notebooks/H2H_Wass_SigPred.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from os.path import expanduser\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/work/05202/dbryce/jupyter_packages/lib/python3.6/site-packages/pandas/core/frame.py:6692: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  sort=sort)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TX_plate_name</th>\n",
       "      <th>TX_project_name</th>\n",
       "      <th>TX_sample_name</th>\n",
       "      <th>_id</th>\n",
       "      <th>aliquot_id</th>\n",
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       "      <th>date_of_experiment</th>\n",
       "      <th>experiment_id</th>\n",
       "      <th>experiment_reference</th>\n",
       "      <th>...</th>\n",
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       "      <th>strain</th>\n",
       "      <th>strain_class</th>\n",
       "      <th>strain_name</th>\n",
       "      <th>temperature</th>\n",
       "      <th>temperature_unit</th>\n",
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       "      <th>timepoint_unit</th>\n",
       "      <th>total_counts</th>\n",
       "      <th>well</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>TimeSeries-12-11-2019-Run1P1</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
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       "      <td>aq1dvh7dssh7ewe</td>\n",
       "      <td>4.500000e+02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-Dec-2019</td>\n",
       "      <td>experiment.transcriptic.r1dvbsa7n5qwxe</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SpheroControl</td>\n",
       "      <td>Process</td>\n",
       "      <td>Fluorescent_beads</td>\n",
       "      <td>30.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>18.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>27</td>\n",
       "      <td>f2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TimeSeries-12-11-2019-Run1P1</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5e74fd1ba3b44b40d9568234</td>\n",
       "      <td>aq1dvh7dssbhf98</td>\n",
       "      <td>9.166667e+02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-Dec-2019</td>\n",
       "      <td>experiment.transcriptic.r1dvbsa7n5qwxe</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>beads_spherotech_pps_6K</td>\n",
       "      <td>Process</td>\n",
       "      <td>Sizing_beads</td>\n",
       "      <td>30.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>18.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>55</td>\n",
       "      <td>e2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TimeSeries-12-11-2019-Run1P2</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5e7502d723b9a6ed388174f7</td>\n",
       "      <td>aq1dvhgvrhhf3gj</td>\n",
       "      <td>3.387167e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-Dec-2019</td>\n",
       "      <td>experiment.transcriptic.r1dvbsa7n5qwxe</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SpheroControl</td>\n",
       "      <td>Process</td>\n",
       "      <td>Fluorescent_beads</td>\n",
       "      <td>30.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>20.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>20323</td>\n",
       "      <td>f2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TimeSeries-12-11-2019-Run1P2</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5e74ffc980408659f9a161c6</td>\n",
       "      <td>aq1dvhgvrhamvqg</td>\n",
       "      <td>1.639167e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-Dec-2019</td>\n",
       "      <td>experiment.transcriptic.r1dvbsa7n5qwxe</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>beads_spherotech_pps_6K</td>\n",
       "      <td>Process</td>\n",
       "      <td>Sizing_beads</td>\n",
       "      <td>30.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>20.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>9835</td>\n",
       "      <td>e2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TimeSeries-12-11-2019-Run1P3</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5e74fda2c05815244ace28b7</td>\n",
       "      <td>aq1dvhtnnh2kdb9</td>\n",
       "      <td>3.372000e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-Dec-2019</td>\n",
       "      <td>experiment.transcriptic.r1dvbsa7n5qwxe</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SpheroControl</td>\n",
       "      <td>Process</td>\n",
       "      <td>Fluorescent_beads</td>\n",
       "      <td>30.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>22.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>20232</td>\n",
       "      <td>f2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>TimeSeries-12-11-2019-Run1P3</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5e74fe1b279337c2a7aa1153</td>\n",
       "      <td>aq1dvhtnnguwdp3</td>\n",
       "      <td>1.653167e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-Dec-2019</td>\n",
       "      <td>experiment.transcriptic.r1dvbsa7n5qwxe</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>beads_spherotech_pps_6K</td>\n",
       "      <td>Process</td>\n",
       "      <td>Sizing_beads</td>\n",
       "      <td>30.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>22.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>9919</td>\n",
       "      <td>e2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>TimeSeries-12-11-2019-Run1P4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5e74fe5c1339c26e49c9c472</td>\n",
       "      <td>aq1dvj68hhw8abj</td>\n",
       "      <td>3.771667e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-Dec-2019</td>\n",
       "      <td>experiment.transcriptic.r1dvbsa7n5qwxe</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SpheroControl</td>\n",
       "      <td>Process</td>\n",
       "      <td>Fluorescent_beads</td>\n",
       "      <td>30.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>22630</td>\n",
       "      <td>f2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>TimeSeries-12-11-2019-Run1P4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5e74fd7537827b21162fa0ea</td>\n",
       "      <td>aq1dvj68hhpe4je</td>\n",
       "      <td>1.971167e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-Dec-2019</td>\n",
       "      <td>experiment.transcriptic.r1dvbsa7n5qwxe</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>beads_spherotech_pps_6K</td>\n",
       "      <td>Process</td>\n",
       "      <td>Sizing_beads</td>\n",
       "      <td>30.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>11827</td>\n",
       "      <td>e2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>TimeSeries-12-11-2019-Run1P1</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5e6c02528859561b561e3e19</td>\n",
       "      <td>aq1dvh7dsr6754b</td>\n",
       "      <td>6.521739e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-Dec-2019</td>\n",
       "      <td>experiment.transcriptic.r1dvbsa7n5qwxe</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24926</td>\n",
       "      <td>30.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>18.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>a3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>TimeSeries-12-11-2019-Run1P1</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5e6c0914e766fe93eaa7318e</td>\n",
       "      <td>aq1dvh7dsr67549</td>\n",
       "      <td>3.575000e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-Dec-2019</td>\n",
       "      <td>experiment.transcriptic.r1dvbsa7n5qwxe</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_23970</td>\n",
       "      <td>Control</td>\n",
       "      <td>CRISPR_CEN.PK2_negative_control_23970</td>\n",
       "      <td>30.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>18.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>2145</td>\n",
       "      <td>a2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>TimeSeries-12-11-2019-Run1P1</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5e6c0561d3befe91aee87fae</td>\n",
       "      <td>aq1dvh7dst4ckyw</td>\n",
       "      <td>1.495667e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-Dec-2019</td>\n",
       "      <td>experiment.transcriptic.r1dvbsa7n5qwxe</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24961</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24961</td>\n",
       "      <td>30.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>18.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>8974</td>\n",
       "      <td>h12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>TimeSeries-12-11-2019-Run1P1</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5e6c0915e766fe93eaa73193</td>\n",
       "      <td>aq1dvh7dst38duv</td>\n",
       "      <td>6.148333e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-Dec-2019</td>\n",
       "      <td>experiment.transcriptic.r1dvbsa7n5qwxe</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24961</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24961</td>\n",
       "      <td>30.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>18.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>3689</td>\n",
       "      <td>h10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>TimeSeries-12-11-2019-Run1P1</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5e56eb1a7d52ef7e1cfdc983</td>\n",
       "      <td>aq1dvh7dsrq842p</td>\n",
       "      <td>1.363636e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-Dec-2019</td>\n",
       "      <td>experiment.transcriptic.r1dvbsa7n5qwxe</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24959</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24959</td>\n",
       "      <td>30.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>18.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>c8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>TimeSeries-12-11-2019-Run1P1</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5e6c0916e766fe93eaa73196</td>\n",
       "      <td>aq1dvh7dsr67547</td>\n",
       "      <td>4.177667e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-Dec-2019</td>\n",
       "      <td>experiment.transcriptic.r1dvbsa7n5qwxe</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_6390</td>\n",
       "      <td>Control</td>\n",
       "      <td>CRISPR_w303_positive_control_NOR_00_6390</td>\n",
       "      <td>30.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>18.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>25066</td>\n",
       "      <td>a1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>TimeSeries-12-11-2019-Run1P1</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5e56eb1a7d52ef7e1cfdc986</td>\n",
       "      <td>aq1dvh7dss2ev7y</td>\n",
       "      <td>8.333333e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-Dec-2019</td>\n",
       "      <td>experiment.transcriptic.r1dvbsa7n5qwxe</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24959</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24959</td>\n",
       "      <td>30.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>18.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>c9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>TimeSeries-12-11-2019-Run1P1</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5e6a6d8c644e7f44df302c9c</td>\n",
       "      <td>aq1dvh7dst38dux</td>\n",
       "      <td>9.366667e+03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-Dec-2019</td>\n",
       "      <td>experiment.transcriptic.r1dvbsa7n5qwxe</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24961</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24961</td>\n",
       "      <td>30.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>18.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>562</td>\n",
       "      <td>h11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>TimeSeries-12-11-2019-Run1P1</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5e6c0916e766fe93eaa73199</td>\n",
       "      <td>aq1dvh7dsr7bb8b</td>\n",
       "      <td>7.894737e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-Dec-2019</td>\n",
       "      <td>experiment.transcriptic.r1dvbsa7n5qwxe</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24926</td>\n",
       "      <td>30.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>18.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>a5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>TimeSeries-12-11-2019-Run1P1</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5e6c0917e766fe93eaa7319c</td>\n",
       "      <td>aq1dvh7dsrgdw9g</td>\n",
       "      <td>1.250000e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-Dec-2019</td>\n",
       "      <td>experiment.transcriptic.r1dvbsa7n5qwxe</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24952</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24952</td>\n",
       "      <td>30.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>18.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>b9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>TimeSeries-12-11-2019-Run1P1</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5e6c0917e766fe93eaa7319f</td>\n",
       "      <td>aq1dvh7dsrf9q5h</td>\n",
       "      <td>1.034483e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-Dec-2019</td>\n",
       "      <td>experiment.transcriptic.r1dvbsa7n5qwxe</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24952</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24952</td>\n",
       "      <td>30.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>18.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>b8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>TimeSeries-12-11-2019-Run1P1</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5e56eb1c7d52ef7e1cfdc995</td>\n",
