synthetic scRNA-Seq from Random Network.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3256fad0",
   "metadata": {},
   "source": [
    "# Generate scRNA-Seq data using scBoolSeq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d9870d29",
   "metadata": {},
   "outputs": [
    {
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       "<IPython.core.display.HTML object>"
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    }
   ],
   "source": [
    "from IPython.display import HTML, display\n",
    "display(HTML('<style>''.container { width:92.5% !important; }''</style>'))\n",
    "\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import HBox, VBox\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "902bb331",
   "metadata": {},
   "outputs": [
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       "\n",
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       "\n",
       "\n",
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import plotnine as p9\n",
    "\n",
    "from sklearn import metrics\n",
    "from scipy.spatial import distance\n",
    "import itertools\n",
    "\n",
    "import bonesis\n",
    "import scboolseq\n",
    "from scboolseq import scBoolSeq\n",
    "\n",
    "from colomoto_jupyter import tabulate\n",
    "\n",
    "from sklearn.utils import Bunch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "072772ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED = 1234\n",
    "root = Path(\".\").resolve()\n",
    "here = Path(\".\").resolve()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e3d2490f",
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint_dir = here / \"multilevel/\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ad7771e",
   "metadata": {},
   "source": [
    "### Experiment config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "20772c40",
   "metadata": {},
   "outputs": [],
   "source": [
    "import mpbn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d8fa1473",
   "metadata": {},
   "outputs": [],
   "source": [
    "import booleantraces as btrace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c97f7e66",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <th>DEFENDO</th>\n",
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      "text/plain": [
       "    DEFENDO  MESENDO  ES  MESO  ECTO\n",
       "x0        0        0   0     0     1\n",
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      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confs = pd.read_csv(checkpoint_dir / \"10_configurations.csv\", index_col=0)\n",
    "confs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2c619759",
   "metadata": {},
   "outputs": [],
   "source": [
    "confs.columns = ['stable3', 'bifurcation', 'init', 'stable2', 'stable1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "04905570",
   "metadata": {},
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stable3</th>\n",
       "      <th>bifurcation</th>\n",
       "      <th>init</th>\n",
       "      <th>stable2</th>\n",
       "      <th>stable1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>x2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x10</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x11</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x17</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x20</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x23</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x24</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>x18</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>x19</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x21</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x25</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x26</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     stable3  bifurcation  init  stable2  stable1\n",
       "x0         0            0     0        0        1\n",
       "x2         0            0     1        0        1\n",
       "x3         0            0     1        0        0\n",
       "x4         0            1     1        0        0\n",
       "x7         0            0     0        0        0\n",
       "x9         0            0     0        0        1\n",
       "x10        0            1     1        1        1\n",
