.Index} at {row.persistent_id}\")\n",
    "            continue\n",
    "        for col in temp_df.columns: \n",
    "            ctrl_meta_list.append([col, row.batch])\n",
    "        ctrl_data_list.append(temp_df)\n",
    "\n",
    "    ctrl_expr_df = pd.concat(ctrl_data_list, axis=1)\n",
    "    ctrl_meta_df = pd.DataFrame(ctrl_meta_list, columns=['id', 'batch']).set_index('id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>XPR032_A375.311_96H_X1_B38:D10</th>\n",
       "      <th>XPR032_A375.311_96H_X2_B38:D10</th>\n",
       "      <th>XPR032_A375.311_96H_X3_B38:D10</th>\n",
       "      <th>XPR032_A375.311_96H_X1_B38:E14</th>\n",
       "      <th>XPR032_A375.311_96H_X2_B38:E14</th>\n",
       "      <th>XPR032_A375.311_96H_X3_B38:E14</th>\n",
       "      <th>XPR032_A375.311_96H_X1_B38:F21</th>\n",
       "      <th>XPR032_A375.311_96H_X2_B38:F21</th>\n",
       "      <th>XPR032_A375.311_96H_X3_B38:F21</th>\n",
       "      <th>XPR032_A375.311_96H_X1_B38:G09</th>\n",
       "      <th>...</th>\n",
       "      <th>XPR032_YAPC.311_96H_X6.L2_B41:I09</th>\n",
       "      <th>XPR032_YAPC.311_96H_X4.L2_B41:K10</th>\n",
       "      <th>XPR032_YAPC.311_96H_X5.L2_B41:K10</th>\n",
       "      <th>XPR032_YAPC.311_96H_X6.L2_B41:K10</th>\n",
       "      <th>XPR032_YAPC.311_96H_X4.L2_B41:K20</th>\n",
       "      <th>XPR032_YAPC.311_96H_X5.L2_B41:K20</th>\n",
       "      <th>XPR032_YAPC.311_96H_X6.L2_B41:K20</th>\n",
       "      <th>XPR032_YAPC.311_96H_X4.L2_B41:P03</th>\n",
       "      <th>XPR032_YAPC.311_96H_X5.L2_B41:P03</th>\n",
       "      <th>XPR032_YAPC.311_96H_X6.L2_B41:P03</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symbol</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>NAT2</th>\n",
       "      <td>8.064151</td>\n",
       "      <td>9.469100</td>\n",
       "      <td>7.601850</td>\n",
       "      <td>8.033700</td>\n",
       "      <td>9.220400</td>\n",
       "      <td>8.24655</td>\n",
       "      <td>8.894925</td>\n",
       "      <td>8.72050</td>\n",
       "      <td>9.17865</td>\n",
       "      <td>8.68495</td>\n",
       "      <td>...</td>\n",
       "      <td>11.370251</td>\n",
       "      <td>10.716150</td>\n",
       "      <td>10.178425</td>\n",
       "      <td>11.328951</td>\n",
       "      <td>9.41990</td>\n",
       "      <td>11.02845</td>\n",
       "      <td>12.07100</td>\n",
       "      <td>11.243151</td>\n",
       "      <td>9.276251</td>\n",
       "      <td>9.758101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADA</th>\n",
       "      <td>6.507850</td>\n",
       "      <td>6.016400</td>\n",
       "      <td>7.335375</td>\n",
       "      <td>6.141600</td>\n",
       "      <td>5.903550</td>\n",
       "      <td>6.85880</td>\n",
       "      <td>6.393500</td>\n",
       "      <td>6.74075</td>\n",
       "      <td>6.35025</td>\n",
       "      <td>5.83335</td>\n",
       "      <td>...</td>\n",
       "      <td>4.890375</td>\n",
       "      <td>5.205100</td>\n",
       "      <td>3.744300</td>\n",
       "      <td>5.539550</td>\n",
       "      <td>5.21020</td>\n",
       "      <td>4.89420</td>\n",
       "      <td>4.83905</td>\n",
       "      <td>4.656200</td>\n",
       "      <td>4.270800</td>\n",
       "      <td>5.249350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CDH2</th>\n",
       "      <td>4.857850</td>\n",
       "      <td>4.623050</td>\n",
       "      <td>5.011100</td>\n",
       "      <td>4.752725</td>\n",
       "      <td>4.761450</td>\n",
       "      <td>4.68530</td>\n",
       "      <td>5.200500</td>\n",
       "      <td>4.61915</td>\n",
       "      <td>4.61665</td>\n",
       "      <td>4.86035</td>\n",
       "      <td>...</td>\n",
       "      <td>4.382100</td>\n",
       "      <td>4.921475</td>\n",
       "      <td>4.550850</td>\n",
       "      <td>4.611150</td>\n",
       "      <td>4.30870</td>\n",
       "      <td>4.04470</td>\n",
       "      <td>4.89275</td>\n",
       "      <td>5.437850</td>\n",
       "      <td>4.703550</td>\n",
       "      <td>5.628650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AKT3</th>\n",
       "      <td>4.182400</td>\n",
       "      <td>1.805000</td>\n",
       "      <td>4.125150</td>\n",
       "      <td>3.381000</td>\n",
       "      <td>3.420925</td>\n",
       "      <td>4.00585</td>\n",
       "      <td>4.777750</td>\n",
       "      <td>4.22330</td>\n",
       "      <td>6.42225</td>\n",
       "      <td>2.92995</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.254300</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MED6</th>\n",
       "      <td>5.369700</td>\n",
       "      <td>6.277175</td>\n",
       "      <td>7.007350</td>\n",
       "      <td>6.533900</td>\n",
       "      <td>6.349325</td>\n",
       "      <td>6.65425</td>\n",
       "      <td>5.729750</td>\n",
       "      <td>6.16220</td>\n",
       "      <td>6.22880</td>\n",
       "      <td>6.02475</td>\n",
       "      <td>...</td>\n",
       "      <td>6.200250</td>\n",
       "      <td>6.642900</td>\n",
       "      <td>5.949200</td>\n",
       "      <td>7.149099</td>\n",
       "      <td>6.65355</td>\n",
       "      <td>6.47650</td>\n",
       "      <td>6.49045</td>\n",
       "      <td>7.638025</td>\n",
       "      <td>6.261400</td>\n",
       "      <td>7.256250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 238 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        XPR032_A375.311_96H_X1_B38:D10  XPR032_A375.311_96H_X2_B38:D10  \\\n",
       "symbol                                                                   \n",
       "NAT2                          8.064151                        9.469100   \n",
       "ADA                           6.507850                        6.016400   \n",
       "CDH2                          4.857850                        4.623050   \n",
       "AKT3                          4.182400                        1.805000   \n",
       "MED6                          5.369700                        6.277175   \n",
       "\n",
       "        XPR032_A375.311_96H_X3_B38:D10  XPR032_A375.311_96H_X1_B38:E14  \\\n",
       "symbol                                                                   \n",
       "NAT2                          7.601850                        8.033700   \n",
       "ADA                           7.335375                        6.141600   \n",
       "CDH2                          5.011100                        4.752725   \n",
       "AKT3                          4.125150                        3.381000   \n",
       "MED6                          7.007350                        6.533900   \n",
       "\n",
       "        XPR032_A375.311_96H_X2_B38:E14  XPR032_A375.311_96H_X3_B38:E14  \\\n",
       "symbol                                                                   \n",
       "NAT2                          9.220400                         8.24655   \n",
       "ADA                           5.903550                         6.85880   \n",
       "CDH2                          4.761450                         4.68530   \n",
       "AKT3                          3.420925                         4.00585   \n",
       "MED6                          6.349325                         6.65425   \n",
       "\n",
       "        XPR032_A375.311_96H_X1_B38:F21  XPR032_A375.311_96H_X2_B38:F21  \\\n",
       "symbol                                                                   \n",
       "NAT2                          8.894925                         8.72050   \n",
       "ADA                           6.393500                         6.74075   \n",
       "CDH2                          5.200500                         4.61915   \n",
       "AKT3                          4.777750                         4.22330   \n",
       "MED6                          5.729750                         6.16220   \n",
       "\n",
       "        XPR032_A375.311_96H_X3_B38:F21  XPR032_A375.311_96H_X1_B38:G09  ...  \\\n",
       "symbol                                                                  ...   \n",
       "NAT2                           9.17865                         8.68495  ...   \n",
       "ADA                            6.35025                         5.83335  ...   \n",
       "CDH2                           4.61665                         4.86035  ...   \n",
       "AKT3                           6.42225                         2.92995  ...   \n",
       "MED6                           6.22880                         6.02475  ...   \n",
       "\n",
       "        XPR032_YAPC.311_96H_X6.L2_B41:I09  XPR032_YAPC.311_96H_X4.L2_B41:K10  \\\n",
       "symbol                                                                         \n",
       "NAT2                            11.370251                          10.716150   \n",
       "ADA                              4.890375                           5.205100   \n",
       "CDH2                             4.382100                           4.921475   \n",
       "AKT3                             0.000000                           1.254300   \n",
       "MED6                             6.200250                           6.642900   \n",
       "\n",
       "        XPR032_YAPC.311_96H_X5.L2_B41:K10  XPR032_YAPC.311_96H_X6.L2_B41:K10  \\\n",
       "symbol                                                                         \n",
       "NAT2                            10.178425                          11.328951   \n",
       "ADA                              3.744300                           5.539550   \n",
       "CDH2                             4.550850                           4.611150   \n",
       "AKT3                             0.000000                           0.000000   \n",
       "MED6                             5.949200                           7.149099   \n",
       "\n",
       "        XPR032_YAPC.311_96H_X4.L2_B41:K20  XPR032_YAPC.311_96H_X5.L2_B41:K20  \\\n",
       "symbol                                                                         \n",
       "NAT2                              9.41990                           11.02845   \n",
       "ADA                               5.21020                            4.89420   \n",
       "CDH2                              4.30870                            4.04470   \n",
       "AKT3                              0.00000                            0.00000   \n",
       "MED6                              6.65355                            6.47650   \n",
       "\n",
       "        XPR032_YAPC.311_96H_X6.L2_B41:K20  XPR032_YAPC.311_96H_X4.L2_B41:P03  \\\n",
       "symbol                                                                         \n",
       "NAT2                             12.07100                          11.243151   \n",
       "ADA                               4.83905                           4.656200   \n",
       "CDH2                              4.89275                           5.437850   \n",
       "AKT3                              0.00000                           0.000000   \n",
       "MED6                              6.49045                           7.638025   \n",
       "\n",
       "        XPR032_YAPC.311_96H_X5.L2_B41:P03  XPR032_YAPC.311_96H_X6.L2_B41:P03  \n",
       "symbol                                                                        \n",
       "NAT2                             9.276251                           9.758101  \n",
       "ADA                              4.270800                           5.249350  \n",
       "CDH2                             4.703550                           5.628650  \n",
       "AKT3                             0.000000                           0.000000  \n",
       "MED6                             6.261400                           7.256250  \n",
       "\n",
       "[5 rows x 238 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctrl_expr_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>batch</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>XPR032_A375.311_96H_X1_B38:D10</th>\n",
       "      <td>XPR032_A375.311_96H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XPR032_A375.311_96H_X2_B38:D10</th>\n",
       "      <td>XPR032_A375.311_96H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XPR032_A375.311_96H_X3_B38:D10</th>\n",
       "      <td>XPR032_A375.311_96H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XPR032_A375.311_96H_X1_B38:E14</th>\n",
       "      <td>XPR032_A375.311_96H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XPR032_A375.311_96H_X2_B38:E14</th>\n",
       "      <td>XPR032_A375.311_96H</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              batch\n",
       "id                                                 \n",
       "XPR032_A375.311_96H_X1_B38:D10  XPR032_A375.311_96H\n",
       "XPR032_A375.311_96H_X2_B38:D10  XPR032_A375.311_96H\n",
       "XPR032_A375.311_96H_X3_B38:D10  XPR032_A375.311_96H\n",
       "XPR032_A375.311_96H_X1_B38:E14  XPR032_A375.311_96H\n",
       "XPR032_A375.311_96H_X2_B38:E14  XPR032_A375.311_96H"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctrl_meta_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File at '../L1000_data/NR0B1_L1000_Controls_fulldata.tsv' already exists!\n"