       "      <td>aq1dvh7dsr7bb89</td>\n",
       "      <td>1.111111e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-Dec-2019</td>\n",
       "      <td>experiment.transcriptic.r1dvbsa7n5qwxe</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24926</td>\n",
       "      <td>30.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>18.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>a4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>TimeSeries-12-11-2019-Run1P1</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5e6c0918e766fe93eaa731a2</td>\n",
       "      <td>aq1dvh7dsr8fhc9</td>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-Dec-2019</td>\n",
       "      <td>experiment.transcriptic.r1dvbsa7n5qwxe</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24926</td>\n",
       "      <td>30.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>18.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>a6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>TimeSeries-12-11-2019-Run1P1</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5e6c033b90977b21964e1f5c</td>\n",
       "      <td>aq1dvh7dsr8fhcb</td>\n",
       "      <td>1.111111e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-Dec-2019</td>\n",
       "      <td>experiment.transcriptic.r1dvbsa7n5qwxe</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24926</td>\n",
       "      <td>30.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>18.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>a7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>TimeSeries-12-11-2019-Run1P1</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5e6c0919e766fe93eaa731a7</td>\n",
       "      <td>aq1dvh7dssdsuhc</td>\n",
       "      <td>2.913000e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-Dec-2019</td>\n",
       "      <td>experiment.transcriptic.r1dvbsa7n5qwxe</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_25784</td>\n",
       "      <td>Control</td>\n",
       "      <td>CRISPR_CEN.PK2_inducible_FP_25784</td>\n",
       "      <td>30.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>18.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>17478</td>\n",
       "      <td>e8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>TimeSeries-12-11-2019-Run1P1</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5e56eb1e7d52ef7e1cfdc9a1</td>\n",
       "      <td>aq1dvh7dssjbn2f</td>\n",
       "      <td>1.304348e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-Dec-2019</td>\n",
       "      <td>experiment.transcriptic.r1dvbsa7n5qwxe</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24962</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24962</td>\n",
       "      <td>30.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>18.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>f4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>TimeSeries-12-11-2019-Run1P1</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5e73ab591fc1a5fe22778470</td>\n",
       "      <td>aq1dvh7dsspvejk</td>\n",
       "      <td>1.258333e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-Dec-2019</td>\n",
       "      <td>experiment.transcriptic.r1dvbsa7n5qwxe</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_23970</td>\n",
       "      <td>Control</td>\n",
       "      <td>CRISPR_CEN.PK2_negative_control_23970</td>\n",
       "      <td>30.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>18.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>755</td>\n",
       "      <td>g1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>TimeSeries-12-11-2019-Run1P1</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5e56eb1e7d52ef7e1cfdc9a4</td>\n",
       "      <td>aq1dvh7dssjbn2h</td>\n",
       "      <td>1.071429e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-Dec-2019</td>\n",
       "      <td>experiment.transcriptic.r1dvbsa7n5qwxe</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24962</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24962</td>\n",
       "      <td>30.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>18.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>f5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>TimeSeries-12-11-2019-Run1P1</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5e6c091ae766fe93eaa731ad</td>\n",
       "      <td>aq1dvh7dssex2nb</td>\n",
       "      <td>1.060000e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-Dec-2019</td>\n",
       "      <td>experiment.transcriptic.r1dvbsa7n5qwxe</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_25784</td>\n",
       "      <td>Control</td>\n",
       "      <td>CRISPR_CEN.PK2_inducible_FP_25784</td>\n",
       "      <td>30.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>18.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>636</td>\n",
       "      <td>e9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>TimeSeries-12-11-2019-Run1P1</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5e6c021141bfc9f37abc5584</td>\n",
       "      <td>aq1dvh7dssqzmpm</td>\n",
       "      <td>5.000000e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-Dec-2019</td>\n",
       "      <td>experiment.transcriptic.r1dvbsa7n5qwxe</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24963</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24963</td>\n",
       "      <td>30.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>18.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>g3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>TimeSeries-12-11-2019-Run1P1</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5e56eb1f7d52ef7e1cfdc9aa</td>\n",
       "      <td>aq1dvh7dsskfu6h</td>\n",
       "      <td>1.250000e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-Dec-2019</td>\n",
       "      <td>experiment.transcriptic.r1dvbsa7n5qwxe</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24962</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24962</td>\n",
       "      <td>30.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>18.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>f7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>TimeSeries-12-11-2019-Run1P1</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5e56eb207d52ef7e1cfdc9ad</td>\n",
       "      <td>aq1dvh7dsskfu6f</td>\n",
       "      <td>1.304348e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-Dec-2019</td>\n",
       "      <td>experiment.transcriptic.r1dvbsa7n5qwxe</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24962</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24962</td>\n",
       "      <td>30.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>18.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>f6</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",
       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1478</th>\n",
       "      <td>YS-TS35C-03-05-2020-T4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5ef2667c31dd0f21f58bad37</td>\n",
       "      <td>aq1e6tn4gmdj8ug</td>\n",
       "      <td>3.000000e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-Mar-2020</td>\n",
       "      <td>experiment.transcriptic.r1e6ge2n8x7q5p</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24926</td>\n",
       "      <td>35.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>a7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1479</th>\n",
       "      <td>YS-TS35C-03-05-2020-T4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5ef2668931dd0f21f58bad70</td>\n",
       "      <td>aq1e6tn4gm3bfp8</td>\n",
       "      <td>3.000000e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-Mar-2020</td>\n",
       "      <td>experiment.transcriptic.r1e6ge2n8x7q5p</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24926</td>\n",
       "      <td>35.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>a5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1480</th>\n",
       "      <td>YS-TS35C-03-05-2020-T4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5ef266ca197571bba534acb0</td>\n",
       "      <td>aq1e6tn4gmrw85s</td>\n",
       "      <td>2.727273e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-Mar-2020</td>\n",
       "      <td>experiment.transcriptic.r1e6ge2n8x7q5p</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24952</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24952</td>\n",
       "      <td>35.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>b9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1481</th>\n",
       "      <td>YS-TS35C-03-05-2020-T4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5ef2676cfdcaed7ae4c67b47</td>\n",
       "      <td>aq1e6tn4gmk88gp</td>\n",
       "      <td>2.727273e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-Mar-2020</td>\n",
       "      <td>experiment.transcriptic.r1e6ge2n8x7q5p</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24952</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24952</td>\n",
       "      <td>35.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>b8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1482</th>\n",
       "      <td>YS-TS35C-03-05-2020-T4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5ef266616d07d57cb70bf1c9</td>\n",
       "      <td>aq1e6tn4gkwsp62</td>\n",
       "      <td>2.727273e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-Mar-2020</td>\n",
       "      <td>experiment.transcriptic.r1e6ge2n8x7q5p</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24926</td>\n",
       "      <td>35.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>a4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1483</th>\n",
       "      <td>YS-TS35C-03-05-2020-T4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5ef2676bfdcaed7ae4c67b43</td>\n",
       "      <td>aq1e6tn4gmmcemn</td>\n",
       "      <td>2.727273e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-Mar-2020</td>\n",
       "      <td>experiment.transcriptic.r1e6ge2n8x7q5p</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24959</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24959</td>\n",
       "      <td>35.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>c8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1484</th>\n",
       "      <td>YS-TS35C-03-05-2020-T4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5ef266d84a888501c32f6a81</td>\n",
       "      <td>aq1e6tn4gk9c4td</td>\n",
       "      <td>2.727273e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-Mar-2020</td>\n",
       "      <td>experiment.transcriptic.r1e6ge2n8x7q5p</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24926</td>\n",
       "      <td>35.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>a1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1485</th>\n",
       "      <td>YS-TS35C-03-05-2020-T4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5ef266bc197571bba534ac83</td>\n",
       "      <td>aq1e6tn4gmt2e9r</td>\n",
       "      <td>2.500000e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-Mar-2020</td>\n",
       "      <td>experiment.transcriptic.r1e6ge2n8x7q5p</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24959</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24959</td>\n",
       "      <td>35.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>c9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1486</th>\n",
       "      <td>YS-TS35C-03-05-2020-T4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5ef266ed4a888501c32f6ad7</td>\n",
       "      <td>aq1e6tn4gks8vmu</td>\n",
       "      <td>3.000000e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-Mar-2020</td>\n",
       "      <td>experiment.transcriptic.r1e6ge2n8x7q5p</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24926</td>\n",
       "      <td>35.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>a3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1487</th>\n",
       "      <td>YS-TS35C-03-05-2020-T4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5ef266b4197571bba534ac63</td>\n",
       "      <td>aq1e6tn4gkmq44q</td>\n",
       "      <td>2.727273e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-Mar-2020</td>\n",
       "      <td>experiment.transcriptic.r1e6ge2n8x7q5p</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24926</td>\n",
       "      <td>35.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>a2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1488</th>\n",
       "      <td>YS-TS35C-03-05-2020-T4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5ef266031224eb25fb26e5d6</td>\n",
       "      <td>aq1e6tn4gkjepuq</td>\n",
       "      <td>2.500000e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-Mar-2020</td>\n",
       "      <td>experiment.transcriptic.r1e6ge2n8x7q5p</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24962</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24962</td>\n",
       "      <td>35.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>f1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1489</th>\n",
       "      <td>YS-TS35C-03-05-2020-T4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5ef266041224eb25fb26e5dc</td>\n",
       "      <td>aq1e6tn4gm7v978</td>\n",
       "      <td>2.727273e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-Mar-2020</td>\n",
       "      <td>experiment.transcriptic.r1e6ge2n8x7q5p</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_25784</td>\n",
       "      <td>Control</td>\n",
       "      <td>CRISPR_CEN.PK2_inducible_FP_25784</td>\n",
       "      <td>35.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>g5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1490</th>\n",
       "      <td>YS-TS35C-03-05-2020-T4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5ef266606d07d57cb70bf1c3</td>\n",