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       "x17        0            1     0        0        1\n",
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       "x16        1            0     0        1        1\n",
       "x18        1            0     0        1        1\n",
       "x19        1            0     0        1        1\n",
       "x21        1            0     0        0        0\n",
       "x25        1            1     1        1        1\n",
       "x26        1            1     1        1        0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2915d38e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# computing graph layout...\n"
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    },
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      "text/plain": [
       "<bonesis.domains.InfluenceGraph at 0x7366d40d2410>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = mpbn.load((checkpoint_dir/\"10_bn.bnet\").as_posix())\n",
    "_ig = f.influence_graph()\n",
    "bonesis.InfluenceGraph(_ig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "090447d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['x16', 'x18', 'x19', 'x22', 'x25', 'x27', 'x28', 'x29', 'x5', 'x7']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wcc = nx.induced_subgraph(_ig, max(nx.weakly_connected_components(_ig)))\n",
    "isolated = [_x for _x in _ig.nodes() if _x not in wcc]\n",
    "isolated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b3044fd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "ig = bonesis.InfluenceGraph(wcc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b1530648",
   "metadata": {},
   "outputs": [],
   "source": [
    "nx.nx_pydot.write_dot(ig, \"multilevel_random_network_ig.dot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ee91ceeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[f.pop(i) for i in isolated]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a65d6136",
   "metadata": {},
   "outputs": [],
   "source": [
    "confs = confs.loc[confs.index.difference(isolated), :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "838c4320",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confs.index.isin(f.keys()).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "db9698c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x0     False\n",
       "x1      True\n",
       "x10     True\n",
       "x11    False\n",
       "x12    False\n",
       "x13    False\n",
       "x14    False\n",
       "x15     True\n",
       "x17     True\n",
       "x2     False\n",
       "x20    False\n",
       "x21     True\n",
       "x23     True\n",
       "x24     True\n",
       "x26    False\n",
       "x3     False\n",
       "x4      True\n",
       "x6     False\n",
       "x8      True\n",
       "x9     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confs['bifurcation'] !=  confs['stable3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c622c5bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grounding...done in 0.0s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "init_transient = btrace.labelled_trajectory(f, confs, start=\"init\", stop=\"bifurcation\", _trans_label=\"init_to_bifurcation\")\n",
    "init_transient.shape[0] - 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bb0054da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grounding...done in 0.1s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "init_stable1 = btrace.labelled_trajectory(f, confs, start=\"init\", stop=\"stable1\", _trans_label=\"init_to_stable1\")\n",
    "init_stable1.shape[0] -  2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3a521134",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grounding...done in 0.0s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "transient_stable2 = btrace.labelled_trajectory(f, confs, start=\"bifurcation\", stop=\"stable2\", _trans_label=\"bifurcation_to_stable2\")\n",
    "transient_stable2.shape[0] - 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c264fef6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grounding...done in 0.0s\n"
     ]
    },
    {
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      "text/plain": [
       "8"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transient_stable3 = btrace.labelled_trajectory(f, confs, start=\"bifurcation\", stop=\"stable3\", _trans_label=\"bifurcation_to_stable3\")\n",
