{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "71b68588",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/icb/yuge.ji/miniconda3/envs/py37/lib/python3.7/site-packages/scanpy/_settings.py:447: DeprecationWarning: `set_matplotlib_formats` is deprecated since IPython 7.23, directly use `matplotlib_inline.backend_inline.set_matplotlib_formats()`\n",
      "  IPython.display.set_matplotlib_formats(*ipython_format)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import scanpy as sc\n",
    "sc.set_figure_params(dpi=100, frameon=False, color_map='Reds')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48d1b033",
   "metadata": {},
   "source": [
    "## Statistics on tools\n",
    "\n",
    "From scrna-tools.org."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "32dffb03",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('perturbation-tools.tsv', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1a394f56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tool</th>\n",
       "      <th>Platform</th>\n",
       "      <th>Code</th>\n",
       "      <th>Description</th>\n",
       "      <th>License</th>\n",
       "      <th>Added</th>\n",
       "      <th>Updated</th>\n",
       "      <th>PlatformR</th>\n",
       "      <th>PlatformPy</th>\n",
       "      <th>PlatformCPP</th>\n",
       "      <th>...</th>\n",
       "      <th>GHLogins</th>\n",
       "      <th>GHCommits</th>\n",
       "      <th>GHIssues</th>\n",
       "      <th>GHClosedIssues</th>\n",
       "      <th>GHPctIssuesClosed</th>\n",
       "      <th>GHMedianResponseDays</th>\n",
       "      <th>GHMedianClosedDays</th>\n",
       "      <th>GHIssueActivity</th>\n",
       "      <th>GHIssueResponse</th>\n",
       "      <th>GHPopularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Augur</td>\n",
       "      <td>R</td>\n",
       "      <td>https://github.com/neurorestore/Augur</td>\n",
       "      <td>Augur is an R package to prioritize cell types...</td>\n",
       "      <td>MIT</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>2021-07-02</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>1.180486</td>\n",
       "      <td>4.585359</td>\n",
       "      <td>0.430587</td>\n",
       "      <td>3.169630</td>\n",
       "      <td>1.431245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beyondcell</td>\n",
       "      <td>R</td>\n",
       "      <td>https://gitlab.com/bu_cnio/beyondcell</td>\n",
       "      <td>Beyondcell is a computational methodology for ...</td>\n",
       "      <td>GPL-2.0</td>\n",
       "      <td>2021-04-12</td>\n",
       "      <td>2021-04-12</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CellOracle</td>\n",
       "      <td>python</td>\n",
       "      <td>https://github.com/morris-lab/CellOracle</td>\n",
       "      <td>CellOracle integrates single-cell transcriptom...</td>\n",
       "      <td>Apache-2.0</td>\n",
       "      <td>2021-01-25</td>\n",
       "      <td>2021-01-25</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>257.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>96.610169</td>\n",
       "      <td>0.385810</td>\n",
       "      <td>8.786991</td>\n",
       "      <td>1.472808</td>\n",
       "      <td>3.655317</td>\n",
       "      <td>1.723191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CPA</td>\n",
       "      <td>Python</td>\n",
       "      <td>https://github.com/facebookresearch/CPA</td>\n",
       "      <td>CPA is a deep generative framework to learn ef...</td>\n",
       "      <td>MIT</td>\n",
       "      <td>2021-04-16</td>\n",
       "      <td>2021-04-16</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.510862</td>\n",
       "      <td>3.944132</td>\n",
       "      <td>0.721092</td>\n",
       "      <td>3.533387</td>\n",
       "      <td>2.549845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MELD</td>\n",
       "      <td>Python</td>\n",
       "      <td>https://github.com/KrishnaswamyLab/MELD</td>\n",
       "      <td>MELD (Manifold Enhancement of Latent Dimension...</td>\n",
       "      <td>GPL-3.0</td>\n",
       "      <td>2019-02-01</td>\n",
       "      <td>2021-03-19</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>336.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>81.818182</td>\n",
       "      <td>0.703079</td>\n",
       "      <td>10.855683</td>\n",
       "      <td>0.525024</td>\n",
       "      <td>3.394687</td>\n",
       "      <td>1.307943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MIMOSCA</td>\n",
       "      <td>Python</td>\n",
       "      <td>https://github.com/asncd/MIMOSCA</td>\n",
       "      <td>Multiple Input Multiple Output Single Cell Ana...</td>\n",
       "      <td>MIT</td>\n",
       "      <td>2018-11-15</td>\n",
       "      <td>2021-01-12</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>243.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>0.072697</td>\n",
       "      <td>9.958241</td>\n",
       "      <td>0.306348</td>\n",
       "      <td>4.380176</td>\n",
       "      <td>1.441548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MUSIC</td>\n",
       "      <td>R</td>\n",
       "      <td>https://github.com/bm2-lab/MUSIC</td>\n",
       "      <td>MUSIC: Model-based Understanding of SIngle-cel...</td>\n",
       "      <td>Apache-2.0</td>\n",
       "      <td>2019-05-31</td>\n",