       "      <td>aq1e6tn4gmt2e9u</td>\n",
       "      <td>2.500000e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-Mar-2020</td>\n",
       "      <td>experiment.transcriptic.r1e6ge2n8x7q5p</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24960</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24960</td>\n",
       "      <td>35.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>d9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1491</th>\n",
       "      <td>YS-TS35C-03-05-2020-T4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5ef26600d0a530a5a55a60f9</td>\n",
       "      <td>aq1e6tn4gmngmrm</td>\n",
       "      <td>2.307692e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-Mar-2020</td>\n",
       "      <td>experiment.transcriptic.r1e6ge2n8x7q5p</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24960</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24960</td>\n",
       "      <td>35.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>d8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1492</th>\n",
       "      <td>YS-TS35C-03-05-2020-T4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5ef266626d07d57cb70bf1cb</td>\n",
       "      <td>aq1e6tn4gm279j6</td>\n",
       "      <td>2.727273e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-Mar-2020</td>\n",
       "      <td>experiment.transcriptic.r1e6ge2n8x7q5p</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_25784</td>\n",
       "      <td>Control</td>\n",
       "      <td>CRISPR_CEN.PK2_inducible_FP_25784</td>\n",
       "      <td>35.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>g4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1493</th>\n",
       "      <td>YS-TS35C-03-05-2020-T4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5ef2671b76edeb8538512386</td>\n",
       "      <td>aq1e6tn4gmce2qf</td>\n",
       "      <td>2.500000e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-Mar-2020</td>\n",
       "      <td>experiment.transcriptic.r1e6ge2n8x7q5p</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_25784</td>\n",
       "      <td>Control</td>\n",
       "      <td>CRISPR_CEN.PK2_inducible_FP_25784</td>\n",
       "      <td>35.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>g6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1494</th>\n",
       "      <td>YS-TS35C-03-05-2020-T4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5ef266091224eb25fb26e5ec</td>\n",
       "      <td>aq1e6tn4gkpygcv</td>\n",
       "      <td>2.727273e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-Mar-2020</td>\n",
       "      <td>experiment.transcriptic.r1e6ge2n8x7q5p</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24962</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24962</td>\n",
       "      <td>35.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>f2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1495</th>\n",
       "      <td>YS-TS35C-03-05-2020-T4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5ef26776fdcaed7ae4c67b75</td>\n",
       "      <td>aq1e6tn4gmweznu</td>\n",
       "      <td>2.307692e+06</td>\n",
       "      <td>HIGH_FITC</td>\n",
       "      <td>05-Mar-2020</td>\n",
       "      <td>experiment.transcriptic.r1e6ge2n8x7q5p</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_6390</td>\n",
       "      <td>Control</td>\n",
       "      <td>CRISPR_w303_positive_control_NOR_00_6390</td>\n",
       "      <td>35.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>h9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1496</th>\n",
       "      <td>YS-TS35C-03-05-2020-T4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5ef266ea4a888501c32f6acd</td>\n",
       "      <td>aq1e6tn4gmqrzzq</td>\n",
       "      <td>2.000000e+06</td>\n",
       "      <td>HIGH_FITC</td>\n",
       "      <td>05-Mar-2020</td>\n",
       "      <td>experiment.transcriptic.r1e6ge2n8x7q5p</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_6390</td>\n",
       "      <td>Control</td>\n",
       "      <td>CRISPR_w303_positive_control_NOR_00_6390</td>\n",
       "      <td>35.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>h8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1497</th>\n",
       "      <td>YS-TS35C-03-05-2020-T4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5ef266686d07d57cb70bf1e8</td>\n",
       "      <td>aq1e6tn4gkuh9w2</td>\n",
       "      <td>2.727273e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-Mar-2020</td>\n",
       "      <td>experiment.transcriptic.r1e6ge2n8x7q5p</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24962</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24962</td>\n",
       "      <td>35.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>f3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1498</th>\n",
       "      <td>YS-TS35C-03-05-2020-T4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5ef2672876edeb85385123b8</td>\n",
       "      <td>aq1e6tn4gmj42ch</td>\n",
       "      <td>2.727273e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-Mar-2020</td>\n",
       "      <td>experiment.transcriptic.r1e6ge2n8x7q5p</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_25784</td>\n",
       "      <td>Control</td>\n",
       "      <td>CRISPR_CEN.PK2_inducible_FP_25784</td>\n",
       "      <td>35.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>g7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499</th>\n",
       "      <td>YS-TS35C-03-05-2020-T4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5ef2677afdcaed7ae4c67b88</td>\n",
       "      <td>aq1e6tn4gkvnfzz</td>\n",
       "      <td>2.142857e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-Mar-2020</td>\n",
       "      <td>experiment.transcriptic.r1e6ge2n8x7q5p</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_25784</td>\n",
       "      <td>Control</td>\n",
       "      <td>CRISPR_CEN.PK2_inducible_FP_25784</td>\n",
       "      <td>35.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>g3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1500</th>\n",
       "      <td>YS-TS35C-03-05-2020-T4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5ef2666e6d07d57cb70bf205</td>\n",
       "      <td>aq1e6tn4gmgxu8k</td>\n",
       "      <td>2.500000e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-Mar-2020</td>\n",
       "      <td>experiment.transcriptic.r1e6ge2n8x7q5p</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24962</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24962</td>\n",
       "      <td>35.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>f7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1501</th>\n",
       "      <td>YS-TS35C-03-05-2020-T4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5ef2668431dd0f21f58bad5c</td>\n",
       "      <td>aq1e6tn4gmce2qc</td>\n",
       "      <td>2.727273e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-Mar-2020</td>\n",
       "      <td>experiment.transcriptic.r1e6ge2n8x7q5p</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24962</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24962</td>\n",
       "      <td>35.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>f6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1502</th>\n",
       "      <td>YS-TS35C-03-05-2020-T4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5ef265f7d0a530a5a55a60d4</td>\n",
       "      <td>aq1e6tn4gkr4pgt</td>\n",
       "      <td>2.142857e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-Mar-2020</td>\n",
       "      <td>experiment.transcriptic.r1e6ge2n8x7q5p</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_25784</td>\n",
       "      <td>Control</td>\n",
       "      <td>CRISPR_CEN.PK2_inducible_FP_25784</td>\n",
       "      <td>35.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>g2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1503</th>\n",
       "      <td>YS-TS35C-03-05-2020-T4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5ef2660d1224eb25fb26e5fe</td>\n",
       "      <td>aq1e6tn4gmngmrq</td>\n",
       "      <td>2.142857e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-Mar-2020</td>\n",
       "      <td>experiment.transcriptic.r1e6ge2n8x7q5p</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24961</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24961</td>\n",
       "      <td>35.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>e8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1504</th>\n",
       "      <td>YS-TS35C-03-05-2020-T4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5ef265f0d0a530a5a55a60ba</td>\n",
       "      <td>aq1e6tn4gm279j3</td>\n",
       "      <td>2.727273e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-Mar-2020</td>\n",
       "      <td>experiment.transcriptic.r1e6ge2n8x7q5p</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24962</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24962</td>\n",
       "      <td>35.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>f4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1505</th>\n",
       "      <td>YS-TS35C-03-05-2020-T4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5ef2673376edeb85385123ea</td>\n",
       "      <td>aq1e6tn4gkjeput</td>\n",
       "      <td>2.727273e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-Mar-2020</td>\n",
       "      <td>experiment.transcriptic.r1e6ge2n8x7q5p</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_25784</td>\n",
       "      <td>Control</td>\n",
       "      <td>CRISPR_CEN.PK2_inducible_FP_25784</td>\n",
       "      <td>35.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>g1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1506</th>\n",
       "      <td>YS-TS35C-03-05-2020-T4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5ef2676efdcaed7ae4c67b4f</td>\n",
       "      <td>aq1e6tn4gm6r33a</td>\n",
       "      <td>2.500000e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-Mar-2020</td>\n",
       "      <td>experiment.transcriptic.r1e6ge2n8x7q5p</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24962</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24962</td>\n",
       "      <td>35.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>f5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1507</th>\n",
       "      <td>YS-TS35C-03-05-2020-T4</td>\n",
       "      <td>Sytox-YeastGates</td>\n",
       "      <td>SytoxYG-Plate</td>\n",
       "      <td>5ef266d6d6c89f74262756bf</td>\n",
       "      <td>aq1e6tn4gmu6mds</td>\n",
       "      <td>3.000000e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-Mar-2020</td>\n",
       "      <td>experiment.transcriptic.r1e6ge2n8x7q5p</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UWBF_24961</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>CRISPR_CEN.PK2_24961</td>\n",
       "      <td>35.0</td>\n",
       "      <td>celsius</td>\n",
       "      <td>24.0</td>\n",
       "      <td>hour</td>\n",
       "      <td>30000</td>\n",
       "      <td>e9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1508 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     TX_plate_name   TX_project_name TX_sample_name  \\\n",
       "0     TimeSeries-12-11-2019-Run1P1  Sytox-YeastGates  SytoxYG-Plate   \n",
       "1     TimeSeries-12-11-2019-Run1P1  Sytox-YeastGates  SytoxYG-Plate   \n",
       "2     TimeSeries-12-11-2019-Run1P2  Sytox-YeastGates  SytoxYG-Plate   \n",
       "3     TimeSeries-12-11-2019-Run1P2  Sytox-YeastGates  SytoxYG-Plate   \n",
       "4     TimeSeries-12-11-2019-Run1P3  Sytox-YeastGates  SytoxYG-Plate   \n",
       "5     TimeSeries-12-11-2019-Run1P3  Sytox-YeastGates  SytoxYG-Plate   \n",
       "6     TimeSeries-12-11-2019-Run1P4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "7     TimeSeries-12-11-2019-Run1P4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "8     TimeSeries-12-11-2019-Run1P1  Sytox-YeastGates  SytoxYG-Plate   \n",
       "9     TimeSeries-12-11-2019-Run1P1  Sytox-YeastGates  SytoxYG-Plate   \n",
       "10    TimeSeries-12-11-2019-Run1P1  Sytox-YeastGates  SytoxYG-Plate   \n",
       "11    TimeSeries-12-11-2019-Run1P1  Sytox-YeastGates  SytoxYG-Plate   \n",
       "12    TimeSeries-12-11-2019-Run1P1  Sytox-YeastGates  SytoxYG-Plate   \n",
       "13    TimeSeries-12-11-2019-Run1P1  Sytox-YeastGates  SytoxYG-Plate   \n",
       "14    TimeSeries-12-11-2019-Run1P1  Sytox-YeastGates  SytoxYG-Plate   \n",
       "15    TimeSeries-12-11-2019-Run1P1  Sytox-YeastGates  SytoxYG-Plate   \n",
       "16    TimeSeries-12-11-2019-Run1P1  Sytox-YeastGates  SytoxYG-Plate   \n",
       "17    TimeSeries-12-11-2019-Run1P1  Sytox-YeastGates  SytoxYG-Plate   \n",
       "18    TimeSeries-12-11-2019-Run1P1  Sytox-YeastGates  SytoxYG-Plate   \n",
       "19    TimeSeries-12-11-2019-Run1P1  Sytox-YeastGates  SytoxYG-Plate   \n",
       "20    TimeSeries-12-11-2019-Run1P1  Sytox-YeastGates  SytoxYG-Plate   \n",
       "21    TimeSeries-12-11-2019-Run1P1  Sytox-YeastGates  SytoxYG-Plate   \n",
       "22    TimeSeries-12-11-2019-Run1P1  Sytox-YeastGates  SytoxYG-Plate   \n",
       "23    TimeSeries-12-11-2019-Run1P1  Sytox-YeastGates  SytoxYG-Plate   \n",
       "24    TimeSeries-12-11-2019-Run1P1  Sytox-YeastGates  SytoxYG-Plate   \n",
       "25    TimeSeries-12-11-2019-Run1P1  Sytox-YeastGates  SytoxYG-Plate   \n",
       "26    TimeSeries-12-11-2019-Run1P1  Sytox-YeastGates  SytoxYG-Plate   \n",
       "27    TimeSeries-12-11-2019-Run1P1  Sytox-YeastGates  SytoxYG-Plate   \n",
       "28    TimeSeries-12-11-2019-Run1P1  Sytox-YeastGates  SytoxYG-Plate   \n",
       "29    TimeSeries-12-11-2019-Run1P1  Sytox-YeastGates  SytoxYG-Plate   \n",
       "...                            ...               ...            ...   \n",