    "transient_stable3.shape[0] - 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e5122806",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39, 20)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trajectory_df = pd.concat([\n",
    "    init_transient,\n",
    "    init_stable1,\n",
    "    transient_stable2,\n",
    "    transient_stable3,\n",
    "])\n",
    "trajectory_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8200eabb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bifurcation_to_stable2_5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bifurcation_to_stable2_6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bifurcation_to_stable2_7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stable2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bifurcation_to_stable3_1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bifurcation_to_stable3_2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bifurcation_to_stable3_3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bifurcation_to_stable3_4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bifurcation_to_stable3_5</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bifurcation_to_stable3_6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bifurcation_to_stable3_7</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bifurcation_to_stable3_8</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stable3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          x0  x1  x10  x11  x12  x13  x14  x15  x17  x2  x20  \\\n",
       "init                       0   0    1    0    0    1    1    0    0   1    1   \n",
       "init_to_bifurcation_1      1   0    1    0    0    1    1    0    0   1    0   \n",
       "init_to_bifurcation_2      1   0    1    0    0    1    1    0    0   0    0   \n",
       "init_to_bifurcation_3      0   0    1    0    0    1    1    0    1   0    0   \n",
       "init_to_bifurcation_4      0   0    1    0    1    1    0    0    1   0    0   \n",
       "bifurcation                0   0    1    0    1    1    1    0    1   0    0   \n",
       "init_to_stable1_1          0   0    1    0    0    1    1    0    0   1    1   \n",
       "init_to_stable1_2          0   0    1    1    0    1    1    0    0   1    1   \n",
       "init_to_stable1_3          0   0    1    1    0    1    1    0    0   1    1   \n",
       "init_to_stable1_4          0   0    1    1    0    1    1    0    0   1    1   \n",
       "init_to_stable1_5          0   0    1    1    0    1    1    0    0   1    1   \n",
       "init_to_stable1_6          1   0    1    1    0    1    1    0    0   1    1   \n",
       "init_to_stable1_7          1   0    1    1    0    1    1    0    0   1    1   \n",
       "init_to_stable1_8          1   0    1    1    0    1    1    0    1   1    1   \n",
       "init_to_stable1_9          1   0    1    1    0    1    1    0    1   1    1   \n",
       "init_to_stable1_10         1   0    1    1    0    0    1    0    1   1    1   \n",
       "init_to_stable1_11         1   0    1    1    0    0    1    0    1   1    1   \n",
       "init_to_stable1_12         1   0    1    1    0    0    1    0    1   1    0   \n",
       "stable1                    1   0    1    1    0    0    0    0    1   1    0   \n",
       "bifurcation_to_stable2_1   0   0    1    0    1    1    1    0    1   0    0   \n",
       "bifurcation_to_stable2_2   0   0    1    0    1    0    1    0    1   0    0   \n",
       "bifurcation_to_stable2_3   0   0    1    0    1    0    1    0    1   0    0   \n",
       "bifurcation_to_stable2_4   0   0    1    1    1    0    1    0    1   0    0   \n",
       "bifurcation_to_stable2_5   0   0    1    1    1    0    1    0    1   0    0   \n",
       "bifurcation_to_stable2_6   0   0    1    1    1    0    1    0    1   0    0   \n",
       "bifurcation_to_stable2_7   0   0    1    1    1    0    1    1    1   0    0   \n",
       "stable2                    0   0    1    1    1    0    1    1    0   0    0   \n",
       "bifurcation_to_stable3_1   0   1    1    0    1    1    1    0    1   0    0   \n",
       "bifurcation_to_stable3_2   0   1    1    0    1    1    1    0    1   0    0   \n",
       "bifurcation_to_stable3_3   0   1    0    0    1    1    1    0    1   0    0   \n",