       "      <td>2021-01-12</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>114.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>317.077650</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.740525</td>\n",
       "      <td>1.175149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PhEMD</td>\n",
       "      <td>R</td>\n",
       "      <td>https://github.com/KrishnaswamyLab/phemd</td>\n",
       "      <td>PhEMD (phenotypic earth mover's distance) iden...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-01-25</td>\n",
       "      <td>2021-01-25</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>7.549664</td>\n",
       "      <td>16.165671</td>\n",
       "      <td>0.402925</td>\n",
       "      <td>2.363763</td>\n",
       "      <td>0.813173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PopAlign</td>\n",
       "      <td>python</td>\n",
       "      <td>https://github.com/thomsonlab/popalign</td>\n",
       "      <td>PopAlign constructs a compressed representatio...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-01-25</td>\n",
       "      <td>2021-01-25</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>265.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.679630</td>\n",
       "      <td>46.842801</td>\n",
       "      <td>0.326758</td>\n",
       "      <td>3.409418</td>\n",
       "      <td>0.844124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PRESCIENT</td>\n",
       "      <td>Python</td>\n",
       "      <td>https://github.com/gifford-lab/prescient</td>\n",
       "      <td>PRESCIENT (Potential eneRgy undErlying Single ...</td>\n",
       "      <td>MIT</td>\n",
       "      <td>2020-09-10</td>\n",
       "      <td>2021-06-04</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>2.081644</td>\n",
       "      <td>6.324039</td>\n",
       "      <td>0.283095</td>\n",
       "      <td>2.923284</td>\n",
       "      <td>1.196050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>SCATTome</td>\n",
       "      <td>R</td>\n",
       "      <td>https://github.com/bvnlab/SCATTome</td>\n",
       "      <td>SCATTome (Single Cell Analysis of Targeted Tra...</td>\n",
       "      <td>GPL-3.0</td>\n",
       "      <td>2021-01-25</td>\n",
       "      <td>2021-01-25</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.256736</td>\n",
       "      <td>83.250637</td>\n",
       "      <td>0.068492</td>\n",
       "      <td>3.832204</td>\n",
       "      <td>0.127640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>scDEAL</td>\n",
       "      <td>Python</td>\n",
       "      <td>https://github.com/OSU-BMBL/scDEAL</td>\n",
       "      <td>Deep Transfer Learning of Drug Sensitivity by ...</td>\n",
       "      <td>Apache-2.0</td>\n",
       "      <td>2021-08-08</td>\n",
       "      <td>2021-08-08</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.700971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>scGen</td>\n",
       "      <td>Python</td>\n",
       "      <td>https://github.com/theislab/scgen</td>\n",
       "      <td>scGen is a generative model to predict single-...</td>\n",
       "      <td>GPL-3.0</td>\n",
       "      <td>2019-08-05</td>\n",
       "      <td>2021-01-12</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>361.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>8.797106</td>\n",
       "      <td>37.129219</td>\n",
       "      <td>1.199783</td>\n",
       "      <td>2.297351</td>\n",
       "      <td>1.967889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>scMAGeCK</td>\n",
       "      <td>R/Python</td>\n",
       "      <td>https://bitbucket.org/weililab/scmageck</td>\n",
       "      <td>scMAGeCK is a computational model to identify ...</td>\n",
       "      <td>BSD-2-Clause</td>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>2021-01-12</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>scTenifoldKnk</td>\n",
       "      <td>R/Python/MATLAB</td>\n",
       "      <td>https://github.com/cailab-tamu/scTenifoldKnk</td>\n",
       "      <td>Perform virtual knockout experiments on single...</td>\n",
       "      <td>GPL-2.0-or-later</td>\n",
       "      <td>2021-03-26</td>\n",
       "      <td>2021-07-27</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>239.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.185932</td>\n",
       "      <td>0.594566</td>\n",
       "      <td>0.694318</td>\n",
       "      <td>3.972337</td>\n",
       "      <td>1.061626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>trVAE</td>\n",
       "      <td>Python</td>\n",
       "      <td>https://github.com/theislab/trVAE</td>\n",
       "      <td>trVAE is a deep generative model which learns ...</td>\n",
       "      <td>MIT</td>\n",
       "      <td>2019-11-03</td>\n",
       "      <td>2021-06-28</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>843.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>3.857726</td>\n",
       "      <td>18.720347</td>\n",
       "      <td>0.640489</td>\n",
       "      <td>2.655359</td>\n",
       "      <td>1.367855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Tool         Platform  \\\n",
       "0           Augur                R   \n",
       "1      Beyondcell                R   \n",
       "2      CellOracle           python   \n",
       "3             CPA           Python   \n",
       "4            MELD           Python   \n",