     ]
    }
   ],
   "source": [
    "if not exists(f\"{l1000_data_dir}/{ko_gene}_L1000_Controls_fulldata.tsv\"): \n",
    "    ctrl_expr_df.to_csv(f\"{l1000_data_dir}/{ko_gene}_L1000_Controls_fulldata.tsv\", sep='\\t', index=True)\n",
    "else: \n",
    "    print(f\"File at '{l1000_data_dir}/{ko_gene}_L1000_Controls_fulldata.tsv' already exists!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File at '../L1000_data/NR0B1_L1000_Controls_metadata.tsv' already exists!\n"
     ]
    }
   ],
   "source": [
    "if not exists(f\"{l1000_data_dir}/{ko_gene}_L1000_Controls_metadata.tsv\"): \n",
    "    ctrl_meta_df.to_csv(f\"{l1000_data_dir}/{ko_gene}_L1000_Controls_metadata.tsv\", sep='\\t', index=True)\n",
    "else: \n",
    "    print(f\"File at '{l1000_data_dir}/{ko_gene}_L1000_Controls_metadata.tsv' already exists!\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Process Data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine data and remove duplicate genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>XPR032_A375.311_96H_X1_B38:N14</th>\n",
       "      <th>XPR032_A375.311_96H_X2_B38:N14</th>\n",
       "      <th>XPR032_A375.311_96H_X3_B38:N14</th>\n",
       "      <th>XPR032_A375.311_96H_X1_B38:P20</th>\n",
       "      <th>XPR032_A375.311_96H_X3_B38:P20</th>\n",
       "      <th>XPR032_A549.311_96H_X1_B38:N14</th>\n",
       "      <th>XPR032_A549.311_96H_X2_B38:N14</th>\n",
       "      <th>XPR032_A549.311_96H_X3_B38:N14</th>\n",
       "      <th>XPR032_A549.311_96H_X1_B38:P20</th>\n",
       "      <th>XPR032_A549.311_96H_X2_B38:P20</th>\n",
       "      <th>...</th>\n",
       "      <th>XPR032_YAPC.311_96H_X6.L2_B41:I09</th>\n",
       "      <th>XPR032_YAPC.311_96H_X4.L2_B41:K10</th>\n",
       "      <th>XPR032_YAPC.311_96H_X5.L2_B41:K10</th>\n",
       "      <th>XPR032_YAPC.311_96H_X6.L2_B41:K10</th>\n",
       "      <th>XPR032_YAPC.311_96H_X4.L2_B41:K20</th>\n",
       "      <th>XPR032_YAPC.311_96H_X5.L2_B41:K20</th>\n",
       "      <th>XPR032_YAPC.311_96H_X6.L2_B41:K20</th>\n",
       "      <th>XPR032_YAPC.311_96H_X4.L2_B41:P03</th>\n",
       "      <th>XPR032_YAPC.311_96H_X5.L2_B41:P03</th>\n",
       "      <th>XPR032_YAPC.311_96H_X6.L2_B41:P03</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symbol</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>A1CF</th>\n",
       "      <td>5.042300</td>\n",
       "      <td>4.88865</td>\n",
       "      <td>4.503100</td>\n",
       "      <td>5.114400</td>\n",
       "      <td>4.19680</td>\n",
       "      <td>5.7100</td>\n",
       "      <td>5.7842</td>\n",
       "      <td>6.03365</td>\n",
       "      <td>6.00650</td>\n",
       "      <td>5.556000</td>\n",
       "      <td>...</td>\n",
       "      <td>11.807100</td>\n",
       "      <td>11.71735</td>\n",
       "      <td>12.049049</td>\n",
       "      <td>11.65135</td>\n",
       "      <td>12.454750</td>\n",
       "      <td>11.67805</td>\n",
       "      <td>11.785749</td>\n",
       "      <td>11.98595</td>\n",
       "      <td>12.474900</td>\n",
       "      <td>12.255699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2M</th>\n",
       "      <td>8.136351</td>\n",
       "      <td>9.75640</td>\n",
       "      <td>7.550050</td>\n",
       "      <td>9.257950</td>\n",
       "      <td>8.29125</td>\n",
       "      <td>8.0900</td>\n",
       "      <td>8.1192</td>\n",
       "      <td>7.12570</td>\n",
       "      <td>6.50205</td>\n",
       "      <td>6.652599</td>\n",
       "      <td>...</td>\n",
       "      <td>7.871700</td>\n",
       "      <td>7.97915</td>\n",
       "      <td>7.558400</td>\n",
       "      <td>7.86500</td>\n",
       "      <td>7.667699</td>\n",
       "      <td>7.43105</td>\n",
       "      <td>8.419125</td>\n",
       "      <td>8.57765</td>\n",
       "      <td>7.610650</td>\n",
       "      <td>6.937225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A4GALT</th>\n",
       "      <td>5.682650</td>\n",
       "      <td>5.72150</td>\n",
       "      <td>5.574750</td>\n",
       "      <td>5.623775</td>\n",
       "      <td>5.77445</td>\n",
       "      <td>5.9049</td>\n",
       "      <td>5.6885</td>\n",
       "      <td>5.44520</td>\n",
       "      <td>5.58240</td>\n",
       "      <td>5.806400</td>\n",
       "      <td>...</td>\n",
       "      <td>5.152150</td>\n",
       "      <td>5.92100</td>\n",
       "      <td>5.048950</td>\n",
       "      <td>6.11105</td>\n",
       "      <td>5.527200</td>\n",
       "      <td>5.35310</td>\n",
       "      <td>5.177000</td>\n",
       "      <td>5.31725</td>\n",
       "      <td>5.868650</td>\n",
       "      <td>5.505100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A4GNT</th>\n",
       "      <td>5.345350</td>\n",
       "      <td>5.15115</td>\n",
       "      <td>5.101900</td>\n",
       "      <td>5.487600</td>\n",
       "      <td>4.99815</td>\n",
       "      <td>4.7636</td>\n",
       "      <td>4.7672</td>\n",
       "      <td>4.78310</td>\n",
       "      <td>4.81630</td>\n",
       "      <td>4.724250</td>\n",
       "      <td>...</td>\n",
       "      <td>10.035549</td>\n",
       "      <td>11.00025</td>\n",
       "      <td>10.085800</td>\n",
       "      <td>10.36765</td>\n",
       "      <td>10.302500</td>\n",
       "      <td>9.89095</td>\n",
       "      <td>9.880751</td>\n",
       "      <td>10.71460</td>\n",
       "      <td>10.327775</td>\n",
       "      <td>10.306700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAS</th>\n",
       "      <td>7.843300</td>\n",
       "      <td>8.01230</td>\n",
       "      <td>7.421226</td>\n",
       "      <td>7.221250</td>\n",
       "      <td>7.34165</td>\n",
       "      <td>7.4766</td>\n",
       "      <td>7.2301</td>\n",
       "      <td>7.06610</td>\n",
       "      <td>7.10430</td>\n",
       "      <td>7.214500</td>\n",
       "      <td>...</td>\n",
       "      <td>6.554150</td>\n",
       "      <td>6.45785</td>\n",
       "      <td>6.389650</td>\n",
       "      <td>6.15305</td>\n",
       "      <td>6.222600</td>\n",
       "      <td>6.66180</td>\n",
       "      <td>6.661350</td>\n",
       "      <td>6.67710</td>\n",
       "      <td>6.641350</td>\n",
       "      <td>6.521500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 290 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        XPR032_A375.311_96H_X1_B38:N14  XPR032_A375.311_96H_X2_B38:N14  \\\n",
       "symbol                                                                   \n",
       "A1CF                          5.042300                         4.88865   \n",
       "A2M                           8.136351                         9.75640   \n",
       "A4GALT                        5.682650                         5.72150   \n",
       "A4GNT                         5.345350                         5.15115   \n",
       "AAAS                          7.843300                         8.01230   \n",
       "\n",
       "        XPR032_A375.311_96H_X3_B38:N14  XPR032_A375.311_96H_X1_B38:P20  \\\n",
       "symbol                                                                   \n",
       "A1CF                          4.503100                        5.114400   \n",
       "A2M                           7.550050                        9.257950   \n",
       "A4GALT                        5.574750                        5.623775   \n",
       "A4GNT                         5.101900                        5.487600   \n",
       "AAAS                          7.421226                        7.221250   \n",
       "\n",
       "        XPR032_A375.311_96H_X3_B38:P20  XPR032_A549.311_96H_X1_B38:N14  \\\n",
       "symbol                                                                   \n",
       "A1CF                           4.19680                          5.7100   \n",
       "A2M                            8.29125                          8.0900   \n",
       "A4GALT                         5.77445                          5.9049   \n",
       "A4GNT                          4.99815                          4.7636   \n",
       "AAAS                           7.34165                          7.4766   \n",
       "\n",
       "        XPR032_A549.311_96H_X2_B38:N14  XPR032_A549.311_96H_X3_B38:N14  \\\n",
       "symbol                                                                   \n",
       "A1CF                            5.7842                         6.03365   \n",
       "A2M                             8.1192                         7.12570   \n",
       "A4GALT                          5.6885                         5.44520   \n",
       "A4GNT                           4.7672                         4.78310   \n",
       "AAAS                            7.2301                         7.06610   \n",
       "\n",
       "        XPR032_A549.311_96H_X1_B38:P20  XPR032_A549.311_96H_X2_B38:P20  ...  \\\n",
       "symbol                                                                  ...   \n",
       "A1CF                           6.00650                        5.556000  ...   \n",
       "A2M                            6.50205                        6.652599  ...   \n",
       "A4GALT                         5.58240                        5.806400  ...   \n",
       "A4GNT                          4.81630                        4.724250  ...   \n",
       "AAAS                           7.10430                        7.214500  ...   \n",
       "\n",
       "        XPR032_YAPC.311_96H_X6.L2_B41:I09  XPR032_YAPC.311_96H_X4.L2_B41:K10  \\\n",
       "symbol                                                                         \n",
       "A1CF                            11.807100                           11.71735   \n",
       "A2M                              7.871700                            7.97915   \n",
       "A4GALT                           5.152150                            5.92100   \n",
       "A4GNT                           10.035549                           11.00025   \n",
       "AAAS                             6.554150                            6.45785   \n",
       "\n",
       "        XPR032_YAPC.311_96H_X5.L2_B41:K10  XPR032_YAPC.311_96H_X6.L2_B41:K10  \\\n",
       "symbol                                                                         \n",
       "A1CF                            12.049049                           11.65135   \n",
       "A2M                              7.558400                            7.86500   \n",
       "A4GALT                           5.048950                            6.11105   \n",
       "A4GNT                           10.085800                           10.36765   \n",
       "AAAS                             6.389650                            6.15305   \n",
       "\n",
       "        XPR032_YAPC.311_96H_X4.L2_B41:K20  XPR032_YAPC.311_96H_X5.L2_B41:K20  \\\n",
       "symbol                                                                         \n",
       "A1CF                            12.454750                           11.67805   \n",
       "A2M                              7.667699                            7.43105   \n",
       "A4GALT                           5.527200                            5.35310   \n",
       "A4GNT                           10.302500                            9.89095   \n",
       "AAAS                             6.222600                            6.66180   \n",
       "\n",
       "        XPR032_YAPC.311_96H_X6.L2_B41:K20  XPR032_YAPC.311_96H_X4.L2_B41:P03  \\\n",
       "symbol                                                                         \n",
       "A1CF                            11.785749                           11.98595   \n",
       "A2M                              8.419125                            8.57765   \n",
       "A4GALT                           5.177000                            5.31725   \n",
       "A4GNT                            9.880751                           10.71460   \n",
       "AAAS                             6.661350                            6.67710   \n",
       "\n",
       "        XPR032_YAPC.311_96H_X5.L2_B41:P03  XPR032_YAPC.311_96H_X6.L2_B41:P03  \n",
       "symbol                                                                        \n",
       "A1CF                            12.474900                          12.255699  \n",
       "A2M                              7.610650                           6.937225  \n",
       "A4GALT                           5.868650                           5.505100  \n",
       "A4GNT                           10.327775                          10.306700  \n",
       "AAAS                             6.641350                           6.521500  \n",
       "\n",
       "[5 rows x 290 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_expr_df = pd.concat([\n",
    "    expr_df.groupby(expr_df.index).mean(), \n",
    "    ctrl_expr_df.groupby(ctrl_expr_df.index).mean()\n",
    "], axis=1)\n",
    "combined_expr_df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute Signatures: Batch Perturbations vs. Batch Controls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_profiles = {x: {'perts': [], 'ctrls': []} for x in batches}\n",
    "for b in batches: \n",