       "bifurcation_to_stable3_4   0   1    0    0    1    1    1    0    1   0    0   \n",
       "bifurcation_to_stable3_5   0   1    0    0    1    1    1    0    1   0    0   \n",
       "bifurcation_to_stable3_6   0   1    0    0    1    1    1    0    0   0    0   \n",
       "bifurcation_to_stable3_7   0   1    0    0    1    1    1    1    0   0    0   \n",
       "bifurcation_to_stable3_8   0   1    0    0    1    1    1    1    0   0    0   \n",
       "stable3                    0   1    0    0    1    1    1    1    0   0    0   \n",
       "\n",
       "                          x21  x23  x24  x26  x3  x4  x6  x8  x9  \n",
       "init                        0    1    1    1   1   1   1   0   0  \n",
       "init_to_bifurcation_1       0    1    1    1   1   1   1   0   0  \n",
       "init_to_bifurcation_2       0    1    1    1   0   1   1   0   0  \n",
       "init_to_bifurcation_3       0    1    1    1   0   1   1   0   0  \n",
       "init_to_bifurcation_4       0    1    1    1   0   1   1   0   0  \n",
       "bifurcation                 0    1    1    1   0   1   1   0   0  \n",
       "init_to_stable1_1           0    0    1    1   1   1   1   0   0  \n",
       "init_to_stable1_2           0    0    1    1   1   1   1   0   0  \n",
       "init_to_stable1_3           0    0    1    1   1   0   1   0   0  \n",
       "init_to_stable1_4           0    0    1    1   0   0   1   0   0  \n",
       "init_to_stable1_5           0    0    1    1   0   0   0   0   0  \n",
       "init_to_stable1_6           0    0    1    1   0   0   0   0   0  \n",
       "init_to_stable1_7           0    0    1    1   0   0   0   0   1  \n",
       "init_to_stable1_8           0    0    1    1   0   0   0   0   1  \n",
       "init_to_stable1_9           0    0    1    0   0   0   0   0   1  \n",
       "init_to_stable1_10          0    0    1    0   0   0   0   0   1  \n",
       "init_to_stable1_11          0    0    0    0   0   0   0   0   1  \n",
       "init_to_stable1_12          0    0    0    0   0   0   0   0   1  \n",
       "stable1                     0    0    0    0   0   0   0   0   1  \n",
       "bifurcation_to_stable2_1    0    0    1    1   0   1   1   0   0  \n",
       "bifurcation_to_stable2_2    0    0    1    1   0   1   1   0   0  \n",
       "bifurcation_to_stable2_3    0    0    1    1   0   0   1   0   0  \n",
       "bifurcation_to_stable2_4    0    0    1    1   0   0   1   0   0  \n",
       "bifurcation_to_stable2_5    0    0    1    1   0   0   0   0   0  \n",
       "bifurcation_to_stable2_6    0    0    0    1   0   0   0   0   0  \n",
       "bifurcation_to_stable2_7    0    0    0    1   0   0   0   0   0  \n",
       "stable2                     0    0    0    1   0   0   0   0   0  \n",
       "bifurcation_to_stable3_1    0    1    1    1   0   1   1   0   0  \n",
       "bifurcation_to_stable3_2    0    1    1    1   0   0   1   0   0  \n",
       "bifurcation_to_stable3_3    0    1    1    1   0   0   1   0   0  \n",
       "bifurcation_to_stable3_4    1    1    1    1   0   0   1   0   0  \n",
       "bifurcation_to_stable3_5    1    1    1    1   0   0   1   1   0  \n",
       "bifurcation_to_stable3_6    1    1    1    1   0   0   1   1   0  \n",
       "bifurcation_to_stable3_7    1    1    1    1   0   0   1   1   0  \n",
       "bifurcation_to_stable3_8    1    0    1    1   0   0   1   1   0  \n",
       "stable3                     1    0    0    1   0   0   1   1   0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traj_df = trajectory_df.drop_duplicates()\n",
    "traj_df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8056f4c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>8430408G22Rik</th>\n",
       "      <th>Plp1</th>\n",
       "      <th>Zfp947</th>\n",
       "      <th>Bhlhb9</th>\n",
       "      <th>Vps35</th>\n",
       "      <th>Slc18a1</th>\n",
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       "      <th>Plekhn1</th>\n",
       "      <th>Ankrd6</th>\n",
       "      <th>9030619P08Rik</th>\n",
       "      <th>Prtn3</th>\n",
       "      <th>Lrrn4</th>\n",
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       "      <th>Plppr3</th>\n",
       "      <th>Memo1</th>\n",
       "      <th>Cdkn1c</th>\n",
       "      <th>Gm2a</th>\n",
       "      <th>Phxr4</th>\n",