       "1478        YS-TS35C-03-05-2020-T4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "1479        YS-TS35C-03-05-2020-T4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "1480        YS-TS35C-03-05-2020-T4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "1481        YS-TS35C-03-05-2020-T4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "1482        YS-TS35C-03-05-2020-T4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "1483        YS-TS35C-03-05-2020-T4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "1484        YS-TS35C-03-05-2020-T4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "1485        YS-TS35C-03-05-2020-T4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "1486        YS-TS35C-03-05-2020-T4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "1487        YS-TS35C-03-05-2020-T4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "1488        YS-TS35C-03-05-2020-T4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "1489        YS-TS35C-03-05-2020-T4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "1490        YS-TS35C-03-05-2020-T4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "1491        YS-TS35C-03-05-2020-T4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "1492        YS-TS35C-03-05-2020-T4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "1493        YS-TS35C-03-05-2020-T4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "1494        YS-TS35C-03-05-2020-T4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "1495        YS-TS35C-03-05-2020-T4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "1496        YS-TS35C-03-05-2020-T4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "1497        YS-TS35C-03-05-2020-T4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "1498        YS-TS35C-03-05-2020-T4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "1499        YS-TS35C-03-05-2020-T4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "1500        YS-TS35C-03-05-2020-T4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "1501        YS-TS35C-03-05-2020-T4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "1502        YS-TS35C-03-05-2020-T4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "1503        YS-TS35C-03-05-2020-T4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "1504        YS-TS35C-03-05-2020-T4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "1505        YS-TS35C-03-05-2020-T4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "1506        YS-TS35C-03-05-2020-T4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "1507        YS-TS35C-03-05-2020-T4  Sytox-YeastGates  SytoxYG-Plate   \n",
       "\n",
       "                           _id       aliquot_id      cells/mL control_type  \\\n",
       "0     5e74fcffd3add00ff38d733b  aq1dvh7dssh7ewe  4.500000e+02          NaN   \n",
       "1     5e74fd1ba3b44b40d9568234  aq1dvh7dssbhf98  9.166667e+02          NaN   \n",
       "2     5e7502d723b9a6ed388174f7  aq1dvhgvrhhf3gj  3.387167e+05          NaN   \n",
       "3     5e74ffc980408659f9a161c6  aq1dvhgvrhamvqg  1.639167e+05          NaN   \n",
       "4     5e74fda2c05815244ace28b7  aq1dvhtnnh2kdb9  3.372000e+05          NaN   \n",
       "5     5e74fe1b279337c2a7aa1153  aq1dvhtnnguwdp3  1.653167e+05          NaN   \n",
       "6     5e74fe5c1339c26e49c9c472  aq1dvj68hhw8abj  3.771667e+05          NaN   \n",
       "7     5e74fd7537827b21162fa0ea  aq1dvj68hhpe4je  1.971167e+05          NaN   \n",
       "8     5e6c02528859561b561e3e19  aq1dvh7dsr6754b  6.521739e+05          NaN   \n",
       "9     5e6c0914e766fe93eaa7318e  aq1dvh7dsr67549  3.575000e+04          NaN   \n",
       "10    5e6c0561d3befe91aee87fae  aq1dvh7dst4ckyw  1.495667e+05          NaN   \n",
       "11    5e6c0915e766fe93eaa73193  aq1dvh7dst38duv  6.148333e+04          NaN   \n",
       "12    5e56eb1a7d52ef7e1cfdc983  aq1dvh7dsrq842p  1.363636e+06          NaN   \n",
       "13    5e6c0916e766fe93eaa73196  aq1dvh7dsr67547  4.177667e+05          NaN   \n",
       "14    5e56eb1a7d52ef7e1cfdc986  aq1dvh7dss2ev7y  8.333333e+05          NaN   \n",
       "15    5e6a6d8c644e7f44df302c9c  aq1dvh7dst38dux  9.366667e+03          NaN   \n",
       "16    5e6c0916e766fe93eaa73199  aq1dvh7dsr7bb8b  7.894737e+05          NaN   \n",
       "17    5e6c0917e766fe93eaa7319c  aq1dvh7dsrgdw9g  1.250000e+06          NaN   \n",
       "18    5e6c0917e766fe93eaa7319f  aq1dvh7dsrf9q5h  1.034483e+06          NaN   \n",
       "19    5e56eb1c7d52ef7e1cfdc995  aq1dvh7dsr7bb89  1.111111e+06          NaN   \n",
       "20    5e6c0918e766fe93eaa731a2  aq1dvh7dsr8fhc9  1.000000e+06          NaN   \n",
       "21    5e6c033b90977b21964e1f5c  aq1dvh7dsr8fhcb  1.111111e+06          NaN   \n",
       "22    5e6c0919e766fe93eaa731a7  aq1dvh7dssdsuhc  2.913000e+05          NaN   \n",
       "23    5e56eb1e7d52ef7e1cfdc9a1  aq1dvh7dssjbn2f  1.304348e+06          NaN   \n",
       "24    5e73ab591fc1a5fe22778470  aq1dvh7dsspvejk  1.258333e+04          NaN   \n",
       "25    5e56eb1e7d52ef7e1cfdc9a4  aq1dvh7dssjbn2h  1.071429e+06          NaN   \n",
       "26    5e6c091ae766fe93eaa731ad  aq1dvh7dssex2nb  1.060000e+04          NaN   \n",
       "27    5e6c021141bfc9f37abc5584  aq1dvh7dssqzmpm  5.000000e+05          NaN   \n",
       "28    5e56eb1f7d52ef7e1cfdc9aa  aq1dvh7dsskfu6h  1.250000e+06          NaN   \n",
       "29    5e56eb207d52ef7e1cfdc9ad  aq1dvh7dsskfu6f  1.304348e+06          NaN   \n",
       "...                        ...              ...           ...          ...   \n",
       "1478  5ef2667c31dd0f21f58bad37  aq1e6tn4gmdj8ug  3.000000e+06          NaN   \n",
       "1479  5ef2668931dd0f21f58bad70  aq1e6tn4gm3bfp8  3.000000e+06          NaN   \n",
       "1480  5ef266ca197571bba534acb0  aq1e6tn4gmrw85s  2.727273e+06          NaN   \n",
       "1481  5ef2676cfdcaed7ae4c67b47  aq1e6tn4gmk88gp  2.727273e+06          NaN   \n",
       "1482  5ef266616d07d57cb70bf1c9  aq1e6tn4gkwsp62  2.727273e+06          NaN   \n",
       "1483  5ef2676bfdcaed7ae4c67b43  aq1e6tn4gmmcemn  2.727273e+06          NaN   \n",
       "1484  5ef266d84a888501c32f6a81  aq1e6tn4gk9c4td  2.727273e+06          NaN   \n",
       "1485  5ef266bc197571bba534ac83  aq1e6tn4gmt2e9r  2.500000e+06          NaN   \n",
       "1486  5ef266ed4a888501c32f6ad7  aq1e6tn4gks8vmu  3.000000e+06          NaN   \n",
       "1487  5ef266b4197571bba534ac63  aq1e6tn4gkmq44q  2.727273e+06          NaN   \n",
       "1488  5ef266031224eb25fb26e5d6  aq1e6tn4gkjepuq  2.500000e+06          NaN   \n",
       "1489  5ef266041224eb25fb26e5dc  aq1e6tn4gm7v978  2.727273e+06          NaN   \n",
       "1490  5ef266606d07d57cb70bf1c3  aq1e6tn4gmt2e9u  2.500000e+06          NaN   \n",
       "1491  5ef26600d0a530a5a55a60f9  aq1e6tn4gmngmrm  2.307692e+06          NaN   \n",
       "1492  5ef266626d07d57cb70bf1cb  aq1e6tn4gm279j6  2.727273e+06          NaN   \n",
       "1493  5ef2671b76edeb8538512386  aq1e6tn4gmce2qf  2.500000e+06          NaN   \n",
       "1494  5ef266091224eb25fb26e5ec  aq1e6tn4gkpygcv  2.727273e+06          NaN   \n",
       "1495  5ef26776fdcaed7ae4c67b75  aq1e6tn4gmweznu  2.307692e+06    HIGH_FITC   \n",
       "1496  5ef266ea4a888501c32f6acd  aq1e6tn4gmqrzzq  2.000000e+06    HIGH_FITC   \n",
       "1497  5ef266686d07d57cb70bf1e8  aq1e6tn4gkuh9w2  2.727273e+06          NaN   \n",
       "1498  5ef2672876edeb85385123b8  aq1e6tn4gmj42ch  2.727273e+06          NaN   \n",
       "1499  5ef2677afdcaed7ae4c67b88  aq1e6tn4gkvnfzz  2.142857e+06          NaN   \n",
       "1500  5ef2666e6d07d57cb70bf205  aq1e6tn4gmgxu8k  2.500000e+06          NaN   \n",
       "1501  5ef2668431dd0f21f58bad5c  aq1e6tn4gmce2qc  2.727273e+06          NaN   \n",
       "1502  5ef265f7d0a530a5a55a60d4  aq1e6tn4gkr4pgt  2.142857e+06          NaN   \n",
       "1503  5ef2660d1224eb25fb26e5fe  aq1e6tn4gmngmrq  2.142857e+06          NaN   \n",
       "1504  5ef265f0d0a530a5a55a60ba  aq1e6tn4gm279j3  2.727273e+06          NaN   \n",
       "1505  5ef2673376edeb85385123ea  aq1e6tn4gkjeput  2.727273e+06          NaN   \n",
       "1506  5ef2676efdcaed7ae4c67b4f  aq1e6tn4gm6r33a  2.500000e+06          NaN   \n",
       "1507  5ef266d6d6c89f74262756bf  aq1e6tn4gmu6mds  3.000000e+06          NaN   \n",
       "\n",
       "     date_of_experiment                           experiment_id  \\\n",
       "0           11-Dec-2019  experiment.transcriptic.r1dvbsa7n5qwxe   \n",
       "1           11-Dec-2019  experiment.transcriptic.r1dvbsa7n5qwxe   \n",
       "2           11-Dec-2019  experiment.transcriptic.r1dvbsa7n5qwxe   \n",
       "3           11-Dec-2019  experiment.transcriptic.r1dvbsa7n5qwxe   \n",
       "4           11-Dec-2019  experiment.transcriptic.r1dvbsa7n5qwxe   \n",
       "5           11-Dec-2019  experiment.transcriptic.r1dvbsa7n5qwxe   \n",
       "6           11-Dec-2019  experiment.transcriptic.r1dvbsa7n5qwxe   \n",
       "7           11-Dec-2019  experiment.transcriptic.r1dvbsa7n5qwxe   \n",
       "8           11-Dec-2019  experiment.transcriptic.r1dvbsa7n5qwxe   \n",
       "9           11-Dec-2019  experiment.transcriptic.r1dvbsa7n5qwxe   \n",
       "10          11-Dec-2019  experiment.transcriptic.r1dvbsa7n5qwxe   \n",
       "11          11-Dec-2019  experiment.transcriptic.r1dvbsa7n5qwxe   \n",
       "12          11-Dec-2019  experiment.transcriptic.r1dvbsa7n5qwxe   \n",
       "13          11-Dec-2019  experiment.transcriptic.r1dvbsa7n5qwxe   \n",
       "14          11-Dec-2019  experiment.transcriptic.r1dvbsa7n5qwxe   \n",
       "15          11-Dec-2019  experiment.transcriptic.r1dvbsa7n5qwxe   \n",
       "16          11-Dec-2019  experiment.transcriptic.r1dvbsa7n5qwxe   \n",
       "17          11-Dec-2019  experiment.transcriptic.r1dvbsa7n5qwxe   \n",
       "18          11-Dec-2019  experiment.transcriptic.r1dvbsa7n5qwxe   \n",
       "19          11-Dec-2019  experiment.transcriptic.r1dvbsa7n5qwxe   \n",
       "20          11-Dec-2019  experiment.transcriptic.r1dvbsa7n5qwxe   \n",
       "21          11-Dec-2019  experiment.transcriptic.r1dvbsa7n5qwxe   \n",
       "22          11-Dec-2019  experiment.transcriptic.r1dvbsa7n5qwxe   \n",
       "23          11-Dec-2019  experiment.transcriptic.r1dvbsa7n5qwxe   \n",
       "24          11-Dec-2019  experiment.transcriptic.r1dvbsa7n5qwxe   \n",
       "25          11-Dec-2019  experiment.transcriptic.r1dvbsa7n5qwxe   \n",
       "26          11-Dec-2019  experiment.transcriptic.r1dvbsa7n5qwxe   \n",
       "27          11-Dec-2019  experiment.transcriptic.r1dvbsa7n5qwxe   \n",
       "28          11-Dec-2019  experiment.transcriptic.r1dvbsa7n5qwxe   \n",
       "29          11-Dec-2019  experiment.transcriptic.r1dvbsa7n5qwxe   \n",
       "...                 ...                                     ...   \n",
       "1478        05-Mar-2020  experiment.transcriptic.r1e6ge2n8x7q5p   \n",
       "1479        05-Mar-2020  experiment.transcriptic.r1e6ge2n8x7q5p   \n",
       "1480        05-Mar-2020  experiment.transcriptic.r1e6ge2n8x7q5p   \n",
       "1481        05-Mar-2020  experiment.transcriptic.r1e6ge2n8x7q5p   \n",
       "1482        05-Mar-2020  experiment.transcriptic.r1e6ge2n8x7q5p   \n",
       "1483        05-Mar-2020  experiment.transcriptic.r1e6ge2n8x7q5p   \n",
       "1484        05-Mar-2020  experiment.transcriptic.r1e6ge2n8x7q5p   \n",
       "1485        05-Mar-2020  experiment.transcriptic.r1e6ge2n8x7q5p   \n",
       "1486        05-Mar-2020  experiment.transcriptic.r1e6ge2n8x7q5p   \n",
       "1487        05-Mar-2020  experiment.transcriptic.r1e6ge2n8x7q5p   \n",
       "1488        05-Mar-2020  experiment.transcriptic.r1e6ge2n8x7q5p   \n",
       "1489        05-Mar-2020  experiment.transcriptic.r1e6ge2n8x7q5p   \n",
       "1490        05-Mar-2020  experiment.transcriptic.r1e6ge2n8x7q5p   \n",
       "1491        05-Mar-2020  experiment.transcriptic.r1e6ge2n8x7q5p   \n",
       "1492        05-Mar-2020  experiment.transcriptic.r1e6ge2n8x7q5p   \n",
       "1493        05-Mar-2020  experiment.transcriptic.r1e6ge2n8x7q5p   \n",
       "1494        05-Mar-2020  experiment.transcriptic.r1e6ge2n8x7q5p   \n",
       "1495        05-Mar-2020  experiment.transcriptic.r1e6ge2n8x7q5p   \n",
       "1496        05-Mar-2020  experiment.transcriptic.r1e6ge2n8x7q5p   \n",
       "1497        05-Mar-2020  experiment.transcriptic.r1e6ge2n8x7q5p   \n",
       "1498        05-Mar-2020  experiment.transcriptic.r1e6ge2n8x7q5p   \n",
       "1499        05-Mar-2020  experiment.transcriptic.r1e6ge2n8x7q5p   \n",
       "1500        05-Mar-2020  experiment.transcriptic.r1e6ge2n8x7q5p   \n",
       "1501        05-Mar-2020  experiment.transcriptic.r1e6ge2n8x7q5p   \n",
       "1502        05-Mar-2020  experiment.transcriptic.r1e6ge2n8x7q5p   \n",
       "1503        05-Mar-2020  experiment.transcriptic.r1e6ge2n8x7q5p   \n",
       "1504        05-Mar-2020  experiment.transcriptic.r1e6ge2n8x7q5p   \n",
       "1505        05-Mar-2020  experiment.transcriptic.r1e6ge2n8x7q5p   \n",
       "1506        05-Mar-2020  experiment.transcriptic.r1e6ge2n8x7q5p   \n",
       "1507        05-Mar-2020  experiment.transcriptic.r1e6ge2n8x7q5p   \n",
       "\n",
       "                                   experiment_reference  ...  standard_type  \\\n",
       "0     YeastSTATES-CRISPR-Short-Duration-Time-Series-...  ...            NaN   \n",
       "1     YeastSTATES-CRISPR-Short-Duration-Time-Series-...  ...            NaN   \n",
       "2     YeastSTATES-CRISPR-Short-Duration-Time-Series-...  ...            NaN   \n",
       "3     YeastSTATES-CRISPR-Short-Duration-Time-Series-...  ...            NaN   \n",
       "4     YeastSTATES-CRISPR-Short-Duration-Time-Series-...  ...            NaN   \n",
       "5     YeastSTATES-CRISPR-Short-Duration-Time-Series-...  ...            NaN   \n",
       "6     YeastSTATES-CRISPR-Short-Duration-Time-Series-...  ...            NaN   \n",
       "7     YeastSTATES-CRISPR-Short-Duration-Time-Series-...  ...            NaN   \n",