       "5         MIMOSCA           Python   \n",
       "6           MUSIC                R   \n",
       "7           PhEMD                R   \n",
       "8        PopAlign           python   \n",
       "9       PRESCIENT           Python   \n",
       "10       SCATTome                R   \n",
       "11         scDEAL           Python   \n",
       "12          scGen           Python   \n",
       "13       scMAGeCK         R/Python   \n",
       "14  scTenifoldKnk  R/Python/MATLAB   \n",
       "15          trVAE           Python   \n",
       "\n",
       "                                            Code  \\\n",
       "0          https://github.com/neurorestore/Augur   \n",
       "1          https://gitlab.com/bu_cnio/beyondcell   \n",
       "2       https://github.com/morris-lab/CellOracle   \n",
       "3        https://github.com/facebookresearch/CPA   \n",
       "4        https://github.com/KrishnaswamyLab/MELD   \n",
       "5               https://github.com/asncd/MIMOSCA   \n",
       "6               https://github.com/bm2-lab/MUSIC   \n",
       "7       https://github.com/KrishnaswamyLab/phemd   \n",
       "8         https://github.com/thomsonlab/popalign   \n",
       "9       https://github.com/gifford-lab/prescient   \n",
       "10            https://github.com/bvnlab/SCATTome   \n",
       "11            https://github.com/OSU-BMBL/scDEAL   \n",
       "12             https://github.com/theislab/scgen   \n",
       "13       https://bitbucket.org/weililab/scmageck   \n",
       "14  https://github.com/cailab-tamu/scTenifoldKnk   \n",
       "15             https://github.com/theislab/trVAE   \n",
       "\n",
       "                                          Description           License  \\\n",
       "0   Augur is an R package to prioritize cell types...               MIT   \n",
       "1   Beyondcell is a computational methodology for ...           GPL-2.0   \n",
       "2   CellOracle integrates single-cell transcriptom...        Apache-2.0   \n",
       "3   CPA is a deep generative framework to learn ef...               MIT   \n",
       "4   MELD (Manifold Enhancement of Latent Dimension...           GPL-3.0   \n",
       "5   Multiple Input Multiple Output Single Cell Ana...               MIT   \n",
       "6   MUSIC: Model-based Understanding of SIngle-cel...        Apache-2.0   \n",
       "7   PhEMD (phenotypic earth mover's distance) iden...               NaN   \n",
       "8   PopAlign constructs a compressed representatio...               NaN   \n",
       "9   PRESCIENT (Potential eneRgy undErlying Single ...               MIT   \n",
       "10  SCATTome (Single Cell Analysis of Targeted Tra...           GPL-3.0   \n",
       "11  Deep Transfer Learning of Drug Sensitivity by ...        Apache-2.0   \n",
       "12  scGen is a generative model to predict single-...           GPL-3.0   \n",
       "13  scMAGeCK is a computational model to identify ...      BSD-2-Clause   \n",
       "14  Perform virtual knockout experiments on single...  GPL-2.0-or-later   \n",
       "15  trVAE is a deep generative model which learns ...               MIT   \n",
       "\n",
       "         Added     Updated  PlatformR  PlatformPy  PlatformCPP  ...  GHLogins  \\\n",
       "0   2020-01-02  2021-07-02       True       False        False  ...       NaN   \n",
       "1   2021-04-12  2021-04-12       True       False        False  ...       NaN   \n",
       "2   2021-01-25  2021-01-25      False       False        False  ...       NaN   \n",
       "3   2021-04-16  2021-04-16      False        True        False  ...       NaN   \n",
       "4   2019-02-01  2021-03-19      False        True        False  ...       NaN   \n",
       "5   2018-11-15  2021-01-12      False        True        False  ...       NaN   \n",
       "6   2019-05-31  2021-01-12       True       False        False  ...       NaN   \n",
       "7   2021-01-25  2021-01-25       True       False        False  ...       NaN   \n",
       "8   2021-01-25  2021-01-25      False       False        False  ...       NaN   \n",
       "9   2020-09-10  2021-06-04      False        True        False  ...       NaN   \n",
       "10  2021-01-25  2021-01-25       True       False        False  ...       NaN   \n",
       "11  2021-08-08  2021-08-08      False        True        False  ...       NaN   \n",
       "12  2019-08-05  2021-01-12      False        True        False  ...       NaN   \n",
       "13  2020-02-05  2021-01-12       True        True        False  ...       NaN   \n",
       "14  2021-03-26  2021-07-27       True        True        False  ...       NaN   \n",
       "15  2019-11-03  2021-06-28      False        True        False  ...       NaN   \n",
       "\n",
       "    GHCommits  GHIssues  GHClosedIssues  GHPctIssuesClosed  \\\n",
       "0        14.0      12.0             3.0          25.000000   \n",
       "1         NaN       NaN             NaN                NaN   \n",
       "2       257.0      59.0            57.0          96.610169   \n",
       "3         7.0       4.0             2.0          50.000000   \n",
       "4       336.0      11.0             9.0          81.818182   \n",
       "5       243.0       8.0             5.0          62.500000   \n",
       "6       114.0       2.0             0.0           0.000000   \n",
       "7        10.0       5.0             5.0         100.000000   \n",