       "      <th>B2m</th>\n",
       "      <th>Tbxas1</th>\n",
       "      <th>Glipr2</th>\n",
       "      <th>Tcf15</th>\n",
       "      <th>Trim58</th>\n",
       "      <th>D930028M14Rik</th>\n",
       "      <th>1700010I14Rik</th>\n",
       "      <th>Gm10384</th>\n",
       "      <th>Sla2</th>\n",
       "      <th>Trp53i13</th>\n",
       "      <th>Gm17586</th>\n",
       "      <th>Gypc</th>\n",
       "      <th>Clic5</th>\n",
       "      <th>Tubb4b</th>\n",
       "      <th>AC149090.1</th>\n",
       "      <th>Gm26580</th>\n",
       "      <th>Clec1a</th>\n",
       "      <th>Ccl9</th>\n",
       "      <th>Gimap3</th>\n",
       "      <th>Zscan18</th>\n",
       "      <th>Spi1</th>\n",
       "      <th>Ccl6</th>\n",
       "      <th>2900041M22Rik</th>\n",
       "      <th>Klk8</th>\n",
       "      <th>Gm37637</th>\n",
       "      <th>Gp9</th>\n",
       "      <th>Idh3a</th>\n",
       "      <th>Akr1c13</th>\n",
       "      <th>2810408A11Rik</th>\n",
       "      <th>Npr2</th>\n",
       "      <th>Ephx1</th>\n",
       "      <th>Pik3ip1</th>\n",
       "      <th>ENSMUSG00000043858</th>\n",
       "      <th>Gm26789</th>\n",
       "      <th>Grina</th>\n",
       "      <th>Txnip</th>\n",
       "      <th>Gm26541</th>\n",
       "      <th>Dnajb3</th>\n",
       "      <th>Msn</th>\n",
       "      <th>Gm37124</th>\n",
       "      <th>Srd5a1</th>\n",
       "      <th>Bhlhe40</th>\n",
       "      <th>Pomgnt2</th>\n",
       "      <th>Fcer1g</th>\n",
       "      <th>Gm37298</th>\n",
       "      <th>Lmo1</th>\n",
       "      <th>Rab20</th>\n",
       "      <th>Cd69</th>\n",
       "      <th>Gm2788</th>\n",
       "      <th>Gm14276</th>\n",
       "      <th>Napa</th>\n",
       "      <th>Cd24a</th>\n",
       "      <th>Nsun2</th>\n",
       "      <th>Marveld2</th>\n",
       "      <th>Ms4a4b</th>\n",
       "      <th>Gm17590</th>\n",
       "      <th>Cdk6</th>\n",
       "      <th>Tada2b</th>\n",
       "      <th>Gm5113</th>\n",
       "      <th>Mki67</th>\n",
       "      <th>Btg2</th>\n",
       "      <th>Gnat2</th>\n",
       "      <th>Tcaf1</th>\n",
       "      <th>Slc35e4</th>\n",
       "      <th>Reep2</th>\n",
       "      <th>8430419K02Rik</th>\n",
       "      <th>Pdgfd</th>\n",
       "      <th>H2-K1</th>\n",
       "      <th>Sesn1</th>\n",
       "      <th>Bcap29</th>\n",
       "      <th>Gm21814</th>\n",
       "      <th>Eif5a</th>\n",
       "      <th>Stk25</th>\n",
       "      <th>Maged1</th>\n",
       "      <th>Zfp449</th>\n",
       "      <th>9130604C24Rik</th>\n",
       "      <th>6430590A07Rik</th>\n",
       "      <th>Tcirg1</th>\n",
       "      <th>Il4</th>\n",
       "      <th>Slc35a4</th>\n",
       "      <th>Cysltr2</th>\n",
       "      <th>Ap3m2</th>\n",
       "      <th>Dsel</th>\n",
       "      <th>Cmtm7</th>\n",
       "      <th>Arl8b</th>\n",
       "      <th>Gbx2</th>\n",
       "      <th>Smim10l2a</th>\n",
       "      <th>Cyth4</th>\n",
       "      <th>S1pr4</th>\n",
       "      <th>Gdi1</th>\n",
       "      <th>Hdhd3</th>\n",
       "      <th>Vill</th>\n",
       "      <th>Slc38a6</th>\n",
       "      <th>Calu</th>\n",
       "      <th>Gm16565</th>\n",
       "      <th>Smim3</th>\n",
       "      <th>Sar1a</th>\n",
       "      <th>4833407H14Rik</th>\n",
       "      <th>B230217O12Rik</th>\n",
       "      <th>Ccdc39</th>\n",
       "      <th>Spata2l</th>\n",
       "      <th>Arxes2</th>\n",
       "      <th>Dut</th>\n",
       "      <th>Ifngr1</th>\n",
       "      <th>Rasa3</th>\n",
       "      <th>Fbxl2</th>\n",
       "      <th>Pde4b</th>\n",
       "      <th>Ptp4a3</th>\n",
       "      <th>Cdt1</th>\n",
       "      <th>Fxyd1</th>\n",
       "      <th>Arl11</th>\n",
       "      <th>Shisa8</th>\n",
       "      <th>Dleu7</th>\n",
       "      <th>Tmem173</th>\n",
       "      <th>Atxn10</th>\n",
       "      <th>Rpn2</th>\n",
       "      <th>Ccl3</th>\n",
       "      <th>Nudt16</th>\n",
       "      <th>Kif3c</th>\n",
       "      <th>Hars</th>\n",
       "      <th>Ccl4</th>\n",
       "      <th>Myo1g</th>\n",
       "      <th>Cpq</th>\n",
       "      <th>Cox6a2</th>\n",
       "      <th>Sdc3</th>\n",
       "      <th>Dok2</th>\n",
       "      <th>Fcgrt</th>\n",
       "      <th>Elmo1</th>\n",