    "    batch_profiles[b]['perts'] = meta_df[meta_df['batch'] == b].index.tolist()\n",
    "    batch_profiles[b]['ctrls'] = ctrl_meta_df[ctrl_meta_df['batch'] == b].index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_signatures = {\n",
    "    'cd': {}, \n",
    "    'limma': {}, \n",
    "    'limma-voom': {},\n",
    "    'fc': {},\n",
    "    'ranksum': {},\n",
    "    'ttest': {}\n",
    "}"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Characteristic Direction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function for computing signatures with characteristic direction\n",
    "def cd_signature(ctrl_ids, case_ids, dataset):\n",
    "  \n",
    "    signature = characteristic_direction(\n",
    "        dataset.loc[:, ctrl_ids], \n",
    "        dataset.loc[:, case_ids], \n",
    "        calculate_sig=True\n",
    "    )\n",
    "    signature['Significance'] = signature['CD-coefficient'].apply(abs)\n",
    "    \n",
    "    return signature.sort_values(by=['CD-coefficient'], ascending=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: the following step may take a few minutes to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "for b in batches: \n",
    "    batch_signatures['cd'][b] = cd_signature(\n",
    "        batch_profiles[b]['ctrls'], \n",
    "        batch_profiles[b]['perts'],\n",
    "        combined_expr_df\n",
    "    )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Limma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function for computing signatures\n",
    "def limma(ctrl_ids, case_ids, dataset, voom):\n",
    "    \n",
    "    signature = limma_voom_differential_expression(\n",
    "        dataset.loc[:, ctrl_ids],\n",
    "        dataset.loc[:, case_ids],\n",
    "        voom_design=voom,\n",
    "        filter_genes=False\n",
    "    )\n",
    "    signature['Significance'] = signature['P.Value']\n",
    "\n",
    "    return signature.sort_values(\"t\", ascending=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: the following step may take a few minutes to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Loading required package: R.oo\n",
      "\n",
      "R[write to console]: Loading required package: R.methodsS3\n",
      "\n",
      "R[write to console]: R.methodsS3 v1.8.1 (2020-08-26 16:20:06 UTC) successfully loaded. See ?R.methodsS3 for help.\n",
      "\n",
      "R[write to console]: R.oo v1.24.0 (2020-08-26 16:11:58 UTC) successfully loaded. See ?R.oo for help.\n",
      "\n",
      "R[write to console]: \n",
      "Attaching package: ‘R.oo’\n",
      "\n",
      "\n",
      "R[write to console]: The following object is masked from ‘package:R.methodsS3’:\n",
      "\n",
      "    throw\n",
      "\n",
      "\n",
      "R[write to console]: The following objects are masked from ‘package:methods’:\n",
      "\n",
      "    getClasses, getMethods\n",
      "\n",
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      "\n",
      "    attach, detach, load, save\n",
      "\n",
      "\n",
      "R[write to console]: R.utils v2.10.1 (2020-08-26 22:50:31 UTC) successfully loaded. See ?R.utils for help.\n",
      "\n",
      "R[write to console]: \n",
      "Attaching package: ‘R.utils’\n",
      "\n",
      "\n",
      "R[write to console]: The following object is masked from ‘package:utils’:\n",
      "\n",
      "    timestamp\n",
      "\n",
      "\n",
      "R[write to console]: The following objects are masked from ‘package:base’:\n",
      "\n",
      "    cat, commandArgs, getOption, inherits, isOpen, nullfile, parse,\n",
      "    warnings\n",
      "\n",
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      "R[write to console]: \n",
      "Attaching package: ‘RCurl’\n",
      "\n",
      "\n",
      "R[write to console]: The following object is masked from ‘package:R.utils’:\n",
      "\n",
      "    reset\n",
      "\n",
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      "\n",
      "    clone\n",
      "\n",
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      "R[write to console]: Loading required package: S4Vectors\n",
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      "R[write to console]: Loading required package: stats4\n",
      "\n",
      "R[write to console]: Loading required package: BiocGenerics\n",
      "\n",
      "R[write to console]: Loading required package: parallel\n",
      "\n",
      "R[write to console]: \n",
      "Attaching package: ‘BiocGenerics’\n",
      "\n",
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      "R[write to console]: The following objects are masked from ‘package:parallel’:\n",
      "\n",
      "    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,\n",
      "    clusterExport, clusterMap, parApply, parCapply, parLapply,\n",
      "    parLapplyLB, parRapply, parSapply, parSapplyLB\n",
      "\n",
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      "    Filter, Find, Map, Position, Reduce, anyDuplicated, append,\n",
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      "    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,\n",
      "    lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,\n",
      "    pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,\n",
      "    tapply, union, unique, unsplit, which.max, which.min\n",
      "\n",
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      "R[write to console]: \n",
      "Attaching package: ‘S4Vectors’\n",
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      "R[write to console]: The following objects are masked from ‘package:base’:\n",
      "\n",
      "    I, expand.grid, unname\n",
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      "Attaching package: ‘IRanges’\n",
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      "\n",
      "    trim\n",
      "\n",
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      "R[write to console]: Loading required package: GenomicRanges\n",
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      "R[write to console]: Loading required package: MatrixGenerics\n",
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      "R[write to console]: \n",
      "Attaching package: ‘MatrixGenerics’\n",
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      "\n",
      "    colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,\n",
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      "    colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,\n",
      "    colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,\n",
      "    colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,\n",
      "    colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,\n",
      "    colWeightedMeans, colWeightedMedians, colWeightedSds,\n",
      "    colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,\n",
      "    rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,\n",
      "    rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,\n",
      "    rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,\n",
      "    rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,\n",
      "    rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,\n",
      "    rowWeightedMads, rowWeightedMeans, rowWeightedMedians,\n",
      "    rowWeightedSds, rowWeightedVars\n",
      "\n",
      "\n",
      "R[write to console]: Loading required package: Biobase\n",
      "\n",
      "R[write to console]: Welcome to Bioconductor\n",
      "\n",
      "    Vignettes contain introductory material; view with\n",
      "    'browseVignettes()'. To cite Bioconductor, see\n",
      "    'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'.\n",
      "\n",
      "\n",
      "R[write to console]: \n",
      "Attaching package: ‘Biobase’\n",
      "\n",
      "\n",
      "R[write to console]: The following object is masked from ‘package:MatrixGenerics’:\n",
      "\n",
      "    rowMedians\n",
      "\n",
      "\n",
      "R[write to console]: The following objects are masked from ‘package:matrixStats’:\n",
      "\n",
      "    anyMissing, rowMedians\n",
      "\n",
      "\n",
      "R[write to console]: \n",
      "Attaching package: ‘limma’\n",
      "\n",
      "\n",
      "R[write to console]: The following object is masked from ‘package:DESeq2’:\n",
      "\n",
      "    plotMA\n",
      "\n",
      "\n",
      "R[write to console]: The following object is masked from ‘package:BiocGenerics’:\n",
      "\n",
      "    plotMA\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for b in batches: \n",
    "    batch_signatures['limma'][b] = limma(\n",
    "        batch_profiles[b]['ctrls'], \n",
    "        batch_profiles[b]['perts'], \n",
    "        combined_expr_df,\n",
    "        voom=False\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "for b in batches: \n",
    "    batch_signatures['limma-voom'][b] = limma(\n",
    "        batch_profiles[b]['ctrls'], \n",
    "        batch_profiles[b]['perts'], \n",
    "        combined_expr_df,\n",
    "        voom=True\n",
    "    )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wilcoxon Rank-Sum Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ranksum(ctrl_ids, case_ids, dataset):\n",
    "  if len(ctrl_ids) + len(case_ids) < 32: \n",
    "    print(\"Warning! Sample sizes < 16 generally do not provide good results. \")\n",
    "  res_array = []\n",
    "  for gene in dataset.index: \n",
    "    res = ranksums(\n",
    "      dataset.loc[gene, case_ids],\n",
    "      dataset.loc[gene, ctrl_ids]\n",
    "    )\n",
    "    res_array.append([gene, res.statistic, res.pvalue])\n",
    "  signature = pd.DataFrame(\n",
    "    res_array, columns=['Geneid', 'Statistic', 'Pvalue']\n",
    "  ).set_index('Geneid')\n",
    "  signature['Significance'] = signature['Pvalue']\n",
    "  return signature.sort_values(by=['Statistic'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning! Sample sizes < 16 generally do not provide good results. \n"
     ]
    }
   ],
   "source": [
    "for b in batches: \n",
    "    batch_signatures['ranksum'][b] = ranksum(\n",
    "        batch_profiles[b]['ctrls'], \n",
    "        batch_profiles[b]['perts'], \n",
    "        combined_expr_df\n",
    "    )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Welch's t-test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ttest(ctrl_ids, case_ids, dataset):\n",
    "  res_array = []\n",
    "  for gene in dataset.index: \n",
    "    res = ttest_ind(\n",
    "      dataset.loc[gene, case_ids],\n",
    "      dataset.loc[gene, ctrl_ids],\n",
    "      equal_var = False\n",
    "    )\n",
    "    res_array.append([gene, res.statistic, res.pvalue])\n",
    "  signature = pd.DataFrame(\n",
    "    res_array, columns=['Geneid', 'Statistic', 'Pvalue']\n",
    "  ).set_index('Geneid')\n",
    "  signature['Significance'] = signature['Pvalue']\n",
    "  return signature.sort_values(by=['Statistic'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "for b in batches: \n",
    "    batch_signatures['ttest'][b] = ttest(\n",
    "        batch_profiles[b]['ctrls'], \n",
    "        batch_profiles[b]['perts'], \n",
    "        combined_expr_df\n",
    "    )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (log2) Fold Change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function for computing signatures with fold change\n",
    "def logFC(ctrl_ids, case_ids, dataset):\n",
    "\n",
    "    case_mean = dataset.loc[:, case_ids].mean(axis=1)\n",
    "    ctrl_mean = dataset.loc[:, ctrl_ids].mean(axis=1)\n",
    "\n",
    "    signature = case_mean / (ctrl_mean + 0.001)\n",
    "\n",
    "    signature_df = pd.DataFrame(\n",
    "        signature.apply(lambda x: log2(x+0.001)), columns=['logFC']\n",
    "    )\n",
    "    signature_df['Significance'] = signature_df['logFC'].apply(abs)\n",
    "    \n",
    "    return signature_df.sort_values('logFC', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "for b in batches: \n",
    "    batch_signatures['fc'][b] = logFC(\n",
    "        batch_profiles[b]['ctrls'], \n",
    "        batch_profiles[b]['perts'],\n",
    "        combined_expr_df\n",
    "    )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## All signatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "All CD batch signatures"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>XPR032_A375.311_96H</th>\n",
       "      <th>XPR032_A549.311_96H</th>\n",
       "      <th>XPR032_AGS.311_96H</th>\n",
       "      <th>XPR032_BICR6.311_96H</th>\n",
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       "      <th></th>\n",
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       "      <th>A1CF</th>\n",
       "      <td>-0.020061</td>\n",
       "      <td>-0.016047</td>\n",
       "      <td>-0.017783</td>\n",
       "      <td>-0.020645</td>\n",
       "      <td>-0.022145</td>\n",
       "      <td>-0.020211</td>\n",
       "      <td>-0.020482</td>\n",
       "      <td>-0.019187</td>\n",
       "      <td>-0.017267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2M</th>\n",
       "      <td>0.004170</td>\n",
       "      <td>0.004325</td>\n",
       "      <td>0.005073</td>\n",
       "      <td>-0.002482</td>\n",
       "      <td>0.001378</td>\n",