       "8       265.0       5.0             3.0          60.000000   \n",
       "9        87.0       1.0             1.0         100.000000   \n",
       "10       26.0       1.0             1.0         100.000000   \n",
       "11        2.0       0.0             0.0                NaN   \n",
       "12      361.0      42.0            42.0         100.000000   \n",
       "13        NaN       NaN             NaN                NaN   \n",
       "14      239.0       6.0             6.0         100.000000   \n",
       "15      843.0      10.0             8.0          80.000000   \n",
       "\n",
       "    GHMedianResponseDays  GHMedianClosedDays  GHIssueActivity  \\\n",
       "0               1.180486            4.585359         0.430587   \n",
       "1                    NaN                 NaN              NaN   \n",
       "2               0.385810            8.786991         1.472808   \n",
       "3               0.510862            3.944132         0.721092   \n",
       "4               0.703079           10.855683         0.525024   \n",
       "5               0.072697            9.958241         0.306348   \n",
       "6             317.077650                 NaN         0.000000   \n",
       "7               7.549664           16.165671         0.402925   \n",
       "8               0.679630           46.842801         0.326758   \n",
       "9               2.081644            6.324039         0.283095   \n",
       "10              0.256736           83.250637         0.068492   \n",
       "11                   NaN                 NaN         0.000000   \n",
       "12              8.797106           37.129219         1.199783   \n",
       "13                   NaN                 NaN              NaN   \n",
       "14              0.185932            0.594566         0.694318   \n",
       "15              3.857726           18.720347         0.640489   \n",
       "\n",
       "    GHIssueResponse  GHPopularity  \n",
       "0          3.169630      1.431245  \n",
       "1               NaN           NaN  \n",
       "2          3.655317      1.723191  \n",
       "3          3.533387      2.549845  \n",
       "4          3.394687      1.307943  \n",
       "5          4.380176      1.441548  \n",
       "6          0.740525      1.175149  \n",
       "7          2.363763      0.813173  \n",
       "8          3.409418      0.844124  \n",
       "9          2.923284      1.196050  \n",
       "10         3.832204      0.127640  \n",
       "11              NaN      1.700971  \n",
       "12         2.297351      1.967889  \n",
       "13              NaN           NaN  \n",
       "14         3.972337      1.061626  \n",
       "15         2.655359      1.367855  \n",
       "\n",
       "[16 rows x 46 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "23ff3695",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Platform'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 324,
       "width": 377
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Platform.replace({'python':'Python'}).value_counts().plot(kind='pie', startangle=290)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc016a4f",
   "metadata": {},
   "source": [
    "## Statistics on datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "41569562",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('personal.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2b9c220d",
   "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>DOI</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Technique</th>\n",
       "      <th>Shorthand</th>\n",
       "      <th>Measurement</th>\n",
       "      <th># perturbations</th>\n",
       "      <th># cell types</th>\n",
       "      <th># doses</th>\n",
       "      <th># timepoints</th>\n",
       "      <th>Cell source</th>\n",
       "      <th>Availability</th>\n",
       "      <th>Author</th>\n",
       "      <th>Year</th>\n",
       "      <th>Raw</th>\n",
       "      <th>Processed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.1016/j.cell.2016.11.038</td>\n",
       "      <td>CRISPR</td>\n",
       "      <td>Perturb-seq</td>\n",
       "      <td>Dixit et al (2016)</td>\n",
       "      <td>RNA-seq</td>\n",
       "      <td>10,24</td>\n",
       "      <td>1</td>\n",
       "      <td>-</td>\n",
       "      <td>1-2</td>\n",
       "      <td>TFs followed by LPS treatment in BMDCs, TFs in...</td>\n",
       "      <td>SCP</td>\n",
       "      <td>Dixit</td>\n",
       "      <td>2016</td>\n",
       "      <td>https://ndownloader.figshare.com/files/34011689</td>\n",
       "      <td>https://ndownloader.figshare.com/files/34014608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.1126/science.1247651</td>\n",
       "      <td>CRISPR</td>\n",
       "      <td>CRISP-seq</td>\n",
       "      <td>Jaitin et al (2016)</td>\n",
       "      <td>RNA-seq</td>\n",
       "      <td>8-22</td>\n",
       "      <td>1</td>\n",
       "      <td>-</td>\n",
       "      <td>1</td>\n",
       "      <td>TFs, in vitro hemato and in vivo data. CRISP-s...</td>\n",
       "      <td>processed</td>\n",
       "      <td>Jaitin</td>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.1038/nmeth.4177</td>\n",
       "      <td>CRISPR</td>\n",
       "      <td>CROP-seq</td>\n",
       "      <td>Datlinger et al (2017)</td>\n",
       "      <td>RNA-seq</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>-</td>\n",