       "8     YeastSTATES-CRISPR-Short-Duration-Time-Series-...  ...            NaN   \n",
       "9     YeastSTATES-CRISPR-Short-Duration-Time-Series-...  ...            NaN   \n",
       "10    YeastSTATES-CRISPR-Short-Duration-Time-Series-...  ...            NaN   \n",
       "11    YeastSTATES-CRISPR-Short-Duration-Time-Series-...  ...            NaN   \n",
       "12    YeastSTATES-CRISPR-Short-Duration-Time-Series-...  ...            NaN   \n",
       "13    YeastSTATES-CRISPR-Short-Duration-Time-Series-...  ...            NaN   \n",
       "14    YeastSTATES-CRISPR-Short-Duration-Time-Series-...  ...            NaN   \n",
       "15    YeastSTATES-CRISPR-Short-Duration-Time-Series-...  ...            NaN   \n",
       "16    YeastSTATES-CRISPR-Short-Duration-Time-Series-...  ...            NaN   \n",
       "17    YeastSTATES-CRISPR-Short-Duration-Time-Series-...  ...            NaN   \n",
       "18    YeastSTATES-CRISPR-Short-Duration-Time-Series-...  ...            NaN   \n",
       "19    YeastSTATES-CRISPR-Short-Duration-Time-Series-...  ...            NaN   \n",
       "20    YeastSTATES-CRISPR-Short-Duration-Time-Series-...  ...            NaN   \n",
       "21    YeastSTATES-CRISPR-Short-Duration-Time-Series-...  ...            NaN   \n",
       "22    YeastSTATES-CRISPR-Short-Duration-Time-Series-...  ...            NaN   \n",
       "23    YeastSTATES-CRISPR-Short-Duration-Time-Series-...  ...            NaN   \n",
       "24    YeastSTATES-CRISPR-Short-Duration-Time-Series-...  ...            NaN   \n",
       "25    YeastSTATES-CRISPR-Short-Duration-Time-Series-...  ...            NaN   \n",
       "26    YeastSTATES-CRISPR-Short-Duration-Time-Series-...  ...            NaN   \n",
       "27    YeastSTATES-CRISPR-Short-Duration-Time-Series-...  ...            NaN   \n",
       "28    YeastSTATES-CRISPR-Short-Duration-Time-Series-...  ...            NaN   \n",
       "29    YeastSTATES-CRISPR-Short-Duration-Time-Series-...  ...            NaN   \n",
       "...                                                 ...  ...            ...   \n",
       "1478  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  ...            NaN   \n",
       "1479  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  ...            NaN   \n",
       "1480  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  ...            NaN   \n",
       "1481  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  ...            NaN   \n",
       "1482  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  ...            NaN   \n",
       "1483  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  ...            NaN   \n",
       "1484  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  ...            NaN   \n",
       "1485  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  ...            NaN   \n",
       "1486  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  ...            NaN   \n",
       "1487  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  ...            NaN   \n",
       "1488  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  ...            NaN   \n",
       "1489  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  ...            NaN   \n",
       "1490  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  ...            NaN   \n",
       "1491  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  ...            NaN   \n",
       "1492  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  ...            NaN   \n",
       "1493  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  ...            NaN   \n",
       "1494  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  ...            NaN   \n",
       "1495  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  ...            NaN   \n",
       "1496  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  ...            NaN   \n",
       "1497  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  ...            NaN   \n",
       "1498  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  ...            NaN   \n",
       "1499  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  ...            NaN   \n",
       "1500  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  ...            NaN   \n",
       "1501  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  ...            NaN   \n",
       "1502  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  ...            NaN   \n",
       "1503  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  ...            NaN   \n",
       "1504  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  ...            NaN   \n",
       "1505  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  ...            NaN   \n",
       "1506  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  ...            NaN   \n",
       "1507  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  ...            NaN   \n",
       "\n",
       "                       strain  strain_class  \\\n",
       "0               SpheroControl       Process   \n",
       "1     beads_spherotech_pps_6K       Process   \n",
       "2               SpheroControl       Process   \n",
       "3     beads_spherotech_pps_6K       Process   \n",
       "4               SpheroControl       Process   \n",
       "5     beads_spherotech_pps_6K       Process   \n",
       "6               SpheroControl       Process   \n",
       "7     beads_spherotech_pps_6K       Process   \n",
       "8                  UWBF_24926    Experiment   \n",
       "9                  UWBF_23970       Control   \n",
       "10                 UWBF_24961    Experiment   \n",
       "11                 UWBF_24961    Experiment   \n",
       "12                 UWBF_24959    Experiment   \n",
       "13                  UWBF_6390       Control   \n",
       "14                 UWBF_24959    Experiment   \n",
       "15                 UWBF_24961    Experiment   \n",
       "16                 UWBF_24926    Experiment   \n",
       "17                 UWBF_24952    Experiment   \n",
       "18                 UWBF_24952    Experiment   \n",
       "19                 UWBF_24926    Experiment   \n",
       "20                 UWBF_24926    Experiment   \n",
       "21                 UWBF_24926    Experiment   \n",
       "22                 UWBF_25784       Control   \n",
       "23                 UWBF_24962    Experiment   \n",
       "24                 UWBF_23970       Control   \n",
       "25                 UWBF_24962    Experiment   \n",
       "26                 UWBF_25784       Control   \n",
       "27                 UWBF_24963    Experiment   \n",
       "28                 UWBF_24962    Experiment   \n",
       "29                 UWBF_24962    Experiment   \n",
       "...                       ...           ...   \n",
       "1478               UWBF_24926    Experiment   \n",
       "1479               UWBF_24926    Experiment   \n",
       "1480               UWBF_24952    Experiment   \n",
       "1481               UWBF_24952    Experiment   \n",
       "1482               UWBF_24926    Experiment   \n",
       "1483               UWBF_24959    Experiment   \n",
       "1484               UWBF_24926    Experiment   \n",
       "1485               UWBF_24959    Experiment   \n",
       "1486               UWBF_24926    Experiment   \n",
       "1487               UWBF_24926    Experiment   \n",
       "1488               UWBF_24962    Experiment   \n",
       "1489               UWBF_25784       Control   \n",
       "1490               UWBF_24960    Experiment   \n",
       "1491               UWBF_24960    Experiment   \n",
       "1492               UWBF_25784       Control   \n",
       "1493               UWBF_25784       Control   \n",
       "1494               UWBF_24962    Experiment   \n",
       "1495                UWBF_6390       Control   \n",
       "1496                UWBF_6390       Control   \n",
       "1497               UWBF_24962    Experiment   \n",
       "1498               UWBF_25784       Control   \n",
       "1499               UWBF_25784       Control   \n",
       "1500               UWBF_24962    Experiment   \n",
       "1501               UWBF_24962    Experiment   \n",
       "1502               UWBF_25784       Control   \n",
       "1503               UWBF_24961    Experiment   \n",
       "1504               UWBF_24962    Experiment   \n",
       "1505               UWBF_25784       Control   \n",
       "1506               UWBF_24962    Experiment   \n",
       "1507               UWBF_24961    Experiment   \n",
       "\n",
       "                                   strain_name temperature temperature_unit  \\\n",
       "0                            Fluorescent_beads        30.0          celsius   \n",
       "1                                 Sizing_beads        30.0          celsius   \n",
       "2                            Fluorescent_beads        30.0          celsius   \n",
       "3                                 Sizing_beads        30.0          celsius   \n",
       "4                            Fluorescent_beads        30.0          celsius   \n",
       "5                                 Sizing_beads        30.0          celsius   \n",
       "6                            Fluorescent_beads        30.0          celsius   \n",
       "7                                 Sizing_beads        30.0          celsius   \n",
       "8                         CRISPR_CEN.PK2_24926        30.0          celsius   \n",
       "9        CRISPR_CEN.PK2_negative_control_23970        30.0          celsius   \n",
       "10                        CRISPR_CEN.PK2_24961        30.0          celsius   \n",
       "11                        CRISPR_CEN.PK2_24961        30.0          celsius   \n",
       "12                        CRISPR_CEN.PK2_24959        30.0          celsius   \n",
       "13    CRISPR_w303_positive_control_NOR_00_6390        30.0          celsius   \n",
       "14                        CRISPR_CEN.PK2_24959        30.0          celsius   \n",
       "15                        CRISPR_CEN.PK2_24961        30.0          celsius   \n",
       "16                        CRISPR_CEN.PK2_24926        30.0          celsius   \n",
       "17                        CRISPR_CEN.PK2_24952        30.0          celsius   \n",
       "18                        CRISPR_CEN.PK2_24952        30.0          celsius   \n",
       "19                        CRISPR_CEN.PK2_24926        30.0          celsius   \n",
       "20                        CRISPR_CEN.PK2_24926        30.0          celsius   \n",
       "21                        CRISPR_CEN.PK2_24926        30.0          celsius   \n",
       "22           CRISPR_CEN.PK2_inducible_FP_25784        30.0          celsius   \n",
       "23                        CRISPR_CEN.PK2_24962        30.0          celsius   \n",
       "24       CRISPR_CEN.PK2_negative_control_23970        30.0          celsius   \n",
       "25                        CRISPR_CEN.PK2_24962        30.0          celsius   \n",
       "26           CRISPR_CEN.PK2_inducible_FP_25784        30.0          celsius   \n",
       "27                        CRISPR_CEN.PK2_24963        30.0          celsius   \n",
       "28                        CRISPR_CEN.PK2_24962        30.0          celsius   \n",
       "29                        CRISPR_CEN.PK2_24962        30.0          celsius   \n",
       "...                                        ...         ...              ...   \n",
       "1478                      CRISPR_CEN.PK2_24926        35.0          celsius   \n",
       "1479                      CRISPR_CEN.PK2_24926        35.0          celsius   \n",
       "1480                      CRISPR_CEN.PK2_24952        35.0          celsius   \n",
       "1481                      CRISPR_CEN.PK2_24952        35.0          celsius   \n",
       "1482                      CRISPR_CEN.PK2_24926        35.0          celsius   \n",
       "1483                      CRISPR_CEN.PK2_24959        35.0          celsius   \n",
       "1484                      CRISPR_CEN.PK2_24926        35.0          celsius   \n",
       "1485                      CRISPR_CEN.PK2_24959        35.0          celsius   \n",
       "1486                      CRISPR_CEN.PK2_24926        35.0          celsius   \n",
       "1487                      CRISPR_CEN.PK2_24926        35.0          celsius   \n",
       "1488                      CRISPR_CEN.PK2_24962        35.0          celsius   \n",
       "1489         CRISPR_CEN.PK2_inducible_FP_25784        35.0          celsius   \n",
       "1490                      CRISPR_CEN.PK2_24960        35.0          celsius   \n",
       "1491                      CRISPR_CEN.PK2_24960        35.0          celsius   \n",
       "1492         CRISPR_CEN.PK2_inducible_FP_25784        35.0          celsius   \n",
       "1493         CRISPR_CEN.PK2_inducible_FP_25784        35.0          celsius   \n",
       "1494                      CRISPR_CEN.PK2_24962        35.0          celsius   \n",
       "1495  CRISPR_w303_positive_control_NOR_00_6390        35.0          celsius   \n",
       "1496  CRISPR_w303_positive_control_NOR_00_6390        35.0          celsius   \n",
       "1497                      CRISPR_CEN.PK2_24962        35.0          celsius   \n",
       "1498         CRISPR_CEN.PK2_inducible_FP_25784        35.0          celsius   \n",
       "1499         CRISPR_CEN.PK2_inducible_FP_25784        35.0          celsius   \n",
       "1500                      CRISPR_CEN.PK2_24962        35.0          celsius   \n",
       "1501                      CRISPR_CEN.PK2_24962        35.0          celsius   \n",
       "1502         CRISPR_CEN.PK2_inducible_FP_25784        35.0          celsius   \n",
       "1503                      CRISPR_CEN.PK2_24961        35.0          celsius   \n",