       "      <td>0.003904</td>\n",
       "      <td>0.010943</td>\n",
       "      <td>0.009085</td>\n",
       "      <td>0.008592</td>\n",
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       "      <th>A4GALT</th>\n",
       "      <td>0.000460</td>\n",
       "      <td>-0.001746</td>\n",
       "      <td>0.001997</td>\n",
       "      <td>0.002162</td>\n",
       "      <td>-0.001375</td>\n",
       "      <td>0.000720</td>\n",
       "      <td>0.000410</td>\n",
       "      <td>-0.000217</td>\n",
       "      <td>0.001741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A4GNT</th>\n",
       "      <td>-0.016629</td>\n",
       "      <td>-0.014972</td>\n",
       "      <td>-0.017431</td>\n",
       "      <td>-0.015936</td>\n",
       "      <td>-0.018799</td>\n",
       "      <td>-0.018877</td>\n",
       "      <td>-0.017925</td>\n",
       "      <td>-0.019558</td>\n",
       "      <td>-0.013562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAS</th>\n",
       "      <td>0.002492</td>\n",
       "      <td>0.003185</td>\n",
       "      <td>0.002736</td>\n",
       "      <td>0.001409</td>\n",
       "      <td>0.004325</td>\n",
       "      <td>0.002569</td>\n",
       "      <td>0.001021</td>\n",
       "      <td>0.003434</td>\n",
       "      <td>0.003237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZXDB</th>\n",
       "      <td>-0.000004</td>\n",
       "      <td>-0.000234</td>\n",
       "      <td>-0.000282</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>-0.002297</td>\n",
       "      <td>-0.000076</td>\n",
       "      <td>0.001384</td>\n",
       "      <td>-0.000324</td>\n",
       "      <td>-0.001289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZXDC</th>\n",
       "      <td>-0.000090</td>\n",
       "      <td>-0.010458</td>\n",
       "      <td>-0.006142</td>\n",
       "      <td>-0.003045</td>\n",
       "      <td>-0.003486</td>\n",
       "      <td>0.002266</td>\n",
       "      <td>-0.004996</td>\n",
       "      <td>-0.008848</td>\n",
       "      <td>-0.002561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZYX</th>\n",
       "      <td>0.014974</td>\n",
       "      <td>0.009031</td>\n",
       "      <td>0.012263</td>\n",
       "      <td>0.010796</td>\n",
       "      <td>0.016862</td>\n",
       "      <td>0.007596</td>\n",
       "      <td>0.014329</td>\n",
       "      <td>0.013464</td>\n",
       "      <td>0.009235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZZEF1</th>\n",
       "      <td>-0.010300</td>\n",
       "      <td>-0.013921</td>\n",
       "      <td>-0.006011</td>\n",
       "      <td>-0.008275</td>\n",
       "      <td>-0.012102</td>\n",
       "      <td>-0.009300</td>\n",
       "      <td>-0.005447</td>\n",
       "      <td>-0.010448</td>\n",
       "      <td>-0.013139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZZZ3</th>\n",
       "      <td>0.007604</td>\n",
       "      <td>0.006931</td>\n",
       "      <td>0.004252</td>\n",
       "      <td>0.005963</td>\n",
       "      <td>0.010460</td>\n",
       "      <td>0.006008</td>\n",
       "      <td>0.009018</td>\n",
       "      <td>0.005085</td>\n",
       "      <td>0.003209</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12327 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        XPR032_A375.311_96H  XPR032_A549.311_96H  XPR032_AGS.311_96H  \\\n",
       "Gene                                                                   \n",
       "A1CF              -0.020061            -0.016047           -0.017783   \n",
       "A2M                0.004170             0.004325            0.005073   \n",
       "A4GALT             0.000460            -0.001746            0.001997   \n",
       "A4GNT             -0.016629            -0.014972           -0.017431   \n",
       "AAAS               0.002492             0.003185            0.002736   \n",
       "...                     ...                  ...                 ...   \n",
       "ZXDB              -0.000004            -0.000234           -0.000282   \n",
       "ZXDC              -0.000090            -0.010458           -0.006142   \n",
       "ZYX                0.014974             0.009031            0.012263   \n",
       "ZZEF1             -0.010300            -0.013921           -0.006011   \n",
       "ZZZ3               0.007604             0.006931            0.004252   \n",
       "\n",
       "        XPR032_BICR6.311_96H  XPR032_ES2.311_96H  XPR032_HT29.311_96H  \\\n",
       "Gene                                                                    \n",
       "A1CF               -0.020645           -0.022145            -0.020211   \n",
       "A2M                -0.002482            0.001378             0.003904   \n",
       "A4GALT              0.002162           -0.001375             0.000720   \n",
       "A4GNT              -0.015936           -0.018799            -0.018877   \n",
       "AAAS                0.001409            0.004325             0.002569   \n",
       "...                      ...                 ...                  ...   \n",
       "ZXDB                0.000028           -0.002297            -0.000076   \n",
       "ZXDC               -0.003045           -0.003486             0.002266   \n",
       "ZYX                 0.010796            0.016862             0.007596   \n",
       "ZZEF1              -0.008275           -0.012102            -0.009300   \n",
       "ZZZ3                0.005963            0.010460             0.006008   \n",
       "\n",
       "        XPR032_PC3.311B_96H  XPR032_U251MG.311_96H  XPR032_YAPC.311_96H  \n",
       "Gene                                                                     \n",
       "A1CF              -0.020482              -0.019187            -0.017267  \n",
       "A2M                0.010943               0.009085             0.008592  \n",
       "A4GALT             0.000410              -0.000217             0.001741  \n",
       "A4GNT             -0.017925              -0.019558            -0.013562  \n",
       "AAAS               0.001021               0.003434             0.003237  \n",
       "...                     ...                    ...                  ...  \n",
       "ZXDB               0.001384              -0.000324            -0.001289  \n",
       "ZXDC              -0.004996              -0.008848            -0.002561  \n",
       "ZYX                0.014329               0.013464             0.009235  \n",
       "ZZEF1             -0.005447              -0.010448            -0.013139  \n",
       "ZZZ3               0.009018               0.005085             0.003209  \n",
       "\n",
       "[12327 rows x 9 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "All LIMMA batch signatures"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>XPR032_A375.311_96H</th>\n",
       "      <th>XPR032_A549.311_96H</th>\n",
       "      <th>XPR032_AGS.311_96H</th>\n",
       "      <th>XPR032_BICR6.311_96H</th>\n",
       "      <th>XPR032_ES2.311_96H</th>\n",
       "      <th>XPR032_HT29.311_96H</th>\n",
       "      <th>XPR032_PC3.311B_96H</th>\n",
       "      <th>XPR032_U251MG.311_96H</th>\n",
       "      <th>XPR032_YAPC.311_96H</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gene</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>A1CF</th>\n",
       "      <td>-43.490817</td>\n",
       "      <td>-50.202253</td>\n",
       "      <td>-31.401609</td>\n",
       "      <td>-92.215935</td>\n",
       "      <td>-57.170962</td>\n",
       "      <td>-52.801217</td>\n",
       "      <td>-40.826071</td>\n",
       "      <td>-40.648398</td>\n",
       "      <td>-32.479343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2M</th>\n",
       "      <td>5.799222</td>\n",
       "      <td>4.644173</td>\n",
       "      <td>4.969393</td>\n",
       "      <td>-7.136606</td>\n",
       "      <td>0.107135</td>\n",
       "      <td>4.674300</td>\n",
       "      <td>10.578271</td>\n",
       "      <td>11.012444</td>\n",
       "      <td>7.700708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A4GALT</th>\n",
       "      <td>-1.500088</td>\n",
       "      <td>-5.635605</td>\n",
       "      <td>2.188914</td>\n",
       "      <td>10.049661</td>\n",
       "      <td>-2.815912</td>\n",
       "      <td>-0.499869</td>\n",
       "      <td>2.751041</td>\n",
       "      <td>0.346938</td>\n",
       "      <td>4.627847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A4GNT</th>\n",
       "      <td>-43.542568</td>\n",
       "      <td>-41.352716</td>\n",
       "      <td>-52.692189</td>\n",
       "      <td>-79.309756</td>\n",
       "      <td>-68.903881</td>\n",
       "      <td>-60.534034</td>\n",
       "      <td>-53.336398</td>\n",
       "      <td>-57.597987</td>\n",
       "      <td>-30.738910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAS</th>\n",
       "      <td>10.579879</td>\n",
       "      <td>14.252838</td>\n",
       "      <td>12.600847</td>\n",
       "      <td>7.151148</td>\n",
       "      <td>11.757109</td>\n",
       "      <td>7.990256</td>\n",
       "      <td>1.258617</td>\n",
       "      <td>15.252655</td>\n",
       "      <td>9.632633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZXDB</th>\n",
       "      <td>3.515837</td>\n",
       "      <td>-0.587111</td>\n",
       "      <td>-0.078946</td>\n",
       "      <td>3.251433</td>\n",
       "      <td>-2.533632</td>\n",
       "      <td>2.519761</td>\n",
       "      <td>0.531741</td>\n",
       "      <td>-1.372360</td>\n",
       "      <td>-2.505127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZXDC</th>\n",
       "      <td>-2.862318</td>\n",
       "      <td>-16.505548</td>\n",
       "      <td>-6.219017</td>\n",
       "      <td>-9.643654</td>\n",
       "      <td>-5.520325</td>\n",
       "      <td>1.014756</td>\n",
       "      <td>-8.714572</td>\n",
       "      <td>-23.390602</td>\n",
       "      <td>-6.854683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZYX</th>\n",
       "      <td>22.315721</td>\n",
       "      <td>27.402711</td>\n",
       "      <td>22.489053</td>\n",
       "      <td>35.119063</td>\n",
       "      <td>21.876607</td>\n",
       "      <td>13.386875</td>\n",
       "      <td>20.948085</td>\n",
       "      <td>26.892091</td>\n",
       "      <td>10.346862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZZEF1</th>\n",
       "      <td>-17.462598</td>\n",
       "      <td>-30.928752</td>\n",
       "      <td>-10.595280</td>\n",
       "      <td>-17.735101</td>\n",
       "      <td>-19.004267</td>\n",
       "      <td>-22.132080</td>\n",
       "      <td>-11.055636</td>\n",
       "      <td>-15.283503</td>\n",
       "      <td>-12.173054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZZZ3</th>\n",
       "      <td>11.261598</td>\n",
       "      <td>12.323906</td>\n",
       "      <td>8.319129</td>\n",
       "      <td>19.538155</td>\n",
       "      <td>31.135778</td>\n",
       "      <td>13.941717</td>\n",
       "      <td>12.643030</td>\n",
       "      <td>12.765400</td>\n",
       "      <td>5.741134</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12327 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        XPR032_A375.311_96H  XPR032_A549.311_96H  XPR032_AGS.311_96H  \\\n",
       "Gene                                                                   \n",
       "A1CF             -43.490817           -50.202253          -31.401609   \n",
       "A2M                5.799222             4.644173            4.969393   \n",
       "A4GALT            -1.500088            -5.635605            2.188914   \n",
       "A4GNT            -43.542568           -41.352716          -52.692189   \n",
       "AAAS              10.579879            14.252838           12.600847   \n",
       "...                     ...                  ...                 ...   \n",
       "ZXDB               3.515837            -0.587111           -0.078946   \n",
       "ZXDC              -2.862318           -16.505548           -6.219017   \n",
       "ZYX               22.315721            27.402711           22.489053   \n",
       "ZZEF1            -17.462598           -30.928752          -10.595280   \n",
       "ZZZ3              11.261598            12.323906            8.319129   \n",
       "\n",
       "        XPR032_BICR6.311_96H  XPR032_ES2.311_96H  XPR032_HT29.311_96H  \\\n",
       "Gene                                                                    \n",
       "A1CF              -92.215935          -57.170962           -52.801217   \n",
       "A2M                -7.136606            0.107135             4.674300   \n",
       "A4GALT             10.049661           -2.815912            -0.499869   \n",
       "A4GNT             -79.309756          -68.903881           -60.534034   \n",
       "AAAS                7.151148           11.757109             7.990256   \n",
       "...                      ...                 ...                  ...   \n",
       "ZXDB                3.251433           -2.533632             2.519761   \n",
       "ZXDC               -9.643654           -5.520325             1.014756   \n",
       "ZYX                35.119063           21.876607            13.386875   \n",