       "      <th>Cyc1</th>\n",
       "      <th>Calr</th>\n",
       "      <th>Gpr18</th>\n",
       "      <th>Tbxa2r</th>\n",
       "      <th>Gpr183</th>\n",
       "      <th>Naaa</th>\n",
       "      <th>Gm11110</th>\n",
       "      <th>Alad</th>\n",
       "      <th>Taz</th>\n",
       "      <th>Echdc3</th>\n",
       "      <th>Cd300a</th>\n",
       "      <th>Slc39a4</th>\n",
       "      <th>Zfp128</th>\n",
       "      <th>Gm26819</th>\n",
       "      <th>Ispd</th>\n",
       "      <th>Itm2b</th>\n",
       "      <th>Zfp647</th>\n",
       "      <th>Gm16861</th>\n",
       "      <th>Nek3</th>\n",
       "      <th>Gata2</th>\n",
       "      <th>Prss16</th>\n",
       "      <th>Lppos</th>\n",
       "      <th>Lhcgr</th>\n",
       "      <th>Fundc1</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Tnfrsf26</th>\n",
       "      <th>Prr36</th>\n",
       "      <th>Vldlr</th>\n",
       "      <th>Mfge8</th>\n",
       "      <th>Enkur</th>\n",
       "      <th>Rcor2</th>\n",
       "      <th>Ndn</th>\n",
       "      <th>Btla</th>\n",
       "      <th>Zyx</th>\n",
       "      <th>Cd33</th>\n",
       "      <th>Dkc1</th>\n",
       "      <th>Rab37</th>\n",
       "      <th>Cdcp1</th>\n",
       "      <th>Hoxa9</th>\n",
       "      <th>Oat</th>\n",
       "      <th>Vegfc</th>\n",
       "      <th>Gas6</th>\n",
       "      <th>Oasl2</th>\n",
       "      <th>Tacc1</th>\n",
       "      <th>Colgalt1</th>\n",
       "      <th>Xdh</th>\n",
       "      <th>Rab32</th>\n",
       "      <th>Dusp2</th>\n",
       "      <th>Tmem176a</th>\n",
       "      <th>Gnai3</th>\n",
       "      <th>Rnf13</th>\n",
       "      <th>Elane</th>\n",
       "      <th>Cd48</th>\n",
       "      <th>Mthfd1</th>\n",
       "      <th>Igkc</th>\n",
       "      <th>Fnta</th>\n",
       "      <th>Bok</th>\n",
       "      <th>Ripply3</th>\n",
       "      <th>Ptger2</th>\n",
       "      <th>P2rx7</th>\n",
       "      <th>Wdfy2</th>\n",
       "      <th>Eif1a</th>\n",
       "      <th>Gstm7</th>\n",
       "      <th>Tmem14c</th>\n",
       "      <th>2900018N21Rik</th>\n",
       "      <th>Mpl</th>\n",
       "      <th>Sash3</th>\n",
       "      <th>Tgm2</th>\n",
       "      <th>Gm19569</th>\n",
       "      <th>Ehd3</th>\n",
       "      <th>Cish</th>\n",
       "      <th>Calml4</th>\n",
       "      <th>Mesdc2</th>\n",
       "      <th>Nfil3</th>\n",
       "      <th>Tcn2</th>\n",
       "      <th>Saraf</th>\n",
       "      <th>Meis1</th>\n",
       "      <th>Serpina3g</th>\n",
       "      <th>Pcx</th>\n",
       "      <th>Gpr171</th>\n",
       "      <th>Cks1b</th>\n",
       "      <th>Coa5</th>\n",
       "      <th>Arhgap27os3</th>\n",
       "      <th>Farsa</th>\n",
       "      <th>Ctsl</th>\n",
       "      <th>Iigp1</th>\n",
       "      <th>P2ry14</th>\n",
       "      <th>Cd82</th>\n",
       "      <th>Slc18a2</th>\n",
       "      <th>Cd302</th>\n",
       "      <th>Parp12</th>\n",
       "      <th>Isyna1</th>\n",
       "      <th>S100a8</th>\n",
       "      <th>B130034C11Rik</th>\n",
       "      <th>Arhgap27</th>\n",
       "      <th>Klf1</th>\n",
       "      <th>Gm26740</th>\n",
       "      <th>Cd84</th>\n",
       "      <th>Bag2</th>\n",
       "      <th>Tmem123</th>\n",
       "      <th>Emilin1</th>\n",
       "      <th>4930568A12Rik</th>\n",
       "      <th>Clec11a</th>\n",
       "      <th>Mat2a</th>\n",
       "      <th>Tespa1</th>\n",
       "      <th>Wfdc17</th>\n",
       "      <th>Tspan13</th>\n",
       "      <th>Dyrk2</th>\n",
       "      <th>S100a9</th>\n",
       "      <th>F10</th>\n",
       "      <th>Pde1b</th>\n",
       "      <th>Gcnt2</th>\n",
       "      <th>Ppic</th>\n",
       "      <th>Zfp72</th>\n",
       "      <th>Gm4841</th>\n",
       "      <th>Ckb</th>\n",
       "      <th>C130013H08Rik</th>\n",
       "      <th>Spo11</th>\n",
       "      <th>Ctss</th>\n",
       "      <th>Vwa5a</th>\n",
       "      <th>Serpinb1a</th>\n",
       "      <th>Trib3</th>\n",
       "      <th>Slc28a2</th>\n",
       "      <th>Xbp1</th>\n",
       "      <th>Tsc22d3</th>\n",
       "      <th>Cd74</th>\n",
       "      <th>Nhlrc1</th>\n",
       "      <th>Rac2</th>\n",
       "      <th>Il15</th>\n",
       "      <th>Ift43</th>\n",