       "      <td>1</td>\n",
       "      <td>TFs and T-cell receptors pathway targets (3x g...</td>\n",
       "      <td>processed</td>\n",
       "      <td>Datlinger</td>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.1038/nmeth.4604</td>\n",
       "      <td>CRISPR</td>\n",
       "      <td>CROP-seq</td>\n",
       "      <td>Hill et al (2018)</td>\n",
       "      <td>RNA-seq</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>targeting tumor surpressors in MCF10A with dox...</td>\n",
       "      <td>processed</td>\n",
       "      <td>Hill</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.1101/2020.11.16.383307</td>\n",
       "      <td>CRISPR</td>\n",
       "      <td>Perturb-seq</td>\n",
       "      <td>Ursu et al (2020)</td>\n",
       "      <td>RNA-seq</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>-</td>\n",
       "      <td>1</td>\n",
       "      <td>100 variants each for 2 genes</td>\n",
       "      <td>unavailable</td>\n",
       "      <td>Ursu</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10.1126/science.aaz6063</td>\n",
       "      <td>CRISPR</td>\n",
       "      <td>Perturb-seq</td>\n",
       "      <td>Jin et al (2020)</td>\n",
       "      <td>RNA-seq</td>\n",
       "      <td>35</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>1</td>\n",
       "      <td>in vivo mouse brain development (2x per, frame...</td>\n",
       "      <td>SCP</td>\n",
       "      <td>Jin</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10.1038/s41588-021-00779-1</td>\n",
       "      <td>CRISPR</td>\n",
       "      <td>Perturb-CITE-seq</td>\n",
       "      <td>Frangieh et al (2021)</td>\n",
       "      <td>RNA+protein</td>\n",
       "      <td>248</td>\n",
       "      <td>1</td>\n",
       "      <td>-</td>\n",
       "      <td>1</td>\n",
       "      <td>treatment resistant cancer samples, patient de...</td>\n",
       "      <td>SCP</td>\n",
       "      <td>Frangieh</td>\n",
       "      <td>2021</td>\n",
       "      <td>https://ndownloader.figshare.com/files/34012565</td>\n",
       "      <td>https://ndownloader.figshare.com/files/34013717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10.1038/s41592-021-01153-z</td>\n",
       "      <td>CRISPR KO + antibody</td>\n",
       "      <td>scifi-RNA-seq</td>\n",
       "      <td>Datlinger et al (2021)</td>\n",
       "      <td>RNA-seq</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20 target genes (2x gRNA per) in Jurkat cells,...</td>\n",
       "      <td>GSE168620</td>\n",
       "      <td>Datlinger</td>\n",
       "      <td>2021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10.1126/science.aax4438</td>\n",
       "      <td>CRISPRa</td>\n",
       "      <td>Perturb-seq</td>\n",
       "      <td>Norman et al (2019)</td>\n",
       "      <td>RNA-seq</td>\n",
       "      <td>287</td>\n",
       "      <td>1</td>\n",
       "      <td>-</td>\n",
       "      <td>1</td>\n",
       "      <td>induction of gene pair targets+single gene con...</td>\n",
       "      <td>processed</td>\n",
       "      <td>Norman</td>\n",
       "      <td>2019</td>\n",
       "      <td>https://ndownloader.figshare.com/files/34002548</td>\n",
       "      <td>https://ndownloader.figshare.com/files/34027562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.1016/j.cell.2016.11.048</td>\n",
       "      <td>CRISPRi</td>\n",
       "      <td>Perturb-seq</td>\n",
       "      <td>Adamson et al (2016)</td>\n",
       "      <td>RNA-seq</td>\n",
       "      <td>9-93 (sgRNA)</td>\n",
       "      <td>1</td>\n",
       "      <td>-</td>\n",
       "      <td>1</td>\n",
       "      <td>contains combinatorial guide delivery. Perturb...</td>\n",
       "      <td>processed</td>\n",
       "      <td>Adamson</td>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10.1016/j.cell.2018.11.029</td>\n",
       "      <td>CRISPRi</td>\n",
       "      <td>CROP-seq</td>\n",
       "      <td>Gasperini et al (2019)</td>\n",
       "      <td>RNA-seq</td>\n",
       "      <td>1119, 5779</td>\n",
       "      <td>1</td>\n",
       "      <td>-</td>\n",
       "      <td>1</td>\n",
       "      <td>2 experiments, CRISPRi of enhancer region</td>\n",
       "      <td>processed</td>\n",
       "      <td>Gasperini</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10.1038/s41592-020-0837-5</td>\n",
       "      <td>CRISPRi</td>\n",
       "      <td>TAP-seq</td>\n",
       "      <td>Schraivogel et al (2020)</td>\n",
       "      <td>RNA-seq</td>\n",
       "      <td>1778 (enhancers)</td>\n",
       "      <td>1</td>\n",
       "      <td>-</td>\n",
       "      <td>1</td>\n",
       "      <td>targeted enhancers on two chromosomes in K562 ...</td>\n",
       "      <td>processed</td>\n",
       "      <td>Schraivogel</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10.1038/s41587-019-0387-5</td>\n",
       "      <td>CRISPRi</td>\n",
       "      <td>Perturb-seq</td>\n",
       "      <td>Jost et al (2020)</td>\n",
       "      <td>RNA-seq</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>-</td>\n",
       "      <td>1</td>\n",
       "      <td>4 experiments, sgRNA variants with mismatch</td>\n",
       "      <td>processed</td>\n",
       "      <td>Jost</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10.1038/s41588-021-00778-2</td>\n",
       "      <td>CRISPR</td>\n",