       "ZZEF1             -17.735101          -19.004267           -22.132080   \n",
       "ZZZ3               19.538155           31.135778            13.941717   \n",
       "\n",
       "        XPR032_PC3.311B_96H  XPR032_U251MG.311_96H  XPR032_YAPC.311_96H  \n",
       "Gene                                                                     \n",
       "A1CF             -40.826071             -40.648398           -32.479343  \n",
       "A2M               10.578271              11.012444             7.700708  \n",
       "A4GALT             2.751041               0.346938             4.627847  \n",
       "A4GNT            -53.336398             -57.597987           -30.738910  \n",
       "AAAS               1.258617              15.252655             9.632633  \n",
       "...                     ...                    ...                  ...  \n",
       "ZXDB               0.531741              -1.372360            -2.505127  \n",
       "ZXDC              -8.714572             -23.390602            -6.854683  \n",
       "ZYX               20.948085              26.892091            10.346862  \n",
       "ZZEF1            -11.055636             -15.283503           -12.173054  \n",
       "ZZZ3              12.643030              12.765400             5.741134  \n",
       "\n",
       "[12327 rows x 9 columns]"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/markdown": [
       "All LIMMA-VOOM batch signatures"
      ],
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       "<IPython.core.display.Markdown object>"
      ]
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       "      <th></th>\n",
       "      <th>XPR032_A375.311_96H</th>\n",
       "      <th>XPR032_A549.311_96H</th>\n",
       "      <th>XPR032_AGS.311_96H</th>\n",
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       "    <tr>\n",
       "      <th>Gene</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>A1CF</th>\n",
       "      <td>-34.949567</td>\n",
       "      <td>-39.287164</td>\n",
       "      <td>-29.381604</td>\n",
       "      <td>-52.248859</td>\n",
       "      <td>-34.178231</td>\n",
       "      <td>-40.215366</td>\n",
       "      <td>-22.505707</td>\n",
       "      <td>-37.216377</td>\n",
       "      <td>-31.834028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2M</th>\n",
       "      <td>6.090058</td>\n",
       "      <td>4.804545</td>\n",
       "      <td>5.222679</td>\n",
       "      <td>-7.156249</td>\n",
       "      <td>0.107653</td>\n",
       "      <td>4.825610</td>\n",
       "      <td>12.292449</td>\n",
       "      <td>13.081345</td>\n",
       "      <td>8.563366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A4GALT</th>\n",
       "      <td>-1.517428</td>\n",
       "      <td>-5.634657</td>\n",
       "      <td>2.220746</td>\n",
       "      <td>10.122096</td>\n",
       "      <td>-2.855799</td>\n",
       "      <td>-0.500051</td>\n",
       "      <td>2.779552</td>\n",
       "      <td>0.342804</td>\n",
       "      <td>4.640660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A4GNT</th>\n",
       "      <td>-37.763639</td>\n",
       "      <td>-30.730089</td>\n",
       "      <td>-45.907697</td>\n",
       "      <td>-67.626807</td>\n",
       "      <td>-52.856838</td>\n",
       "      <td>-48.259257</td>\n",
       "      <td>-43.704690</td>\n",
       "      <td>-42.011089</td>\n",
       "      <td>-28.073611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAS</th>\n",
       "      <td>10.250869</td>\n",
       "      <td>14.465891</td>\n",
       "      <td>12.514345</td>\n",
       "      <td>6.997811</td>\n",
       "      <td>11.942195</td>\n",
       "      <td>7.894037</td>\n",
       "      <td>1.229278</td>\n",
       "      <td>14.777241</td>\n",
       "      <td>9.215606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZXDB</th>\n",
       "      <td>3.606642</td>\n",
       "      <td>-0.558975</td>\n",
       "      <td>-0.079113</td>\n",
       "      <td>3.274975</td>\n",
       "      <td>-2.480610</td>\n",
       "      <td>2.491393</td>\n",
       "      <td>0.531275</td>\n",
       "      <td>-1.344889</td>\n",
       "      <td>-2.418831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZXDC</th>\n",
       "      <td>-2.723422</td>\n",
       "      <td>-14.580570</td>\n",
       "      <td>-6.012338</td>\n",
       "      <td>-9.625397</td>\n",
       "      <td>-5.063490</td>\n",
       "      <td>1.028739</td>\n",
       "      <td>-8.797868</td>\n",
       "      <td>-21.225242</td>\n",
       "      <td>-6.738135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZYX</th>\n",
       "      <td>27.032693</td>\n",
       "      <td>31.343592</td>\n",
       "      <td>24.743564</td>\n",
       "      <td>37.172418</td>\n",
       "      <td>29.325499</td>\n",
       "      <td>14.797494</td>\n",
       "      <td>27.354354</td>\n",
       "      <td>31.617104</td>\n",
       "      <td>11.002453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZZEF1</th>\n",
       "      <td>-13.497804</td>\n",
       "      <td>-23.265649</td>\n",
       "      <td>-9.201975</td>\n",
       "      <td>-14.903780</td>\n",
       "      <td>-15.419864</td>\n",
       "      <td>-19.237326</td>\n",
       "      <td>-8.930700</td>\n",
       "      <td>-12.156031</td>\n",
       "      <td>-10.224540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZZZ3</th>\n",
       "      <td>13.068385</td>\n",
       "      <td>14.011240</td>\n",
       "      <td>8.887154</td>\n",
       "      <td>20.470434</td>\n",
       "      <td>37.831049</td>\n",
       "      <td>15.414938</td>\n",
       "      <td>14.520412</td>\n",
       "      <td>14.108206</td>\n",
       "      <td>5.815561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12327 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        XPR032_A375.311_96H  XPR032_A549.311_96H  XPR032_AGS.311_96H  \\\n",
       "Gene                                                                   \n",
       "A1CF             -34.949567           -39.287164          -29.381604   \n",
       "A2M                6.090058             4.804545            5.222679   \n",
       "A4GALT            -1.517428            -5.634657            2.220746   \n",
       "A4GNT            -37.763639           -30.730089          -45.907697   \n",
       "AAAS              10.250869            14.465891           12.514345   \n",
       "...                     ...                  ...                 ...   \n",
       "ZXDB               3.606642            -0.558975           -0.079113   \n",
       "ZXDC              -2.723422           -14.580570           -6.012338   \n",
       "ZYX               27.032693            31.343592           24.743564   \n",
       "ZZEF1            -13.497804           -23.265649           -9.201975   \n",
       "ZZZ3              13.068385            14.011240            8.887154   \n",
       "\n",
       "        XPR032_BICR6.311_96H  XPR032_ES2.311_96H  XPR032_HT29.311_96H  \\\n",
       "Gene                                                                    \n",
       "A1CF              -52.248859          -34.178231           -40.215366   \n",
       "A2M                -7.156249            0.107653             4.825610   \n",
       "A4GALT             10.122096           -2.855799            -0.500051   \n",
       "A4GNT             -67.626807          -52.856838           -48.259257   \n",
       "AAAS                6.997811           11.942195             7.894037   \n",
       "...                      ...                 ...                  ...   \n",
       "ZXDB                3.274975           -2.480610             2.491393   \n",
       "ZXDC               -9.625397           -5.063490             1.028739   \n",
       "ZYX                37.172418           29.325499            14.797494   \n",
       "ZZEF1             -14.903780          -15.419864           -19.237326   \n",
       "ZZZ3               20.470434           37.831049            15.414938   \n",
       "\n",
       "        XPR032_PC3.311B_96H  XPR032_U251MG.311_96H  XPR032_YAPC.311_96H  \n",
       "Gene                                                                     \n",
       "A1CF             -22.505707             -37.216377           -31.834028  \n",
       "A2M               12.292449              13.081345             8.563366  \n",
       "A4GALT             2.779552               0.342804             4.640660  \n",
       "A4GNT            -43.704690             -42.011089           -28.073611  \n",
       "AAAS               1.229278              14.777241             9.215606  \n",
       "...                     ...                    ...                  ...  \n",
       "ZXDB               0.531275              -1.344889            -2.418831  \n",
       "ZXDC              -8.797868             -21.225242            -6.738135  \n",
       "ZYX               27.354354              31.617104            11.002453  \n",
       "ZZEF1             -8.930700             -12.156031           -10.224540  \n",
       "ZZZ3              14.520412              14.108206             5.815561  \n",
       "\n",
       "[12327 rows x 9 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "All RANKSUM batch signatures"
      ],
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       "<IPython.core.display.Markdown object>"
      ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>XPR032_A375.311_96H</th>\n",
       "      <th>XPR032_A549.311_96H</th>\n",
       "      <th>XPR032_AGS.311_96H</th>\n",
       "      <th>XPR032_BICR6.311_96H</th>\n",
       "      <th>XPR032_ES2.311_96H</th>\n",
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       "      <th>XPR032_U251MG.311_96H</th>\n",
       "      <th>XPR032_YAPC.311_96H</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gene</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>A1CF</th>\n",
       "      <td>-3.503245</td>\n",
       "      <td>-3.765875</td>\n",
       "      <td>-3.780756</td>\n",
       "      <td>-3.765875</td>\n",
       "      <td>-3.780756</td>\n",
       "      <td>-3.765875</td>\n",
       "      <td>-3.780756</td>\n",
       "      <td>-3.765875</td>\n",
       "      <td>-3.491060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2M</th>\n",
       "      <td>3.295646</td>\n",
       "      <td>2.945108</td>\n",
       "      <td>3.127292</td>\n",
       "      <td>-2.800266</td>\n",
       "      <td>0.046676</td>\n",
       "      <td>2.800266</td>\n",
       "      <td>3.780756</td>\n",
       "      <td>3.765875</td>\n",
       "      <td>3.383643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A4GALT</th>\n",
       "      <td>-2.880446</td>\n",
       "      <td>-3.524473</td>\n",
       "      <td>2.147096</td>\n",
       "      <td>3.765875</td>\n",
       "      <td>-2.707208</td>\n",
       "      <td>-1.062170</td>\n",
       "      <td>2.520504</td>\n",
       "      <td>0.144841</td>\n",
       "      <td>3.437351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A4GNT</th>\n",
       "      <td>-3.503245</td>\n",
       "      <td>-3.765875</td>\n",
       "      <td>-3.780756</td>\n",
       "      <td>-3.765875</td>\n",
       "      <td>-3.780756</td>\n",
       "      <td>-3.765875</td>\n",
       "      <td>-3.780756</td>\n",
       "      <td>-3.765875</td>\n",
       "      <td>-3.491060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAS</th>\n",
       "      <td>3.503245</td>\n",
       "      <td>3.765875</td>\n",
       "      <td>3.780756</td>\n",
       "      <td>3.765875</td>\n",
       "      <td>3.780756</td>\n",
       "      <td>3.765875</td>\n",
       "      <td>1.236914</td>\n",
       "      <td>3.765875</td>\n",
       "      <td>3.491060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZXDB</th>\n",
       "      <td>3.191846</td>\n",
       "      <td>-0.869048</td>\n",
       "      <td>-0.280056</td>\n",
       "      <td>2.558864</td>\n",
       "      <td>-2.240448</td>\n",
       "      <td>2.220901</td>\n",
       "      <td>0.280056</td>\n",
       "      <td>-1.738096</td>\n",
       "      <td>-2.685431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZXDC</th>\n",
       "      <td>-3.243746</td>\n",
       "      <td>-3.765875</td>\n",
       "      <td>-3.640728</td>\n",
       "      <td>-3.765875</td>\n",
       "      <td>-3.500700</td>\n",
       "      <td>0.531085</td>\n",
       "      <td>-3.780756</td>\n",
       "      <td>-3.765875</td>\n",
       "      <td>-3.491060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZYX</th>\n",
       "      <td>3.503245</td>\n",
       "      <td>3.765875</td>\n",
       "      <td>3.780756</td>\n",
       "      <td>3.765875</td>\n",
       "      <td>3.780756</td>\n",
       "      <td>3.765875</td>\n",
       "      <td>3.780756</td>\n",
       "      <td>3.765875</td>\n",
       "      <td>3.491060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZZEF1</th>\n",
       "      <td>-3.503245</td>\n",
       "      <td>-3.765875</td>\n",
       "      <td>-3.780756</td>\n",
       "      <td>-3.765875</td>\n",
       "      <td>-3.780756</td>\n",