       "1504                      CRISPR_CEN.PK2_24962        35.0          celsius   \n",
       "1505         CRISPR_CEN.PK2_inducible_FP_25784        35.0          celsius   \n",
       "1506                      CRISPR_CEN.PK2_24962        35.0          celsius   \n",
       "1507                      CRISPR_CEN.PK2_24961        35.0          celsius   \n",
       "\n",
       "     timepoint timepoint_unit  total_counts  well  \n",
       "0         18.0           hour            27    f2  \n",
       "1         18.0           hour            55    e2  \n",
       "2         20.0           hour         20323    f2  \n",
       "3         20.0           hour          9835    e2  \n",
       "4         22.0           hour         20232    f2  \n",
       "5         22.0           hour          9919    e2  \n",
       "6         24.0           hour         22630    f2  \n",
       "7         24.0           hour         11827    e2  \n",
       "8         18.0           hour         30000    a3  \n",
       "9         18.0           hour          2145    a2  \n",
       "10        18.0           hour          8974   h12  \n",
       "11        18.0           hour          3689   h10  \n",
       "12        18.0           hour         30000    c8  \n",
       "13        18.0           hour         25066    a1  \n",
       "14        18.0           hour         30000    c9  \n",
       "15        18.0           hour           562   h11  \n",
       "16        18.0           hour         30000    a5  \n",
       "17        18.0           hour         30000    b9  \n",
       "18        18.0           hour         30000    b8  \n",
       "19        18.0           hour         30000    a4  \n",
       "20        18.0           hour         30000    a6  \n",
       "21        18.0           hour         30000    a7  \n",
       "22        18.0           hour         17478    e8  \n",
       "23        18.0           hour         30000    f4  \n",
       "24        18.0           hour           755    g1  \n",
       "25        18.0           hour         30000    f5  \n",
       "26        18.0           hour           636    e9  \n",
       "27        18.0           hour         30000    g3  \n",
       "28        18.0           hour         30000    f7  \n",
       "29        18.0           hour         30000    f6  \n",
       "...        ...            ...           ...   ...  \n",
       "1478      24.0           hour         30000    a7  \n",
       "1479      24.0           hour         30000    a5  \n",
       "1480      24.0           hour         30000    b9  \n",
       "1481      24.0           hour         30000    b8  \n",
       "1482      24.0           hour         30000    a4  \n",
       "1483      24.0           hour         30000    c8  \n",
       "1484      24.0           hour         30000    a1  \n",
       "1485      24.0           hour         30000    c9  \n",
       "1486      24.0           hour         30000    a3  \n",
       "1487      24.0           hour         30000    a2  \n",
       "1488      24.0           hour         30000    f1  \n",
       "1489      24.0           hour         30000    g5  \n",
       "1490      24.0           hour         30000    d9  \n",
       "1491      24.0           hour         30000    d8  \n",
       "1492      24.0           hour         30000    g4  \n",
       "1493      24.0           hour         30000    g6  \n",
       "1494      24.0           hour         30000    f2  \n",
       "1495      24.0           hour         30000    h9  \n",
       "1496      24.0           hour         30000    h8  \n",
       "1497      24.0           hour         30000    f3  \n",
       "1498      24.0           hour         30000    g7  \n",
       "1499      24.0           hour         30000    g3  \n",
       "1500      24.0           hour         30000    f7  \n",
       "1501      24.0           hour         30000    f6  \n",
       "1502      24.0           hour         30000    g2  \n",
       "1503      24.0           hour         30000    e8  \n",
       "1504      24.0           hour         30000    f4  \n",
       "1505      24.0           hour         30000    g1  \n",
       "1506      24.0           hour         30000    f5  \n",
       "1507      24.0           hour         30000    e9  \n",
       "\n",
       "[1508 rows x 35 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get signal prediction results\n",
    "signal_results_files = [\n",
    "    \"YeastSTATES-CRISPR-Short-Duration-Time-Series-20191208_20200610192131.csv\",\n",
    "    \"YeastSTATES-CRISPR-Short-Duration-Time-Series-35C_20200625205807.csv\"\n",
    "]\n",
    "all_pred_df = pd.DataFrame()\n",
    "for pred_file in signal_results_files:\n",
    "    pred_df = pd.read_csv(pred_file, index_col=0).reset_index()\n",
    "    #pred_df[\"experiment_id\"] = pred_file.split(\"_fc_\")[0]\n",
    "    all_pred_df = all_pred_df.append(pred_df, ignore_index=True)\n",
    "    \n",
    "all_pred_df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>strain_name</th>\n",
       "      <th>strain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fluorescent_beads</td>\n",
       "      <td>SpheroControl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sizing_beads</td>\n",
       "      <td>beads_spherotech_pps_6K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CRISPR_CEN.PK2_24926</td>\n",
       "      <td>UWBF_24926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CRISPR_CEN.PK2_negative_control_23970</td>\n",
       "      <td>UWBF_23970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CRISPR_CEN.PK2_24961</td>\n",
       "      <td>UWBF_24961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CRISPR_CEN.PK2_24959</td>\n",
       "      <td>UWBF_24959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CRISPR_w303_positive_control_NOR_00_6390</td>\n",
       "      <td>UWBF_6390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CRISPR_CEN.PK2_24952</td>\n",
       "      <td>UWBF_24952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CRISPR_CEN.PK2_inducible_FP_25784</td>\n",
       "      <td>UWBF_25784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>CRISPR_CEN.PK2_24962</td>\n",
       "      <td>UWBF_24962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>CRISPR_CEN.PK2_24963</td>\n",
       "      <td>UWBF_24963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>CRISPR_CEN.PK2_positive_control_yeVenus_32302</td>\n",
       "      <td>UWBF_32302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>CRISPR_CEN.PK2_24960</td>\n",
       "      <td>UWBF_24960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>CRISPR_CEN.PK2_positive_control_NOR_00_24864</td>\n",
       "      <td>UWBF_24864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128</th>\n",
       "      <td>Fluorescent_beads</td>\n",
       "      <td>beads_spherotech_rainbow</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        strain_name                    strain\n",
       "0                                 Fluorescent_beads             SpheroControl\n",
       "1                                      Sizing_beads   beads_spherotech_pps_6K\n",
       "8                              CRISPR_CEN.PK2_24926                UWBF_24926\n",
       "9             CRISPR_CEN.PK2_negative_control_23970                UWBF_23970\n",
       "10                             CRISPR_CEN.PK2_24961                UWBF_24961\n",
       "12                             CRISPR_CEN.PK2_24959                UWBF_24959\n",
       "13         CRISPR_w303_positive_control_NOR_00_6390                 UWBF_6390\n",
       "17                             CRISPR_CEN.PK2_24952                UWBF_24952\n",
       "22                CRISPR_CEN.PK2_inducible_FP_25784                UWBF_25784\n",
       "23                             CRISPR_CEN.PK2_24962                UWBF_24962\n",
       "27                             CRISPR_CEN.PK2_24963                UWBF_24963\n",
       "35    CRISPR_CEN.PK2_positive_control_yeVenus_32302                UWBF_32302\n",
       "37                             CRISPR_CEN.PK2_24960                UWBF_24960\n",
       "63     CRISPR_CEN.PK2_positive_control_NOR_00_24864                UWBF_24864\n",
       "1128                              Fluorescent_beads  beads_spherotech_rainbow"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_pred_df[['strain_name', 'strain']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>strain</th>\n",
       "      <th>inducer_concentration</th>\n",
       "      <th>experiment_reference</th>\n",
       "      <th>predicted_output_mean</th>\n",
       "      <th>predicted_output_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SpheroControl</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>0.183989</td>\n",
       "      <td>0.611014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UWBF_23970</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>0.067853</td>\n",
       "      <td>0.524050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UWBF_23970</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>0.002748</td>\n",
       "      <td>0.066585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UWBF_23970</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>0.005108</td>\n",
       "      <td>0.079217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UWBF_23970</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>0.000985</td>\n",
       "      <td>0.091659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>UWBF_24864</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>0.065573</td>\n",
       "      <td>0.344280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>UWBF_24864</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>0.005481</td>\n",
       "      <td>0.119235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>UWBF_24864</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>0.015800</td>\n",
       "      <td>0.128188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>UWBF_24864</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>0.013708</td>\n",
       "      <td>0.175822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>0.090055</td>\n",
       "      <td>0.253184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>0.019593</td>\n",
       "      <td>0.107548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>0.030142</td>\n",
       "      <td>0.205076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>0.055713</td>\n",
       "      <td>0.164030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>0.042588</td>\n",
       "      <td>0.243368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>0.009033</td>\n",
       "      <td>0.284054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>0.097885</td>\n",
       "      <td>0.368439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>0.020011</td>\n",
       "      <td>0.262807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>0.106668</td>\n",
       "      <td>0.447336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>0.258867</td>\n",
       "      <td>0.504358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>0.176090</td>\n",
       "      <td>0.512141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>0.166536</td>\n",
       "      <td>0.470882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>0.168899</td>\n",
       "      <td>0.509895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>0.306500</td>\n",
       "      <td>0.545641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>0.255826</td>\n",
       "      <td>0.539045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>0.398606</td>\n",
       "      <td>0.590961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>0.276262</td>\n",
       "      <td>0.615150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>0.500030</td>\n",
       "      <td>0.568076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>0.282448</td>\n",
       "      <td>0.602091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>0.580500</td>\n",
       "      <td>0.582537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>UWBF_24952</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>0.024678</td>\n",
       "      <td>0.234753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>UWBF_24963</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>0.592131</td>\n",
       "      <td>0.570475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>UWBF_24963</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>0.643400</td>\n",
       "      <td>0.556968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>UWBF_25784</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>0.297541</td>\n",
       "      <td>0.205465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>UWBF_25784</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>0.016881</td>\n",
       "      <td>0.109184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>UWBF_25784</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>0.068605</td>\n",
       "      <td>0.327230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>UWBF_25784</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>0.011792</td>\n",
       "      <td>0.124805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>UWBF_25784</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>0.235267</td>\n",
       "      <td>0.290753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>UWBF_25784</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>0.015500</td>\n",
       "      <td>0.179471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>UWBF_25784</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>0.058946</td>\n",
       "      <td>0.169185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>UWBF_25784</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>0.011967</td>\n",