       "      <th>Tnfrsf13c</th>\n",
       "      <th>Gm10451</th>\n",
       "      <th>Plod2</th>\n",
       "      <th>Minpp1</th>\n",
       "      <th>Cs</th>\n",
       "      <th>Gm21967</th>\n",
       "      <th>Trim27</th>\n",
       "      <th>Rrm1</th>\n",
       "      <th>Krt18</th>\n",
       "      <th>Rap1b</th>\n",
       "      <th>Anxa2</th>\n",
       "      <th>D130040H23Rik</th>\n",
       "      <th>Tmsb4x</th>\n",
       "      <th>1810034E14Rik</th>\n",
       "      <th>2810021J22Rik</th>\n",
       "      <th>F630028O10Rik</th>\n",
       "      <th>Cyb5r3</th>\n",
       "      <th>B3gnt3</th>\n",
       "      <th>Laptm5</th>\n",
       "      <th>Adam11</th>\n",
       "      <th>Rab17</th>\n",
       "      <th>Pkia</th>\n",
       "      <th>Hist3h2ba</th>\n",
       "      <th>Cyp27a1</th>\n",
       "      <th>Zfp36</th>\n",
       "      <th>Mcm5</th>\n",
       "      <th>Ssc4d</th>\n",
       "      <th>Gm45837</th>\n",
       "      <th>Lmcd1</th>\n",
       "      <th>Fgf3</th>\n",
       "      <th>Car1</th>\n",
       "      <th>Adssl1</th>\n",
       "      <th>Il21r</th>\n",
       "      <th>Slc50a1</th>\n",
       "      <th>Serpinb9</th>\n",
       "      <th>Pttg1ip</th>\n",
       "      <th>Fam83d</th>\n",
       "      <th>Tspan32</th>\n",
       "      <th>Tnfrsf13b</th>\n",
       "      <th>Gdi2</th>\n",
       "      <th>Nim1k</th>\n",
       "      <th>Mapk12</th>\n",
       "      <th>Wfdc18</th>\n",
       "      <th>Thbs1</th>\n",
       "      <th>Chil5</th>\n",
       "      <th>Mpst</th>\n",
       "      <th>Selenop</th>\n",
       "      <th>Gm43852</th>\n",
       "      <th>Hk3</th>\n",
       "      <th>Irgm1</th>\n",
       "      <th>Nceh1</th>\n",
       "      <th>Myl10</th>\n",
       "      <th>Tmx1</th>\n",
       "      <th>Zcchc24</th>\n",
       "      <th>Acsl5</th>\n",
       "      <th>Tnfaip8</th>\n",
       "      <th>4632427E13Rik</th>\n",
       "      <th>Hspa9</th>\n",
       "      <th>Hid1</th>\n",
       "      <th>Gstt1</th>\n",
       "      <th>Il12rb1</th>\n",
       "      <th>Ficd</th>\n",
       "      <th>Bbs10</th>\n",
       "      <th>Esam</th>\n",
       "      <th>Unc93b1</th>\n",
       "      <th>Casp4</th>\n",
       "      <th>F2r</th>\n",
       "      <th>Dynlt3</th>\n",
       "      <th>Adgrg1</th>\n",
       "      <th>Tmem38a</th>\n",
       "      <th>Stap1</th>\n",
       "      <th>Cep170b</th>\n",
       "      <th>Lpxn</th>\n",
       "      <th>Frmd8</th>\n",
       "      <th>Doc2g</th>\n",
       "      <th>Ighv1-23</th>\n",
       "      <th>Hrh2</th>\n",
       "      <th>Bgn</th>\n",
       "      <th>Ifi206</th>\n",
       "      <th>Fes</th>\n",
       "      <th>Apol7e</th>\n",
       "      <th>Srl</th>\n",
       "      <th>Ifi47</th>\n",
       "      <th>Cdca7</th>\n",
       "      <th>Srm</th>\n",
       "      <th>Smagp</th>\n",
       "      <th>Zfp418</th>\n",
       "      <th>Zfp882</th>\n",
       "      <th>Mmp2</th>\n",
       "      <th>Fam32a</th>\n",
       "      <th>Flna</th>\n",
       "      <th>Lyz2</th>\n",
       "      <th>Cd53</th>\n",
       "      <th>Mapk11</th>\n",
       "      <th>Sptbn1</th>\n",
       "      <th>Mapre3</th>\n",
       "      <th>Nr0b2</th>\n",
       "      <th>Ptprc</th>\n",
       "      <th>Gulp1</th>\n",
       "      <th>Cd63</th>\n",
       "      <th>Nsg1</th>\n",
       "      <th>Fam131a</th>\n",
       "      <th>Dntt</th>\n",
       "      <th>Ifi213</th>\n",
       "      <th>Tcp1</th>\n",
       "      <th>Stard8</th>\n",
       "      <th>Clptm1l</th>\n",
       "      <th>Sigirr</th>\n",
       "      <th>Robo3</th>\n",
       "      <th>Il17rb</th>\n",
       "      <th>Mcfd2</th>\n",
       "      <th>Vpreb1</th>\n",
       "      <th>Casp12</th>\n",
       "      <th>Zbtb3</th>\n",
       "      <th>Ppt1</th>\n",
       "      <th>H1f0</th>\n",
       "      <th>C730034F03Rik</th>\n",
       "      <th>Tpst2</th>\n",
       "      <th>Pnrc1</th>\n",
       "      <th>Sdhaf2</th>\n",
       "      <th>Hnrnpdl</th>\n",
       "      <th>Cyp7b1</th>\n",
       "      <th>Ptpre</th>\n",
       "      <th>Slc25a45</th>\n",
       "      <th>Wdr35</th>\n",
       "      <th>Capg</th>\n",
       "      <th>Sf3b3</th>\n",
       "      <th>Gata1</th>\n",
       "      <th>Spry1</th>\n",