       "      <td>-3.765875</td>\n",
       "      <td>-3.780756</td>\n",
       "      <td>-3.765875</td>\n",
       "      <td>-3.491060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZZZ3</th>\n",
       "      <td>3.503245</td>\n",
       "      <td>3.765875</td>\n",
       "      <td>3.780756</td>\n",
       "      <td>3.765875</td>\n",
       "      <td>3.780756</td>\n",
       "      <td>3.765875</td>\n",
       "      <td>3.780756</td>\n",
       "      <td>3.765875</td>\n",
       "      <td>3.276226</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12327 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        XPR032_A375.311_96H  XPR032_A549.311_96H  XPR032_AGS.311_96H  \\\n",
       "Gene                                                                   \n",
       "A1CF              -3.503245            -3.765875           -3.780756   \n",
       "A2M                3.295646             2.945108            3.127292   \n",
       "A4GALT            -2.880446            -3.524473            2.147096   \n",
       "A4GNT             -3.503245            -3.765875           -3.780756   \n",
       "AAAS               3.503245             3.765875            3.780756   \n",
       "...                     ...                  ...                 ...   \n",
       "ZXDB               3.191846            -0.869048           -0.280056   \n",
       "ZXDC              -3.243746            -3.765875           -3.640728   \n",
       "ZYX                3.503245             3.765875            3.780756   \n",
       "ZZEF1             -3.503245            -3.765875           -3.780756   \n",
       "ZZZ3               3.503245             3.765875            3.780756   \n",
       "\n",
       "        XPR032_BICR6.311_96H  XPR032_ES2.311_96H  XPR032_HT29.311_96H  \\\n",
       "Gene                                                                    \n",
       "A1CF               -3.765875           -3.780756            -3.765875   \n",
       "A2M                -2.800266            0.046676             2.800266   \n",
       "A4GALT              3.765875           -2.707208            -1.062170   \n",
       "A4GNT              -3.765875           -3.780756            -3.765875   \n",
       "AAAS                3.765875            3.780756             3.765875   \n",
       "...                      ...                 ...                  ...   \n",
       "ZXDB                2.558864           -2.240448             2.220901   \n",
       "ZXDC               -3.765875           -3.500700             0.531085   \n",
       "ZYX                 3.765875            3.780756             3.765875   \n",
       "ZZEF1              -3.765875           -3.780756            -3.765875   \n",
       "ZZZ3                3.765875            3.780756             3.765875   \n",
       "\n",
       "        XPR032_PC3.311B_96H  XPR032_U251MG.311_96H  XPR032_YAPC.311_96H  \n",
       "Gene                                                                     \n",
       "A1CF              -3.780756              -3.765875            -3.491060  \n",
       "A2M                3.780756               3.765875             3.383643  \n",
       "A4GALT             2.520504               0.144841             3.437351  \n",
       "A4GNT             -3.780756              -3.765875            -3.491060  \n",
       "AAAS               1.236914               3.765875             3.491060  \n",
       "...                     ...                    ...                  ...  \n",
       "ZXDB               0.280056              -1.738096            -2.685431  \n",
       "ZXDC              -3.780756              -3.765875            -3.491060  \n",
       "ZYX                3.780756               3.765875             3.491060  \n",
       "ZZEF1             -3.780756              -3.765875            -3.491060  \n",
       "ZZZ3               3.780756               3.765875             3.276226  \n",
       "\n",
       "[12327 rows x 9 columns]"
      ]
     },
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    {
     "data": {
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       "All TTEST batch signatures"
      ],
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       "      <th></th>\n",
       "      <th>XPR032_A375.311_96H</th>\n",
       "      <th>XPR032_A549.311_96H</th>\n",
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       "    <tr>\n",
       "      <th>Gene</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>A1CF</th>\n",
       "      <td>-38.394518</td>\n",
       "      <td>-59.336088</td>\n",
       "      <td>-22.471776</td>\n",
       "      <td>-89.859204</td>\n",
       "      <td>-54.234035</td>\n",
       "      <td>-70.422254</td>\n",
       "      <td>-39.011150</td>\n",
       "      <td>-33.182065</td>\n",
       "      <td>-22.360615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2M</th>\n",
       "      <td>4.135173</td>\n",
       "      <td>3.157766</td>\n",
       "      <td>3.538411</td>\n",
       "      <td>-3.938575</td>\n",
       "      <td>-0.010708</td>\n",
       "      <td>2.634366</td>\n",
       "      <td>7.741061</td>\n",
       "      <td>13.162440</td>\n",
       "      <td>6.027080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A4GALT</th>\n",
       "      <td>-3.436719</td>\n",
       "      <td>-7.187708</td>\n",
       "      <td>3.894034</td>\n",
       "      <td>12.137406</td>\n",
       "      <td>-4.919971</td>\n",
       "      <td>-0.479294</td>\n",
       "      <td>4.140085</td>\n",
       "      <td>0.139709</td>\n",
       "      <td>7.239038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A4GNT</th>\n",
       "      <td>-53.340079</td>\n",
       "      <td>-59.180625</td>\n",
       "      <td>-54.150132</td>\n",
       "      <td>-100.037554</td>\n",
       "      <td>-82.525375</td>\n",
       "      <td>-78.971904</td>\n",
       "      <td>-80.393137</td>\n",
       "      <td>-74.348675</td>\n",
       "      <td>-54.954518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAS</th>\n",
       "      <td>8.339641</td>\n",
       "      <td>16.947541</td>\n",
       "      <td>12.632247</td>\n",
       "      <td>7.517854</td>\n",
       "      <td>10.890742</td>\n",
       "      <td>4.614468</td>\n",
       "      <td>1.197955</td>\n",
       "      <td>9.285277</td>\n",
       "      <td>9.381437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZXDB</th>\n",
       "      <td>4.810296</td>\n",
       "      <td>-1.070954</td>\n",
       "      <td>-0.211440</td>\n",
       "      <td>3.026844</td>\n",
       "      <td>-3.042069</td>\n",
       "      <td>2.478141</td>\n",
       "      <td>0.778652</td>\n",
       "      <td>-2.275491</td>\n",
       "      <td>-4.308597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZXDC</th>\n",
       "      <td>-6.237979</td>\n",
       "      <td>-20.850773</td>\n",
       "      <td>-7.439808</td>\n",
       "      <td>-12.624876</td>\n",
       "      <td>-10.083182</td>\n",
       "      <td>1.184002</td>\n",
       "      <td>-15.129020</td>\n",
       "      <td>-23.473938</td>\n",
       "      <td>-14.627460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZYX</th>\n",
       "      <td>28.495121</td>\n",
       "      <td>21.626992</td>\n",
       "      <td>13.667504</td>\n",
       "      <td>22.286669</td>\n",
       "      <td>53.425426</td>\n",
       "      <td>22.670495</td>\n",
       "      <td>34.771121</td>\n",
       "      <td>36.790022</td>\n",
       "      <td>12.940510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZZEF1</th>\n",
       "      <td>-19.685782</td>\n",
       "      <td>-38.108146</td>\n",
       "      <td>-14.730522</td>\n",
       "      <td>-22.842781</td>\n",
       "      <td>-25.259714</td>\n",
       "      <td>-28.075469</td>\n",
       "      <td>-17.625887</td>\n",
       "      <td>-21.861153</td>\n",
       "      <td>-17.152609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZZZ3</th>\n",
       "      <td>14.697477</td>\n",
       "      <td>19.675990</td>\n",
       "      <td>13.887786</td>\n",
       "      <td>14.393348</td>\n",
       "      <td>31.758904</td>\n",
       "      <td>15.307763</td>\n",
       "      <td>14.994530</td>\n",
       "      <td>11.902436</td>\n",
       "      <td>5.515988</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12327 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        XPR032_A375.311_96H  XPR032_A549.311_96H  XPR032_AGS.311_96H  \\\n",
       "Gene                                                                   \n",
       "A1CF             -38.394518           -59.336088          -22.471776   \n",
       "A2M                4.135173             3.157766            3.538411   \n",
       "A4GALT            -3.436719            -7.187708            3.894034   \n",
       "A4GNT            -53.340079           -59.180625          -54.150132   \n",
       "AAAS               8.339641            16.947541           12.632247   \n",
       "...                     ...                  ...                 ...   \n",
       "ZXDB               4.810296            -1.070954           -0.211440   \n",
       "ZXDC              -6.237979           -20.850773           -7.439808   \n",
       "ZYX               28.495121            21.626992           13.667504   \n",
       "ZZEF1            -19.685782           -38.108146          -14.730522   \n",
       "ZZZ3              14.697477            19.675990           13.887786   \n",
       "\n",
       "        XPR032_BICR6.311_96H  XPR032_ES2.311_96H  XPR032_HT29.311_96H  \\\n",
       "Gene                                                                    \n",
       "A1CF              -89.859204          -54.234035           -70.422254   \n",
       "A2M                -3.938575           -0.010708             2.634366   \n",
       "A4GALT             12.137406           -4.919971            -0.479294   \n",
       "A4GNT            -100.037554          -82.525375           -78.971904   \n",
       "AAAS                7.517854           10.890742             4.614468   \n",
       "...                      ...                 ...                  ...   \n",
       "ZXDB                3.026844           -3.042069             2.478141   \n",
       "ZXDC              -12.624876          -10.083182             1.184002   \n",
       "ZYX                22.286669           53.425426            22.670495   \n",
       "ZZEF1             -22.842781          -25.259714           -28.075469   \n",
       "ZZZ3               14.393348           31.758904            15.307763   \n",
       "\n",
       "        XPR032_PC3.311B_96H  XPR032_U251MG.311_96H  XPR032_YAPC.311_96H  \n",
       "Gene                                                                     \n",
       "A1CF             -39.011150             -33.182065           -22.360615  \n",
       "A2M                7.741061              13.162440             6.027080  \n",
       "A4GALT             4.140085               0.139709             7.239038  \n",
       "A4GNT            -80.393137             -74.348675           -54.954518  \n",
       "AAAS               1.197955               9.285277             9.381437  \n",
       "...                     ...                    ...                  ...  \n",
       "ZXDB               0.778652              -2.275491            -4.308597  \n",
       "ZXDC             -15.129020             -23.473938           -14.627460  \n",
       "ZYX               34.771121              36.790022            12.940510  \n",
       "ZZEF1            -17.625887             -21.861153           -17.152609  \n",
       "ZZZ3              14.994530              11.902436             5.515988  \n",
       "\n",
       "[12327 rows x 9 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "All FC batch signatures"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>XPR032_A375.311_96H</th>\n",
       "      <th>XPR032_A549.311_96H</th>\n",
       "      <th>XPR032_AGS.311_96H</th>\n",
       "      <th>XPR032_BICR6.311_96H</th>\n",
       "      <th>XPR032_ES2.311_96H</th>\n",
       "      <th>XPR032_HT29.311_96H</th>\n",
       "      <th>XPR032_PC3.311B_96H</th>\n",
       "      <th>XPR032_U251MG.311_96H</th>\n",
       "      <th>XPR032_YAPC.311_96H</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gene</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>A1CF</th>\n",
       "      <td>-1.321133</td>\n",
       "      <td>-1.004316</td>\n",
       "      <td>-1.246867</td>\n",
       "      <td>-1.690554</td>\n",
       "      <td>-1.665912</td>\n",
       "      <td>-1.224026</td>\n",
       "      <td>-1.630469</td>\n",
       "      <td>-1.179453</td>\n",
       "      <td>-1.056458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2M</th>\n",
       "      <td>0.319610</td>\n",
       "      <td>0.205168</td>\n",
       "      <td>0.278372</td>\n",
       "      <td>-0.272317</td>\n",
       "      <td>0.000566</td>\n",
       "      <td>0.240568</td>\n",
       "      <td>0.646086</td>\n",
       "      <td>0.501869</td>\n",
       "      <td>0.393116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A4GALT</th>\n",
       "      <td>-0.065473</td>\n",