       "      <td>0.117953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>UWBF_25784</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>0.018449</td>\n",
       "      <td>0.134318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>UWBF_25784</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>0.015022</td>\n",
       "      <td>0.123005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>UWBF_25784</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>0.011295</td>\n",
       "      <td>0.143511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>UWBF_25784</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>0.014026</td>\n",
       "      <td>0.115637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>UWBF_25784</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>0.012796</td>\n",
       "      <td>0.150911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>UWBF_25784</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>0.018079</td>\n",
       "      <td>0.115247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>UWBF_25784</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>0.070569</td>\n",
       "      <td>0.239162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>UWBF_25784</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>0.018500</td>\n",
       "      <td>0.196462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>UWBF_25784</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>0.086911</td>\n",
       "      <td>0.304143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>UWBF_25784</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>0.043072</td>\n",
       "      <td>0.211726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>UWBF_25784</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>0.038182</td>\n",
       "      <td>0.228913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>UWBF_25784</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>0.069200</td>\n",
       "      <td>0.290905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>UWBF_32302</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>0.004664</td>\n",
       "      <td>0.072032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>UWBF_6390</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>0.090074</td>\n",
       "      <td>0.341158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>UWBF_6390</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>0.038533</td>\n",
       "      <td>0.217119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>UWBF_6390</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>0.060133</td>\n",
       "      <td>0.251268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>UWBF_6390</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>0.037667</td>\n",
       "      <td>0.229878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>beads_spherotech_pps_6K</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-...</td>\n",
       "      <td>0.022598</td>\n",
       "      <td>0.676765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>beads_spherotech_pps_6K</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>0.061955</td>\n",
       "      <td>0.590843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>beads_spherotech_rainbow</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>0.001558</td>\n",
       "      <td>0.677492</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>167 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       strain  inducer_concentration  \\\n",
       "0               SpheroControl               0.000000   \n",
       "1                  UWBF_23970               0.000000   \n",
       "2                  UWBF_23970               0.000040   \n",
       "3                  UWBF_23970               0.000080   \n",
       "4                  UWBF_23970               0.000200   \n",
       "5                  UWBF_24864               0.000000   \n",
       "6                  UWBF_24864               0.000004   \n",
       "7                  UWBF_24864               0.000025   \n",
       "8                  UWBF_24864               0.000060   \n",
       "9                  UWBF_24926               0.000000   \n",
       "10                 UWBF_24926               0.000000   \n",
       "11                 UWBF_24926               0.000004   \n",
       "12                 UWBF_24926               0.000004   \n",
       "13                 UWBF_24926               0.000010   \n",
       "14                 UWBF_24926               0.000010   \n",
       "15                 UWBF_24926               0.000025   \n",
       "16                 UWBF_24926               0.000025   \n",
       "17                 UWBF_24926               0.000040   \n",
       "18                 UWBF_24926               0.000040   \n",
       "19                 UWBF_24926               0.000060   \n",
       "20                 UWBF_24926               0.000060   \n",
       "21                 UWBF_24926               0.000080   \n",
       "22                 UWBF_24926               0.000080   \n",
       "23                 UWBF_24926               0.000100   \n",
       "24                 UWBF_24926               0.000100   \n",
       "25                 UWBF_24926               0.000200   \n",
       "26                 UWBF_24926               0.000200   \n",
       "27                 UWBF_24926               0.000400   \n",
       "28                 UWBF_24926               0.000400   \n",
       "29                 UWBF_24952               0.000000   \n",
       "..                        ...                    ...   \n",
       "137                UWBF_24963               0.000200   \n",
       "138                UWBF_24963               0.000400   \n",
       "139                UWBF_25784               0.000000   \n",
       "140                UWBF_25784               0.000000   \n",
       "141                UWBF_25784               0.000004   \n",
       "142                UWBF_25784               0.000004   \n",
       "143                UWBF_25784               0.000010   \n",
       "144                UWBF_25784               0.000010   \n",
       "145                UWBF_25784               0.000025   \n",
       "146                UWBF_25784               0.000025   \n",
       "147                UWBF_25784               0.000040   \n",
       "148                UWBF_25784               0.000040   \n",
       "149                UWBF_25784               0.000060   \n",
       "150                UWBF_25784               0.000060   \n",
       "151                UWBF_25784               0.000080   \n",
       "152                UWBF_25784               0.000080   \n",
       "153                UWBF_25784               0.000100   \n",
       "154                UWBF_25784               0.000100   \n",
       "155                UWBF_25784               0.000200   \n",
       "156                UWBF_25784               0.000200   \n",
       "157                UWBF_25784               0.000400   \n",
       "158                UWBF_25784               0.000400   \n",
       "159                UWBF_32302               0.000000   \n",
       "160                 UWBF_6390               0.000000   \n",
       "161                 UWBF_6390               0.000000   \n",
       "162                 UWBF_6390               0.000100   \n",
       "163                 UWBF_6390               0.000400   \n",
       "164   beads_spherotech_pps_6K               0.000000   \n",
       "165   beads_spherotech_pps_6K               0.000200   \n",
       "166  beads_spherotech_rainbow               0.000010   \n",
       "\n",
       "                                  experiment_reference  predicted_output_mean  \\\n",
       "0    YeastSTATES-CRISPR-Short-Duration-Time-Series-...               0.183989   \n",
       "1    YeastSTATES-CRISPR-Short-Duration-Time-Series-...               0.067853   \n",
       "2    YeastSTATES-CRISPR-Short-Duration-Time-Series-35C               0.002748   \n",
       "3    YeastSTATES-CRISPR-Short-Duration-Time-Series-35C               0.005108   \n",
       "4    YeastSTATES-CRISPR-Short-Duration-Time-Series-35C               0.000985   \n",
       "5    YeastSTATES-CRISPR-Short-Duration-Time-Series-...               0.065573   \n",
       "6    YeastSTATES-CRISPR-Short-Duration-Time-Series-35C               0.005481   \n",
       "7    YeastSTATES-CRISPR-Short-Duration-Time-Series-35C               0.015800   \n",
       "8    YeastSTATES-CRISPR-Short-Duration-Time-Series-35C               0.013708   \n",
       "9    YeastSTATES-CRISPR-Short-Duration-Time-Series-...               0.090055   \n",
       "10   YeastSTATES-CRISPR-Short-Duration-Time-Series-35C               0.019593   \n",
       "11   YeastSTATES-CRISPR-Short-Duration-Time-Series-...               0.030142   \n",
       "12   YeastSTATES-CRISPR-Short-Duration-Time-Series-35C               0.055713   \n",
       "13   YeastSTATES-CRISPR-Short-Duration-Time-Series-...               0.042588   \n",
       "14   YeastSTATES-CRISPR-Short-Duration-Time-Series-35C               0.009033   \n",
       "15   YeastSTATES-CRISPR-Short-Duration-Time-Series-...               0.097885   \n",
       "16   YeastSTATES-CRISPR-Short-Duration-Time-Series-35C               0.020011   \n",
       "17   YeastSTATES-CRISPR-Short-Duration-Time-Series-...               0.106668   \n",
       "18   YeastSTATES-CRISPR-Short-Duration-Time-Series-35C               0.258867   \n",
       "19   YeastSTATES-CRISPR-Short-Duration-Time-Series-...               0.176090   \n",
       "20   YeastSTATES-CRISPR-Short-Duration-Time-Series-35C               0.166536   \n",
       "21   YeastSTATES-CRISPR-Short-Duration-Time-Series-...               0.168899   \n",
       "22   YeastSTATES-CRISPR-Short-Duration-Time-Series-35C               0.306500   \n",
       "23   YeastSTATES-CRISPR-Short-Duration-Time-Series-...               0.255826   \n",
       "24   YeastSTATES-CRISPR-Short-Duration-Time-Series-35C               0.398606   \n",
       "25   YeastSTATES-CRISPR-Short-Duration-Time-Series-...               0.276262   \n",
       "26   YeastSTATES-CRISPR-Short-Duration-Time-Series-35C               0.500030   \n",
       "27   YeastSTATES-CRISPR-Short-Duration-Time-Series-...               0.282448   \n",
       "28   YeastSTATES-CRISPR-Short-Duration-Time-Series-35C               0.580500   \n",
       "29   YeastSTATES-CRISPR-Short-Duration-Time-Series-...               0.024678   \n",
       "..                                                 ...                    ...   \n",
       "137  YeastSTATES-CRISPR-Short-Duration-Time-Series-...               0.592131   \n",
       "138  YeastSTATES-CRISPR-Short-Duration-Time-Series-...               0.643400   \n",
       "139  YeastSTATES-CRISPR-Short-Duration-Time-Series-...               0.297541   \n",
       "140  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C               0.016881   \n",
       "141  YeastSTATES-CRISPR-Short-Duration-Time-Series-...               0.068605   \n",
       "142  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C               0.011792   \n",
       "143  YeastSTATES-CRISPR-Short-Duration-Time-Series-...               0.235267   \n",
       "144  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C               0.015500   \n",
       "145  YeastSTATES-CRISPR-Short-Duration-Time-Series-...               0.058946   \n",
       "146  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C               0.011967   \n",
       "147  YeastSTATES-CRISPR-Short-Duration-Time-Series-...               0.018449   \n",
       "148  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C               0.015022   \n",
       "149  YeastSTATES-CRISPR-Short-Duration-Time-Series-...               0.011295   \n",
       "150  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C               0.014026   \n",
       "151  YeastSTATES-CRISPR-Short-Duration-Time-Series-...               0.012796   \n",
       "152  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C               0.018079   \n",
       "153  YeastSTATES-CRISPR-Short-Duration-Time-Series-...               0.070569   \n",
       "154  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C               0.018500   \n",
       "155  YeastSTATES-CRISPR-Short-Duration-Time-Series-...               0.086911   \n",
       "156  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C               0.043072   \n",
       "157  YeastSTATES-CRISPR-Short-Duration-Time-Series-...               0.038182   \n",
       "158  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C               0.069200   \n",
       "159  YeastSTATES-CRISPR-Short-Duration-Time-Series-...               0.004664   \n",
       "160  YeastSTATES-CRISPR-Short-Duration-Time-Series-...               0.090074   \n",
       "161  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C               0.038533   \n",
       "162  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C               0.060133   \n",
       "163  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C               0.037667   \n",
       "164  YeastSTATES-CRISPR-Short-Duration-Time-Series-...               0.022598   \n",
       "165  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C               0.061955   \n",
       "166  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C               0.001558   \n",
       "\n",
       "     predicted_output_std  \n",
       "0                0.611014  \n",
       "1                0.524050  \n",
       "2                0.066585  \n",
       "3                0.079217  \n",
       "4                0.091659  \n",
       "5                0.344280  \n",
       "6                0.119235  \n",