       "      <td>ECCITE-seq</td>\n",
       "      <td>Papalexi et al (2021)</td>\n",
       "      <td>RNA-seq</td>\n",
       "      <td>111 (sgRNA)</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-</td>\n",
       "      <td>IFNg, DAC, and TGFb induced THP-1 cells, analy...</td>\n",
       "      <td>processed</td>\n",
       "      <td>Papalexi</td>\n",
       "      <td>2021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10.1016/j.cels.2020.06.004</td>\n",
       "      <td>CRISPRa</td>\n",
       "      <td>-</td>\n",
       "      <td>Alda-Catalinas et al (2020)</td>\n",
       "      <td>RNA-seq</td>\n",
       "      <td>230</td>\n",
       "      <td>1</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>zygotic genomic activation factors in mouse ES...</td>\n",
       "      <td>GSE135621</td>\n",
       "      <td>Alda-Catalinas</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10.1101/2021.08.23.457400</td>\n",
       "      <td>CRISPRi</td>\n",
       "      <td>CROP-seq</td>\n",
       "      <td>Leng et al (2021)</td>\n",
       "      <td>RNA-seq</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-</td>\n",
       "      <td>IL-1α+TNF+C1q in human IPSC-derived astrocytes</td>\n",
       "      <td>GSE182308</td>\n",
       "      <td>Leng</td>\n",
       "      <td>2021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>genetic targets</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Replogle et al (2020)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Replogle</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>10.1101/2021.12.16.473013v3</td>\n",
       "      <td>genetic targets</td>\n",
       "      <td>Perturb-seq</td>\n",
       "      <td>Replogle et al (2021)</td>\n",
       "      <td>RNA-seq</td>\n",
       "      <td>&gt;10000</td>\n",
       "      <td>2</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>upon publication</td>\n",
       "      <td>Replogle</td>\n",
       "      <td>2021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>10.1126/science.aax6234</td>\n",
       "      <td>small molecules</td>\n",
       "      <td>sci-Plex</td>\n",
       "      <td>Srivatsan et al (2019)</td>\n",
       "      <td>RNA-seq</td>\n",
       "      <td>188</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>in vitro cancer cell lines and small molecules</td>\n",
       "      <td>processed</td>\n",
       "      <td>Srivatsan</td>\n",
       "      <td>2019</td>\n",
       "      <td>https://ndownloader.figshare.com/files/33979517</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>10.1126/sciadv.aav2249</td>\n",
       "      <td>small molecules</td>\n",
       "      <td>multiplexed</td>\n",
       "      <td>Shin et al (2019)</td>\n",
       "      <td>RNA-seq</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>transfected barcodes label perturbation condit...</td>\n",
       "      <td>unavailable</td>\n",
       "      <td>Shin</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10.1038/s41467-020-17440-w</td>\n",
       "      <td>small molecules</td>\n",
       "      <td>MIX-seq</td>\n",
       "      <td>McFarland et al (2020)</td>\n",
       "      <td>RNA-seq</td>\n",
       "      <td>1-13</td>\n",
       "      <td>24-99</td>\n",
       "      <td>1</td>\n",
       "      <td>1-5</td>\n",
       "      <td>4 small molecule experiments, one genetic</td>\n",
       "      <td>processed</td>\n",
       "      <td>McFarland</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10.1038/s41592-019-0689-z</td>\n",
       "      <td>small molecules</td>\n",
       "      <td>CyTOF</td>\n",
       "      <td>Chen et al (2020)</td>\n",
       "      <td>protein</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>breast  cancer  cells  undergoing  TGF-β-induc...</td>\n",
       "      <td>-</td>\n",
       "      <td>Chen</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>10.1101/2020.04.22.056341</td>\n",
       "      <td>small molecules</td>\n",
       "      <td>scRNA-seq</td>\n",
       "      <td>Zhao et al (2020)</td>\n",
       "      <td>RNA-seq</td>\n",
       "      <td>2,6</td>\n",
       "      <td>6,1</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>compounds applied to patient resections</td>\n",
       "      <td>processed</td>\n",
       "      <td>Zhao</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            DOI             Treatment         Technique  \\\n",
       "0    10.1016/j.cell.2016.11.038                CRISPR       Perturb-seq   \n",
       "1       10.1126/science.1247651                CRISPR         CRISP-seq   \n",
       "2            10.1038/nmeth.4177                CRISPR          CROP-seq   \n",
       "3            10.1038/nmeth.4604                CRISPR          CROP-seq   \n",
       "4     10.1101/2020.11.16.383307                CRISPR       Perturb-seq   \n",
       "5       10.1126/science.aaz6063                CRISPR       Perturb-seq   \n",
       "6    10.1038/s41588-021-00779-1                CRISPR  Perturb-CITE-seq   \n",
       "7    10.1038/s41592-021-01153-z  CRISPR KO + antibody     scifi-RNA-seq   \n",
       "8       10.1126/science.aax4438               CRISPRa       Perturb-seq   \n",
       "9    10.1016/j.cell.2016.11.048               CRISPRi       Perturb-seq   \n",
       "10   10.1016/j.cell.2018.11.029               CRISPRi          CROP-seq   \n",