       "      <td>-0.146714</td>\n",
       "      <td>0.067911</td>\n",
       "      <td>0.248012</td>\n",
       "      <td>-0.157861</td>\n",
       "      <td>-0.022088</td>\n",
       "      <td>0.139092</td>\n",
       "      <td>0.003921</td>\n",
       "      <td>0.184544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A4GNT</th>\n",
       "      <td>-1.089672</td>\n",
       "      <td>-1.144151</td>\n",
       "      <td>-1.126503</td>\n",
       "      <td>-1.158497</td>\n",
       "      <td>-1.293550</td>\n",
       "      <td>-1.185962</td>\n",
       "      <td>-1.172858</td>\n",
       "      <td>-1.301919</td>\n",
       "      <td>-0.852535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAS</th>\n",
       "      <td>0.278446</td>\n",
       "      <td>0.270815</td>\n",
       "      <td>0.240285</td>\n",
       "      <td>0.096369</td>\n",
       "      <td>0.260568</td>\n",
       "      <td>0.179558</td>\n",
       "      <td>0.036423</td>\n",
       "      <td>0.311551</td>\n",
       "      <td>0.198877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZXDB</th>\n",
       "      <td>0.108461</td>\n",
       "      <td>-0.018144</td>\n",
       "      <td>-0.004145</td>\n",
       "      <td>0.057488</td>\n",
       "      <td>-0.096245</td>\n",
       "      <td>0.053364</td>\n",
       "      <td>0.019388</td>\n",
       "      <td>-0.036942</td>\n",
       "      <td>-0.078879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZXDC</th>\n",
       "      <td>-0.265663</td>\n",
       "      <td>-0.705461</td>\n",
       "      <td>-0.307902</td>\n",
       "      <td>-0.273159</td>\n",
       "      <td>-0.435184</td>\n",
       "      <td>0.042980</td>\n",
       "      <td>-0.509716</td>\n",
       "      <td>-0.574593</td>\n",
       "      <td>-0.251885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZYX</th>\n",
       "      <td>1.060365</td>\n",
       "      <td>0.651585</td>\n",
       "      <td>0.832980</td>\n",
       "      <td>0.741397</td>\n",
       "      <td>1.233811</td>\n",
       "      <td>0.630866</td>\n",
       "      <td>1.058643</td>\n",
       "      <td>0.963777</td>\n",
       "      <td>0.554451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZZEF1</th>\n",
       "      <td>-0.679210</td>\n",
       "      <td>-0.787376</td>\n",
       "      <td>-0.488814</td>\n",
       "      <td>-0.614017</td>\n",
       "      <td>-0.653738</td>\n",
       "      <td>-0.670349</td>\n",
       "      <td>-0.583244</td>\n",
       "      <td>-0.635765</td>\n",
       "      <td>-0.674994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZZZ3</th>\n",
       "      <td>0.403328</td>\n",
       "      <td>0.434186</td>\n",
       "      <td>0.218489</td>\n",
       "      <td>0.416350</td>\n",
       "      <td>0.805939</td>\n",
       "      <td>0.355360</td>\n",
       "      <td>0.467729</td>\n",
       "      <td>0.357720</td>\n",
       "      <td>0.165330</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12327 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        XPR032_A375.311_96H  XPR032_A549.311_96H  XPR032_AGS.311_96H  \\\n",
       "Gene                                                                   \n",
       "A1CF              -1.321133            -1.004316           -1.246867   \n",
       "A2M                0.319610             0.205168            0.278372   \n",
       "A4GALT            -0.065473            -0.146714            0.067911   \n",
       "A4GNT             -1.089672            -1.144151           -1.126503   \n",
       "AAAS               0.278446             0.270815            0.240285   \n",
       "...                     ...                  ...                 ...   \n",
       "ZXDB               0.108461            -0.018144           -0.004145   \n",
       "ZXDC              -0.265663            -0.705461           -0.307902   \n",
       "ZYX                1.060365             0.651585            0.832980   \n",
       "ZZEF1             -0.679210            -0.787376           -0.488814   \n",
       "ZZZ3               0.403328             0.434186            0.218489   \n",
       "\n",
       "        XPR032_BICR6.311_96H  XPR032_ES2.311_96H  XPR032_HT29.311_96H  \\\n",
       "Gene                                                                    \n",
       "A1CF               -1.690554           -1.665912            -1.224026   \n",
       "A2M                -0.272317            0.000566             0.240568   \n",
       "A4GALT              0.248012           -0.157861            -0.022088   \n",
       "A4GNT              -1.158497           -1.293550            -1.185962   \n",
       "AAAS                0.096369            0.260568             0.179558   \n",
       "...                      ...                 ...                  ...   \n",
       "ZXDB                0.057488           -0.096245             0.053364   \n",
       "ZXDC               -0.273159           -0.435184             0.042980   \n",
       "ZYX                 0.741397            1.233811             0.630866   \n",
       "ZZEF1              -0.614017           -0.653738            -0.670349   \n",
       "ZZZ3                0.416350            0.805939             0.355360   \n",
       "\n",
       "        XPR032_PC3.311B_96H  XPR032_U251MG.311_96H  XPR032_YAPC.311_96H  \n",
       "Gene                                                                     \n",
       "A1CF              -1.630469              -1.179453            -1.056458  \n",
       "A2M                0.646086               0.501869             0.393116  \n",
       "A4GALT             0.139092               0.003921             0.184544  \n",
       "A4GNT             -1.172858              -1.301919            -0.852535  \n",
       "AAAS               0.036423               0.311551             0.198877  \n",
       "...                     ...                    ...                  ...  \n",
       "ZXDB               0.019388              -0.036942            -0.078879  \n",
       "ZXDC              -0.509716              -0.574593            -0.251885  \n",
       "ZYX                1.058643               0.963777             0.554451  \n",
       "ZZEF1             -0.583244              -0.635765            -0.674994  \n",
       "ZZZ3               0.467729               0.357720             0.165330  \n",
       "\n",
       "[12327 rows x 9 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_signatures = {}\n",
    "\n",
    "all_signatures['cd_all']= pd.concat([\n",
    "    df['CD-coefficient'].rename(b) for (b, df) in batch_signatures['cd'].items()\n",
    "], axis=1).sort_index().rename_axis('Gene')\n",
    "all_signatures['limma_all'] = pd.concat([\n",
    "    df['t'].rename(b) for (b, df) in batch_signatures['limma'].items()\n",
    "], axis=1).sort_index().rename_axis('Gene')\n",
    "all_signatures['limma-voom_all'] = pd.concat([\n",
    "    df['t'].rename(b) for (b, df) in batch_signatures['limma-voom'].items()\n",
    "], axis=1).sort_index().rename_axis('Gene')\n",
    "all_signatures['ranksum_all'] = pd.concat([\n",
    "    df['Statistic'].rename(b) for (b, df) in batch_signatures['ranksum'].items()\n",
    "], axis=1).sort_index().rename_axis('Gene')\n",
    "all_signatures['ttest_all'] = pd.concat([\n",
    "    df['Statistic'].rename(b) for (b, df) in batch_signatures['ttest'].items()\n",
    "], axis=1).sort_index().rename_axis('Gene')\n",
    "all_signatures['fc_all'] = pd.concat([\n",
    "    df['logFC'].rename(b) for (b, df) in batch_signatures['fc'].items()\n",
    "], axis=1).sort_index().rename_axis('Gene')\n",
    "\n",
    "for k in all_signatures.keys(): \n",
    "    method = k.split('_')[0].upper()\n",
    "    display(Markdown(f\"All {method} batch signatures\"))\n",
    "    display(all_signatures[k])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enrichment Analysis Rankings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to get Enrichr Results\n",
    "def getEnrichrLibrary(library_name): \n",
    "    ENRICHR_URL = f'https://maayanlab.cloud/Enrichr/geneSetLibrary?mode=json&libraryName={library_name}'\n",
    "    resp = requests.get(ENRICHR_URL)\n",
    "    if not resp.ok: \n",
    "        raise Exception(f\"Error downloading {library_name} library from Enrichr, please try again.\")\n",
    "    return resp.json()[library_name]['terms']\n",
    "\n",
    "def getLibraryIter(libdict):\n",
    "    for k,v in libdict.items():\n",
    "        if type(v) == list:\n",
    "            yield k, v\n",
    "        else:\n",
    "            yield k, list(v.keys())\n",
    "\n",
    "def enrich(gene_list, lib_json, name): \n",
    "    all_terms = list(lib_json.keys())\n",
    "    termranks = []\n",
    "    enrich_res = enrich_crisp(gene_list, getLibraryIter(lib_json), 20000, False)\n",
    "    enrich_res = [[r[0], r[1].pvalue] for r in enrich_res]\n",
    "    sorted_res = sorted(enrich_res, key=lambda x: x[1])\n",
    "    for i in range(len(sorted_res)): \n",
    "        termranks.append([name, sorted_res[i][0], i])\n",
    "    for t in set(all_terms).difference([x[0] for x in termranks]): \n",
    "        i+=1\n",
    "        termranks.append([name, t, i])\n",
    "    return pd.DataFrame(termranks, columns=['Gene_Set', 'Term', 'Rank'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "chea2022 = getEnrichrLibrary('ChEA_2022')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get gene lists to put into Enrichr\n",
    "gene_lists = {}\n",
    "for m in all_signatures.keys():\n",
    "    mname = m.split('_')[0]\n",
    "    gene_lists[mname] = {'up': {}, 'down': {}, 'combined': {}}\n",
    "    for col in all_signatures[m].columns: \n",
    "        gene_lists[mname]['up'][col] = all_signatures[m][col].sort_values(ascending=False).index.tolist()[:100]\n",
    "        gene_lists[mname]['down'][col] = all_signatures[m][col].sort_values(ascending=True).index.tolist()[:100]\n",
    "        gene_lists[mname]['combined'][col] = gene_lists[mname]['up'][col] + gene_lists[mname]['down'][col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get results\n",
    "chea2022_results = []\n",
    "\n",
    "for m in gene_lists.keys(): \n",
    "    for sig in gene_lists[m]['up'].keys(): \n",
    "        chea2022_results.append(enrich(gene_lists[m]['up'][sig], chea2022, f\"{sig}:{m}:up:ChEA 2022\"))\n",
    "        chea2022_results.append(enrich(gene_lists[m]['down'][sig], chea2022, f\"{sig}:{m}:down:ChEA 2022\"))\n",
    "        chea2022_results.append(enrich(gene_lists[m]['combined'][sig], chea2022, f\"{sig}:{m}:combined:ChEA 2022\"))\n",
    "\n",
    "chea2022_df = pd.concat(chea2022_results, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "dex_chea2022_df = chea2022_df[chea2022_df['Term'].apply(lambda term: ko_gene in term)]\n",
    "dex_chea2022_df['Library'] = 'ChEA 2022'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "dex_chea2022_df = dex_chea2022_df.sort_values(by=['Rank', 'Gene_Set', 'Term'], ascending=[True, True, True]).drop_duplicates(subset=['Gene_Set', 'Term'], keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def createResultsDf(df):\n",
    "  df['Method'] = df['Gene_Set'].apply(lambda x: x.split(':')[1])\n",
    "  df['Direction'] = df['Gene_Set'].apply(lambda x: x.split(':')[2])\n",
    "  df['Method_Direction'] = df.apply(lambda row: row.Method + ':' + row.Direction, axis=1)\n",
    "  df['TF'] = df['Term'].apply(lambda x: x.split(' ')[0].split('_')[0])\n",
    "  df['Cell'] = df['Gene_Set'].apply(lambda x: x.split(':')[0].split('_')[1])\n",
    "  df['Batch'] = df['Gene_Set'].apply(lambda x: x.split(':')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "createResultsDf(dex_chea2022_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_df = dex_chea2022_df\n",
    "\n",
    "up_df = full_df[full_df['Direction'] == 'up']\n",
    "down_df = full_df[full_df['Direction'] == 'down']\n",
    "combined_df = full_df[full_df['Direction'] == 'combined']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Mean ranks of NR0B1 terms for up genes from each method."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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></th>\n",
       "      <th>Rank</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Library</th>\n",
       "      <th>Method</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">ChEA 2022</th>\n",
       "      <th>cd</th>\n",
       "      <td>213.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ttest</th>\n",
       "      <td>226.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fc</th>\n",
       "      <td>263.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>limma-voom</th>\n",
       "      <td>267.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ranksum</th>\n",
       "      <td>273.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>limma</th>\n",
       "      <td>450.444444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Rank\n",
       "Library   Method                \n",
       "ChEA 2022 cd          213.888889\n",
       "          ttest       226.111111\n",
       "          fc          263.777778\n",
       "          limma-voom  267.000000\n",
       "          ranksum     273.777778\n",