       "7                0.128188  \n",
       "8                0.175822  \n",
       "9                0.253184  \n",
       "10               0.107548  \n",
       "11               0.205076  \n",
       "12               0.164030  \n",
       "13               0.243368  \n",
       "14               0.284054  \n",
       "15               0.368439  \n",
       "16               0.262807  \n",
       "17               0.447336  \n",
       "18               0.504358  \n",
       "19               0.512141  \n",
       "20               0.470882  \n",
       "21               0.509895  \n",
       "22               0.545641  \n",
       "23               0.539045  \n",
       "24               0.590961  \n",
       "25               0.615150  \n",
       "26               0.568076  \n",
       "27               0.602091  \n",
       "28               0.582537  \n",
       "29               0.234753  \n",
       "..                    ...  \n",
       "137              0.570475  \n",
       "138              0.556968  \n",
       "139              0.205465  \n",
       "140              0.109184  \n",
       "141              0.327230  \n",
       "142              0.124805  \n",
       "143              0.290753  \n",
       "144              0.179471  \n",
       "145              0.169185  \n",
       "146              0.117953  \n",
       "147              0.134318  \n",
       "148              0.123005  \n",
       "149              0.143511  \n",
       "150              0.115637  \n",
       "151              0.150911  \n",
       "152              0.115247  \n",
       "153              0.239162  \n",
       "154              0.196462  \n",
       "155              0.304143  \n",
       "156              0.211726  \n",
       "157              0.228913  \n",
       "158              0.290905  \n",
       "159              0.072032  \n",
       "160              0.341158  \n",
       "161              0.217119  \n",
       "162              0.251268  \n",
       "163              0.229878  \n",
       "164              0.676765  \n",
       "165              0.590843  \n",
       "166              0.677492  \n",
       "\n",
       "[167 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_pred_filtered_df = all_pred_df[['strain', 'inducer_concentration', 'timepoint', 'experiment_reference', 'experiment_id', 'well', 'predicted_output_mean', 'predicted_output_std']]\n",
    "\n",
    "## Get the first 18 and 24 timepoints\n",
    "all_pred_filtered_df = all_pred_filtered_df.loc[(all_pred_filtered_df.timepoint == 18.0) | (all_pred_filtered_df.timepoint == 24.0)]\n",
    "\n",
    "## Compute difference in prediction between 18 and 24 timepoints\n",
    "pred_18 = all_pred_filtered_df.loc[(all_pred_filtered_df.timepoint == 18.0)].set_index(['strain', 'inducer_concentration', 'experiment_id', 'well', 'experiment_reference'])\n",
    "pred_24 = all_pred_filtered_df.loc[(all_pred_filtered_df.timepoint == 24.0)].set_index(['strain', 'inducer_concentration', 'experiment_id', 'well', 'experiment_reference'])\n",
    "assert(len(pred_18) == len(pred_24))\n",
    "\n",
    "diff = abs(pred_24['predicted_output_mean']-pred_18['predicted_output_mean'])\n",
    "diff = diff.reset_index()\n",
    "mean_std = pred_24['predicted_output_std']+pred_18['predicted_output_std']/2.0\n",
    "mean_std = mean_std.reset_index()\n",
    "\n",
    "mean_diff = diff.groupby(['strain', 'inducer_concentration', 'experiment_reference'])['predicted_output_mean'].agg(np.mean).reset_index()\n",
    "mean_diff\n",
    "mean_std = mean_std.groupby(['strain', 'inducer_concentration', 'experiment_reference'])['predicted_output_std'].agg(np.mean).reset_index()\n",
    "mean_std\n",
    "\n",
    "mean_diff = mean_diff.merge(mean_std, on=[\"strain\", \"inducer_concentration\", \"experiment_reference\"])\n",
    "mean_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['SpheroControl', 'UWBF_23970', 'UWBF_24864', 'UWBF_24926',\n",
       "       'UWBF_24952', 'UWBF_24959', 'UWBF_24960', 'UWBF_24961',\n",
       "       'UWBF_24962', 'UWBF_24963', 'UWBF_25784', 'UWBF_32302',\n",
       "       'UWBF_6390', 'beads_spherotech_pps_6K', 'beads_spherotech_rainbow'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_diff.strain.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['SpheroControl', 'UWBF_23970', 'UWBF_24864', 'UWBF_24926',\n",
       "       'UWBF_24952', 'UWBF_24959', 'UWBF_24960', 'UWBF_24961',\n",
       "       'UWBF_24962', 'UWBF_24963', 'UWBF_25784', 'UWBF_32302',\n",
       "       'UWBF_6390', 'beads_spherotech_pps_6K', 'beads_spherotech_rainbow'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get Wasserstein results\n",
    "\n",
    "wass_results_files = glob.glob(\"*fc_raw*diff_summary*csv\")\n",
    "all_wass_df = pd.DataFrame()\n",
    "for wass_file in wass_results_files:\n",
    "    wass_df = pd.read_csv(wass_file, index_col=0).reset_index().dropna().rename(columns={\"index\": \"case\"})\n",
    "    wass_df[\"experiment_reference\"] = \"_\".join(wass_file.split(\"_fc_\")[0].split(\"_\")[1:-1])\n",
    "    all_wass_df = all_wass_df.append(wass_df, ignore_index=True)\n",
    "    \n",
    "all_wass_df[\"strain\"] = all_wass_df.apply(lambda x: eval(x['case'])[0], axis=1) \n",
    "all_wass_df[\"inducer_concentration\"] = all_wass_df.apply(lambda x: eval(x['case'])[1], axis=1).astype(float)\n",
    "all_wass_df = all_wass_df.drop(columns=[\"case\"])\n",
    "\n",
    "all_wass_df.strain.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2acfe22e4c88>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(all_wass_df.wasserstein_median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "strains_to_drop = ['SpheroControl', 'beads_spherotech_pps_6K', 'beads_spherotech_rainbow', \n",
    "                   'UWBF_23970', 'UWBF_6390', 'UWBF_25784', 'UWBF_32302', 'UWBF_24864']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Mean Difference in Proportion high/total events')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_df = all_wass_df.merge(mean_diff, on=['experiment_reference', 'strain', 'inducer_concentration'])\n",
    "merged_df = merged_df[~merged_df.strain.isin(strains_to_drop)]\n",
    "merged_df['inducer_concentration'] = merged_df['inducer_concentration'].round(5)\n",
    "#merged_df = merged_df.loc[(merged_df.predicted_output_mean > 0.2) |  (merged_df.wasserstein_median < 3) ]\n",
    "#merged_df = merged_df.loc[merged_df.inducer_concentration > 0.0]\n",
    "#merged_df = merged_df.loc[(merged_df.predicted_output_mean < 0.1) &  (merged_df.wasserstein_median > 3) & (merged_df.inducer_concentration > 0.0)]\n",
    "#sns.regplot(merged_df.wasserstein_median, merged_df.predicted_output_mean)\n",
    "\n",
    "## The lower variance samples tend to disagree \n",
    "sns.scatterplot(merged_df.wasserstein_min, merged_df.predicted_output_mean, s=200*merged_df.predicted_output_std, hue=merged_df.inducer_concentration, legend='full', palette=sns.color_palette(\"RdBu\", n_colors=8))\n",
    "plt.title(\"Signal Prediction Difference vs. Wasserstein Distance (18-24h)\\nYeastSTATES-CRISPR-Short-Duration-Time-Series(30,35C)\")\n",
    "plt.xlabel(\"Min Wasserstein Distance\")\n",
    "plt.ylabel(\"Mean Difference in Proportion high/total events\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Mean Difference in Proportion high/total events')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_df = all_wass_df.merge(mean_diff, on=['experiment_reference', 'strain', 'inducer_concentration'])\n",
    "merged_df = merged_df[~merged_df.strain.isin(strains_to_drop)]\n",
    "merged_df['inducer_concentration'] = merged_df['inducer_concentration'].round(5)\n",
    "#merged_df = merged_df.loc[(merged_df.predicted_output_mean > 0.2) |  (merged_df.wasserstein_median < 3) ]\n",
    "#merged_df = merged_df.loc[merged_df.inducer_concentration > 0.0]\n",
    "#merged_df = merged_df.loc[(merged_df.predicted_output_mean < 0.1) &  (merged_df.wasserstein_median > 3) & (merged_df.inducer_concentration > 0.0)]\n",
    "#sns.regplot(merged_df.wasserstein_median, merged_df.predicted_output_mean)\n",
    "\n",
    "## The lower variance samples tend to disagree \n",
    "sns.scatterplot(merged_df.wasserstein_median, merged_df.predicted_output_mean, s=200*merged_df.predicted_output_std, hue=merged_df.inducer_concentration, legend='full', palette=sns.color_palette(\"RdBu\", n_colors=8))\n",
    "plt.title(\"Signal Prediction Difference vs. Wasserstein Distance (18-24h)\\nYeastSTATES-CRISPR-Short-Duration-Time-Series(30,35C)\")\n",
    "plt.xlabel(\"Median Wasserstein Distance\")\n",
    "plt.ylabel(\"Mean Difference in Proportion high/total events\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_df = all_wass_df.merge(mean_diff, on=['experiment_reference', 'strain', 'inducer_concentration'])\n",
    "merged_df = merged_df[~merged_df.strain.isin(strains_to_drop)]\n",
    "merged_df['inducer_concentration'] = merged_df['inducer_concentration'].round(5)\n",
    "#merged_df = merged_df.loc[(merged_df.predicted_output_mean > 0.2) |  (merged_df.wasserstein_median < 3) ]\n",
    "#merged_df = merged_df.loc[merged_df.inducer_concentration > 0.0]\n",
    "#merged_df = merged_df.loc[(merged_df.predicted_output_mean < 0.1) &  (merged_df.wasserstein_median > 3) & (merged_df.inducer_concentration > 0.0)]\n",
    "#sns.regplot(merged_df.wasserstein_median, merged_df.predicted_output_mean)\n",
    "\n",
    "## The lower variance samples tend to disagree \n",
    "plot = sns.scatterplot(merged_df.wasserstein_min, merged_df.predicted_output_mean, s=200*merged_df.predicted_output_std, hue=merged_df.inducer_concentration, legend='full', palette=sns.color_palette(\"RdBu\", n_colors=8))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>wasserstein_min</th>\n",
       "      <th>wasserstein_median</th>\n",
       "      <th>wasserstein_max</th>\n",
       "      <th>experiment_reference</th>\n",
       "      <th>strain</th>\n",
       "      <th>inducer_concentration</th>\n",
       "      <th>predicted_output_mean</th>\n",
       "      <th>predicted_output_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>5.182734</td>\n",
       "      <td>5.182734</td>\n",
       "      <td>5.182734</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.019593</td>\n",
       "      <td>0.107548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>4.673591</td>\n",
       "      <td>4.673591</td>\n",
       "      <td>4.673591</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>UWBF_24926</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.055713</td>\n",
       "      <td>0.164030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>4.760308</td>\n",
       "      <td>4.760308</td>\n",
       "      <td>4.760308</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>UWBF_24952</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015045</td>\n",
       "      <td>0.119782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>4.218812</td>\n",
       "      <td>4.218812</td>\n",
       "      <td>4.218812</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>UWBF_24952</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.017239</td>\n",
       "      <td>0.128271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>5.165200</td>\n",
       "      <td>5.165200</td>\n",
       "      <td>5.165200</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>UWBF_24961</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.095151</td>\n",
       "      <td>0.225129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>4.129357</td>\n",
       "      <td>4.129357</td>\n",
       "      <td>4.129357</td>\n",
       "      <td>YeastSTATES-CRISPR-Short-Duration-Time-Series-35C</td>\n",
       "      <td>UWBF_24961</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.125465</td>\n",
       "      <td>0.272602</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     wasserstein_min  wasserstein_median  wasserstein_max  \\\n",
       "83          5.182734            5.182734         5.182734   \n",
       "84          4.673591            4.673591         4.673591   \n",
       "92          4.760308            4.760308         4.760308   \n",
       "93          4.218812            4.218812         4.218812   \n",
       "119         5.165200            5.165200         5.165200   \n",
       "120         4.129357            4.129357         4.129357   \n",
       "\n",
       "                                  experiment_reference      strain  \\\n",
       "83   YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  UWBF_24926   \n",
       "84   YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  UWBF_24926   \n",
       "92   YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  UWBF_24952   \n",
       "93   YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  UWBF_24952   \n",
       "119  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  UWBF_24961   \n",
       "120  YeastSTATES-CRISPR-Short-Duration-Time-Series-35C  UWBF_24961   \n",
       "\n",
       "     inducer_concentration  predicted_output_mean  predicted_output_std  \n",
       "83                     0.0               0.019593              0.107548  \n",
       "84                     0.0               0.055713              0.164030  \n",
       "92                     0.0               0.015045              0.119782  \n",
       "93                     0.0               0.017239              0.128271  \n",
       "119                    0.0               0.095151              0.225129  \n",
       "120                    0.0               0.125465              0.272602  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.loc[(merged_df.predicted_output_mean < 0.2) &  (merged_df.wasserstein_median > 4) ]\n",
    "#merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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