       "11    10.1038/s41592-020-0837-5               CRISPRi           TAP-seq   \n",
       "12    10.1038/s41587-019-0387-5               CRISPRi       Perturb-seq   \n",
       "13   10.1038/s41588-021-00778-2                CRISPR        ECCITE-seq   \n",
       "14   10.1016/j.cels.2020.06.004               CRISPRa                 -   \n",
       "15    10.1101/2021.08.23.457400               CRISPRi          CROP-seq   \n",
       "16                          NaN       genetic targets               NaN   \n",
       "17  10.1101/2021.12.16.473013v3       genetic targets       Perturb-seq   \n",
       "18      10.1126/science.aax6234       small molecules          sci-Plex   \n",
       "19       10.1126/sciadv.aav2249       small molecules       multiplexed   \n",
       "20   10.1038/s41467-020-17440-w       small molecules           MIX-seq   \n",
       "21    10.1038/s41592-019-0689-z       small molecules             CyTOF   \n",
       "22    10.1101/2020.04.22.056341       small molecules         scRNA-seq   \n",
       "\n",
       "                      Shorthand  Measurement   # perturbations # cell types  \\\n",
       "0            Dixit et al (2016)      RNA-seq             10,24            1   \n",
       "1           Jaitin et al (2016)      RNA-seq              8-22            1   \n",
       "2        Datlinger et al (2017)      RNA-seq                29            1   \n",
       "3             Hill et al (2018)      RNA-seq                32            1   \n",
       "4             Ursu et al (2020)      RNA-seq               200            1   \n",
       "5              Jin et al (2020)      RNA-seq                35            -   \n",
       "6         Frangieh et al (2021)  RNA+protein               248            1   \n",
       "7        Datlinger et al (2021)      RNA-seq                96            1   \n",
       "8           Norman et al (2019)      RNA-seq               287            1   \n",
       "9          Adamson et al (2016)      RNA-seq      9-93 (sgRNA)            1   \n",
       "10       Gasperini et al (2019)      RNA-seq        1119, 5779            1   \n",
       "11     Schraivogel et al (2020)      RNA-seq  1778 (enhancers)            1   \n",
       "12            Jost et al (2020)      RNA-seq                25            2   \n",
       "13        Papalexi et al (2021)      RNA-seq       111 (sgRNA)            1   \n",
       "14  Alda-Catalinas et al (2020)      RNA-seq               230            1   \n",
       "15            Leng et al (2021)      RNA-seq                30            1   \n",
       "16        Replogle et al (2020)          NaN               NaN          NaN   \n",
       "17        Replogle et al (2021)      RNA-seq            >10000            2   \n",
       "18       Srivatsan et al (2019)      RNA-seq               188            3   \n",
       "19            Shin et al (2019)      RNA-seq                45            2   \n",
       "20       McFarland et al (2020)      RNA-seq              1-13        24-99   \n",
       "21            Chen et al (2020)      protein               300            1   \n",
       "22            Zhao et al (2020)      RNA-seq               2,6          6,1   \n",
       "\n",
       "   # doses # timepoints                                        Cell source  \\\n",
       "0        -          1-2  TFs followed by LPS treatment in BMDCs, TFs in...   \n",
       "1        -            1  TFs, in vitro hemato and in vivo data. CRISP-s...   \n",
       "2        -            1  TFs and T-cell receptors pathway targets (3x g...   \n",
       "3        2            1  targeting tumor surpressors in MCF10A with dox...   \n",
       "4        -            1                      100 variants each for 2 genes   \n",
       "5        -            1  in vivo mouse brain development (2x per, frame...   \n",
       "6        -            1  treatment resistant cancer samples, patient de...   \n",
       "7        1            1  20 target genes (2x gRNA per) in Jurkat cells,...   \n",
       "8        -            1  induction of gene pair targets+single gene con...   \n",
       "9        -            1  contains combinatorial guide delivery. Perturb...   \n",
       "10       -            1          2 experiments, CRISPRi of enhancer region   \n",
       "11       -            1  targeted enhancers on two chromosomes in K562 ...   \n",
       "12       -            1        4 experiments, sgRNA variants with mismatch   \n",
       "13       2            -  IFNg, DAC, and TGFb induced THP-1 cells, analy...   \n",
       "14       -            -  zygotic genomic activation factors in mouse ES...   \n",
       "15       2            -     IL-1α+TNF+C1q in human IPSC-derived astrocytes   \n",
       "16     NaN          NaN                                                NaN   \n",
       "17       -            -                                                NaN   \n",
       "18       4            2     in vitro cancer cell lines and small molecules   \n",
       "19       1            1  transfected barcodes label perturbation condit...   \n",
       "20       1          1-5          4 small molecule experiments, one genetic   \n",
       "21       1            1  breast  cancer  cells  undergoing  TGF-β-induc...   \n",
       "22       -            -            compounds applied to patient resections   \n",
       "\n",