       "          limma       450.444444"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Mean ranks of NR0B1 terms for down genes from each method."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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></th>\n",
       "      <th>Rank</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Library</th>\n",
       "      <th>Method</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">ChEA 2022</th>\n",
       "      <th>ranksum</th>\n",
       "      <td>330.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>limma</th>\n",
       "      <td>510.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>limma-voom</th>\n",
       "      <td>520.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cd</th>\n",
       "      <td>569.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ttest</th>\n",
       "      <td>586.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fc</th>\n",
       "      <td>592.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Rank\n",
       "Library   Method                \n",
       "ChEA 2022 ranksum     330.666667\n",
       "          limma       510.555556\n",
       "          limma-voom  520.111111\n",
       "          cd          569.888889\n",
       "          ttest       586.222222\n",
       "          fc          592.666667"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Mean ranks of Dexamethasone terms for combined up and down genes from each method."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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></th>\n",
       "      <th>Rank</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Library</th>\n",
       "      <th>Method</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">ChEA 2022</th>\n",
       "      <th>ranksum</th>\n",
       "      <td>267.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cd</th>\n",
       "      <td>385.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ttest</th>\n",
       "      <td>393.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>limma-voom</th>\n",
       "      <td>397.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fc</th>\n",
       "      <td>489.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>limma</th>\n",
       "      <td>575.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Rank\n",
       "Library   Method                \n",
       "ChEA 2022 ranksum     267.000000\n",
       "          cd          385.777778\n",
       "          ttest       393.333333\n",
       "          limma-voom  397.777778\n",
       "          fc          489.777778\n",
       "          limma       575.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Markdown(f\"Mean ranks of {ko_gene} terms for up genes from each method.\"))\n",
    "display(up_df.groupby(['Library', 'Method']).mean(numeric_only=True).sort_values(['Library', 'Rank']))\n",
    "display(Markdown(f\"Mean ranks of {ko_gene} terms for down genes from each method.\"))\n",
    "display(down_df.groupby(['Library', 'Method']).mean(numeric_only=True).sort_values(['Library', 'Rank']))\n",
    "display(Markdown(f\"Mean ranks of Dexamethasone terms for combined up and down genes from each method.\"))\n",
    "display(combined_df.groupby(['Library', 'Method']).mean(numeric_only=True).sort_values(['Library', 'Rank']))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bootstrap random results\n",
    "random_arr_chea2022 = []\n",
    "\n",
    "for i in range(10):\n",
    "    rand_100 = sample(combined_expr_df.index.tolist(), 100)\n",
    "    rand_200 = sample(combined_expr_df.index.tolist(), 200)\n",
    "\n",
    "    random_arr_chea2022.append(enrich(rand_100, chea2022, 'random:100'))\n",
    "    random_arr_chea2022.append(enrich(rand_200, chea2022, 'random:200'))\n",
    "\n",
    "rand_chea2022_df = pd.concat(random_arr_chea2022, axis=0)\n",
    "rand_chea2022_df['Library'] = 'ChEA 2022'\n",
    "rand_chea2022_df['TF'] = rand_chea2022_df['Term'].apply(lambda x: x.split(' ')[0])\n",
    "\n",
    "rand_df = pd.concat([\n",
    "    rand_chea2022_df\n",
    "], axis=1)\n",
    "rand_df = rand_df[rand_df['TF'].isin(['NR0B1',])]\n",
    "rand_df['Method'] = 'random'"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_df['Cell'] = full_df['Cell'].apply(lambda x: x.replace('.311', ''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# with ChEA manual libraries\n",
    "method_color_map = {\n",
    "    'random': 'black',\n",
    "    'cd': '#DDCC77',\n",
    "    'limma': '#332288',\n",
    "    'limma-voom': '#88CCEE',\n",
    "    'fc': '#117733',\n",
    "    'ttest': '#CC6677',\n",
    "    'ranksum': '#AA4499',\n",
    "}\n",
    "\n",
    "for l in full_df['Library'].unique(): \n",
    "\n",
    "    sub_df = full_df[full_df['Library']==l].set_index(['Method'])\n",
    "    fig1 = go.Figure()\n",
    "    for gs in sub_df.groupby('Method').mean(numeric_only=True).sort_values('Rank').index:\n",
    "        fig1.add_trace(\n",
    "            go.Box(\n",
    "                y=sub_df.loc[gs]['Rank'].tolist(),\n",
    "                name=gs,\n",
    "                marker_color=method_color_map[gs]\n",
    "            )\n",
    "        )\n",
    "    sub_rand_df = rand_df[rand_df['Library'] == l]\n",
    "    fig1.add_trace(\n",
    "        go.Box(\n",
    "            y=rand_df[rand_df['Method']=='random']['Rank'].tolist(),\n",
    "            name='random',\n",
    "            marker_color='black'\n",
    "        )\n",
    "    )\n",
    "    fig1.update_layout(\n",
    "        title_text=f\"{ko_gene} Term Rankings by Method from {l}\",\n",
    "        xaxis={\n",
    "            'title': {'text': 'Method'}, \n",
    "        },\n",
    "        yaxis={\n",
    "            'title': {'text': 'Rank'}\n",
    "        },\n",
    "        showlegend=False\n",
    "    )\n",
    "    fig1.show(\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_dict = {\n",
    "    'random': 'black',\n",
    "    'cd': '#DDCC77',\n",
    "    'limma': '#332288',\n",
    "    'limma-voom': '#88CCEE',\n",
    "    'fc': '#117733',\n",
    "    'ttest': '#CC6677',\n",
    "    'ranksum': '#AA4499',\n",
    "}\n",
    "\n",
    "fig1 = go.Figure()\n",
    "for gs in full_df.groupby('Method_Direction').mean().sort_values('Rank').index:\n",
    "    fig1.add_trace(\n",
    "        go.Box(\n",
    "            y=full_df[full_df['Method_Direction']==gs]['Rank'].tolist(),\n",
    "            name=gs.replace('fc', 'logfc'),\n",
    "            marker_color=color_dict[gs.split(':')[0]]\n",
    "        )\n",
    "    )\n",
    "fig1.add_trace(\n",
    "    go.Box(\n",
    "        y=rand_df[rand_df['Method']==f'random']['Rank'].tolist(),\n",
    "        name='random',\n",
    "        marker_color='black'\n",
    "    )\n",
    ")\n",
    "fig1.update_layout(\n",
    "    title_text=f\"{ko_gene} Term Rankings for L1000 Gene Sets by Method and Direction\",\n",
    "    xaxis={\n",
    "        'title': {'text': 'Method:Direction'}, \n",
    "    },\n",
    "    yaxis={\n",
    "        'title': {'text': 'Rank'}\n",
    "    },\n",
    "    showlegend=False\n",
    ")\n",
    "fig1.update_xaxes(tickangle=45)\n",
    "fig1.show(\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_dict = {\n",
    "    'up': '#648FFF',\n",
    "    'down': '#DC267F', \n",
    "    'combined': '#785EF0'\n",
    "}\n",
    "\n",
    "fig1 = go.Figure()\n",
    "for gs in full_df.groupby('Method_Direction').mean().sort_values('Rank').index:\n",
    "    fig1.add_trace(\n",
    "        go.Box(\n",
    "            y=full_df[full_df['Method_Direction']==gs]['Rank'].tolist(),\n",
    "            name=gs.replace('fc', 'logfc'),\n",
    "            marker_color=color_dict[gs.split(':')[1]]\n",
    "        )\n",
    "    )\n",
    "fig1.add_trace(\n",
    "    go.Box(\n",
    "        y=rand_df[rand_df['Method']==f'random']['Rank'].tolist(),\n",
    "        name='random',\n",
    "        marker_color='#000000'\n",
    "    )\n",
    ")\n",
    "fig1.update_layout(\n",
    "    title_text=f\"{ko_gene} Term Rankings for L1000 Gene Sets by Method and Direction\",\n",
    "    xaxis={\n",
    "        'title': {'text': 'Method:Direction'}, \n",
    "    },\n",
    "    yaxis={\n",
    "        'title': {'text': 'Rank'}\n",
    "    },\n",
    "    showlegend=False\n",
    ")\n",
    "fig1.update_xaxes(tickangle=45)\n",
    "fig1.show(\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "method_color_map = {\n",
    "    'up': '#648FFF',\n",
    "    'down': '#DC267F', \n",
    "    'combined': '#785EF0'\n",
    "}\n",
    "fig1 = go.Figure()\n",
    "order = full_df.groupby(['Method_Direction']).mean(numeric_only=True).sort_values('Rank').index.map(lambda x: x.split(':')[0]).unique()\n",
    "full_df['Method'] = pd.Categorical(full_df['Method'], order)\n",
    "full_df = full_df.sort_values(by=['Method'])\n",
    "\n",
    "for d in ['up', 'combined', 'down']:\n",
    "  d_df = full_df[full_df['Direction'] == d]\n",
    "  fig1.add_trace(\n",
    "    go.Box(\n",
    "      x=d_df['Method'],\n",
    "      y=d_df['Rank'],\n",
    "      name=d, \n",
    "      marker_color=method_color_map[d]\n",
    "    )\n",
    "  )\n",
    "\n",
    "fig1.add_trace(\n",
    "  go.Box(\n",
    "    x=rand_df['Method'],\n",
    "    y=rand_df['Rank'],\n",
    "    name='random',\n",
    "    marker_color='black'\n",
    "  )\n",
    ")\n",
    "\n",
    "fig1.update_layout(\n",
    "  width=800,\n",
    "  boxmode='group',\n",
    "  boxgap=0.1,\n",
    "  xaxis={\n",
    "    'title': {'text': 'Method'},\n",
    "  },\n",
    "  yaxis={\n",
    "    'title': {'text': 'Gene Set Rank'}\n",
    "  },\n",
    "  legend_title_text=\"Direction\"\n",
    ")\n",
    "fig1.show(\"png\")\n",
    "fig1.write_image(f'/Users/maayanlab/Documents/manuscripts/dex-benchmark/revised_figures/4_{ko_gene}_1_300dpi.png', scale=(800/300))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dir_color_map = {\n",
    "    'up': '#648FFF',\n",
    "    'down': '#DC267F', \n",
    "    'combined': '#785EF0'\n",
    "}\n",
    "\n",
    "full_df['Batch'] = full_df['Gene_Set'].apply(lambda x: x.split(':')[0].split('_')[1].replace('.311',''))\n",
    "for l in full_df['Library'].unique():\n",
    "    sub_df = full_df[full_df['Library'] == l].set_index(['Direction', 'Batch'])\n",
    "    fig1 = go.Figure()\n",
    "    for (d, b) in sub_df.groupby(['Direction', 'Batch']).mean(numeric_only=True).sort_values('Rank').index:\n",
    "        fig1.add_trace(\n",
    "            go.Box(\n",
    "                y=sub_df.loc[(d,b)]['Rank'].tolist(),\n",
    "                name=b + ' ' + d,\n",
    "                marker_color=dir_color_map[d]\n",
    "            )\n",
    "        )\n",
    "    sub_rand_df = rand_df[rand_df['Library'] == l]\n",
    "    fig1.add_trace(\n",
    "        go.Box(\n",
    "            y=sub_rand_df[sub_rand_df['Method']==f'random']['Rank'].tolist(),\n",
    "            name='random',\n",
    "            marker_color='black'\n",
    "        )\n",
    "    )\n",
    "    fig1.update_layout(\n",
    "        title_text=f\"{ko_gene} Term Rankings for L1000 Gene Sets by Cell Line\",\n",
    "        xaxis={\n",
    "            'title': {'text': 'Cell Line'}, \n",
    "        },\n",
    "        yaxis={\n",
    "            'title': {'text': 'Rank'}\n",
    "        },\n",
    "        showlegend=False\n",
    ")\n",
    "fig1.update_xaxes(tickangle=45)\n",
    "fig1.show(\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "method_color_map = {\n",
    "    'up': '#648FFF',\n",
    "    'down': '#DC267F', \n",
    "    'combined': '#785EF0'\n",
    "}\n",
    "fig1 = go.Figure()\n",
    "order = full_df.groupby(['Direction', 'Cell']).mean(numeric_only=True).sort_values('Rank').index.map(lambda x: x[1].split(':')[0]).unique()\n",
    "full_df['Cell'] = pd.Categorical(full_df['Cell'], order)\n",
    "full_df = full_df.sort_values(by=['Cell'])\n",
    "\n",
    "for d in ['up', 'combined', 'down']:\n",
    "  d_df = full_df[full_df['Direction'] == d]\n",
    "  fig1.add_trace(\n",
    "    go.Box(\n",
    "      x=d_df['Cell'],\n",
    "      y=d_df['Rank'],\n",
    "      name=d, \n",
    "      marker_color=method_color_map[d]\n",
    "    )\n",
    "  )\n",
    "\n",
    "fig1.add_trace(\n",
    "  go.Box(\n",
    "    x=rand_df['Method'],\n",
    "    y=rand_df['Rank'],\n",
    "    name='random',\n",
    "    marker_color='black'\n",
    "  )\n",
    ")\n",
    "\n",
    "fig1.update_layout(\n",
    "  width=800,\n",
    "  boxmode='group',\n",
    "  boxgap=0.1,\n",
    "  xaxis={\n",
    "    'title': {'text': 'Cell Line'},\n",
    "  },\n",
    "  yaxis={\n",
    "    'title': {'text': 'Gene Set Rank'}\n",
    "  },\n",
    "  legend_title_text=\"Direction\"\n",
    ")\n",
    "fig1.show(\"png\")\n",
    "fig1.write_image(f'/Users/maayanlab/Documents/manuscripts/dex-benchmark/revised_figures/4_{ko_gene}_2_300dpi.png', scale=(800/300))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ScatterEnv",
   "language": "python",
   "name": "scatterenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "59b903cdca14fb863026e39f4185dd43265f1412df959e516078f4f22f35cec9"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}