       "        Availability          Author  Year  \\\n",
       "0                SCP           Dixit  2016   \n",
       "1          processed          Jaitin  2016   \n",
       "2          processed       Datlinger  2017   \n",
       "3          processed            Hill  2018   \n",
       "4        unavailable            Ursu  2020   \n",
       "5                SCP             Jin  2020   \n",
       "6                SCP        Frangieh  2021   \n",
       "7          GSE168620       Datlinger  2021   \n",
       "8          processed          Norman  2019   \n",
       "9          processed         Adamson  2016   \n",
       "10         processed       Gasperini  2019   \n",
       "11         processed     Schraivogel  2020   \n",
       "12         processed            Jost  2020   \n",
       "13         processed        Papalexi  2021   \n",
       "14         GSE135621  Alda-Catalinas  2020   \n",
       "15         GSE182308            Leng  2021   \n",
       "16               NaN        Replogle  2020   \n",
       "17  upon publication        Replogle  2021   \n",
       "18         processed       Srivatsan  2019   \n",
       "19       unavailable            Shin  2019   \n",
       "20         processed       McFarland  2020   \n",
       "21                 -            Chen  2020   \n",
       "22         processed            Zhao  2020   \n",
       "\n",
       "                                                Raw  \\\n",
       "0   https://ndownloader.figshare.com/files/34011689   \n",
       "1                                               NaN   \n",
       "2                                               NaN   \n",
       "3                                               NaN   \n",
       "4                                               NaN   \n",
       "5                                               NaN   \n",
       "6   https://ndownloader.figshare.com/files/34012565   \n",
       "7                                               NaN   \n",
       "8   https://ndownloader.figshare.com/files/34002548   \n",
       "9                                               NaN   \n",
       "10                                              NaN   \n",
       "11                                              NaN   \n",
       "12                                              NaN   \n",
       "13                                              NaN   \n",
       "14                                              NaN   \n",
       "15                                              NaN   \n",
       "16                                              NaN   \n",
       "17                                              NaN   \n",
       "18  https://ndownloader.figshare.com/files/33979517   \n",
       "19                                              NaN   \n",
       "20                                              NaN   \n",
       "21                                              NaN   \n",
       "22                                              NaN   \n",
       "\n",
       "                                          Processed  \n",
       "0   https://ndownloader.figshare.com/files/34014608  \n",
       "1                                               NaN  \n",
       "2                                               NaN  \n",
       "3                                               NaN  \n",
       "4                                               NaN  \n",
       "5                                               NaN  \n",
       "6   https://ndownloader.figshare.com/files/34013717  \n",
       "7                                               NaN  \n",
       "8   https://ndownloader.figshare.com/files/34027562  \n",
       "9                                               NaN  \n",
       "10                                              NaN  \n",
       "11                                              NaN  \n",
       "12                                              NaN  \n",
       "13                                              NaN  \n",
       "14                                              NaN  \n",
       "15                                              NaN  \n",
       "16                                              NaN  \n",
       "17                                              NaN  \n",
       "18                                              NaN  \n",
       "19                                              NaN  \n",
       "20                                              NaN  \n",
       "21                                              NaN  \n",
       "22                                              NaN  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cfe0ea02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Treatment'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 324,
       "width": 521
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Treatment.value_counts().plot(kind='pie', startangle=290)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e9173b03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Technique'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 324,
       "width": 497
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Technique.value_counts().plot(kind='pie', startangle=290)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca2636ba",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:py37] *",
   "language": "python",
   "name": "conda-env-py37-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}