       "      <th>Abce1</th>\n",
       "      <th>Rnf180</th>\n",
       "      <th>Golph3l</th>\n",
       "      <th>P2ry10</th>\n",
       "      <th>Adgrg3</th>\n",
       "      <th>Fkbp4</th>\n",
       "      <th>F2rl2</th>\n",
       "      <th>Gbp2</th>\n",
       "      <th>Plcg2</th>\n",
       "      <th>Ccdc189</th>\n",
       "      <th>4930486L24Rik</th>\n",
       "      <th>E230001N04Rik</th>\n",
       "      <th>Itih5</th>\n",
       "      <th>Chrnb1</th>\n",
       "      <th>Gm16386</th>\n",
       "      <th>Thnsl2</th>\n",
       "      <th>Gm16712</th>\n",
       "      <th>Top2a</th>\n",
       "      <th>Gm5577</th>\n",
       "      <th>Nt5c3</th>\n",
       "      <th>Nlrp10</th>\n",
       "      <th>Ctf1</th>\n",
       "      <th>Il18bp</th>\n",
       "      <th>Il1rl2</th>\n",
       "      <th>Gm5148</th>\n",
       "      <th>Car2</th>\n",
       "      <th>Vim</th>\n",
       "      <th>A630033H20Rik</th>\n",
       "      <th>Cd81</th>\n",
       "      <th>Zfp563</th>\n",
       "      <th>Ctla2a</th>\n",
       "      <th>Mfsd2b</th>\n",
       "      <th>Idh2</th>\n",
       "      <th>Tert</th>\n",
       "      <th>Pwwp2b</th>\n",
       "      <th>Atp6ap2</th>\n",
       "      <th>Ces2g</th>\n",
       "      <th>Gba2</th>\n",
       "      <th>4931428F04Rik</th>\n",
       "      <th>Apoe</th>\n",
       "      <th>Sdha</th>\n",
       "      <th>Dlg3</th>\n",
       "      <th>Igkv4-50</th>\n",
       "      <th>Dhx58</th>\n",
       "      <th>Zfp661</th>\n",
       "      <th>Ccr9</th>\n",
       "      <th>Rgs2</th>\n",
       "      <th>Ctsc</th>\n",
       "      <th>Parp8</th>\n",
       "      <th>Tspo2</th>\n",
       "      <th>5430420F09Rik</th>\n",
       "      <th>Pear1</th>\n",
       "      <th>Ltb</th>\n",
       "      <th>Gm26512</th>\n",
       "      <th>AA467197</th>\n",
       "      <th>Unc5cl</th>\n",
       "      <th>Mtpn</th>\n",
       "      <th>Mcm6</th>\n",
       "      <th>Atp2a3</th>\n",
       "      <th>Tyms</th>\n",
       "      <th>Il12a</th>\n",
       "      <th>Celf2</th>\n",
       "      <th>Axl</th>\n",
       "      <th>Tpm4</th>\n",
       "      <th>Gm3739</th>\n",
       "      <th>Cyp2j9</th>\n",
       "      <th>Fxyd5</th>\n",
       "      <th>B230216N24Rik</th>\n",
       "      <th>Trbc2</th>\n",
       "      <th>Fam161b</th>\n",
       "      <th>Gm30948</th>\n",
       "      <th>Apbb1</th>\n",
       "      <th>Igfbp4</th>\n",
       "      <th>Pcp4l1</th>\n",
       "      <th>Rassf5</th>\n",
       "      <th>Cyp2r1</th>\n",
       "      <th>Smoc1</th>\n",
       "      <th>Srgn</th>\n",
       "      <th>Pygm</th>\n",
       "      <th>Gm43200</th>\n",
       "      <th>Gca</th>\n",
       "      <th>Arhgef6</th>\n",
       "      <th>Fah</th>\n",
       "      <th>BC017643</th>\n",
       "      <th>Ak3</th>\n",
       "      <th>Icam1</th>\n",
       "      <th>Gm10505</th>\n",
       "      <th>C1qbp</th>\n",
       "      <th>Efna1</th>\n",
       "      <th>AW112010</th>\n",
       "      <th>Tnks1bp1</th>\n",
       "      <th>Anxa1</th>\n",
       "      <th>Fgf11</th>\n",
       "      <th>Layn</th>\n",
       "      <th>Gm19331</th>\n",
       "      <th>Rbpms2</th>\n",
       "      <th>Tspan3</th>\n",
       "      <th>Ighv9-1</th>\n",
       "      <th>Gm43201</th>\n",
       "      <th>Arrdc1</th>\n",
       "      <th>Hjurp</th>\n",
       "      <th>4921507P07Rik</th>\n",
       "      <th>Sla</th>\n",
       "      <th>Ms4a6b</th>\n",
       "      <th>Zfp551</th>\n",
       "      <th>2810468N07Rik</th>\n",
       "      <th>Tnf</th>\n",
       "      <th>Serp1</th>\n",
       "      <th>Mfap2</th>\n",
       "      <th>Arhgef18</th>\n",
       "      <th>Slc25a29</th>\n",
       "      <th>Hmox1</th>\n",
       "      <th>Pkm</th>\n",
       "      <th>Arhgap15</th>\n",
       "      <th>Ddx39</th>\n",
       "      <th>Fv1</th>\n",
       "      <th>Gnb5</th>\n",
       "      <th>Tes</th>\n",
       "      <th>Lysmd2</th>\n",
       "      <th>Mmp11</th>\n",
       "      <th>Pgrmc1</th>\n",
       "      <th>Gm42979</th>\n",
       "      <th>Efna4</th>\n",
       "      <th>Uba7</th>\n",
       "      <th>Xist</th>\n",
       "      <th>Sumo3</th>\n",
       "      <th>Flt3</th>\n",
       "      <th>Mycn</th>\n",
       "      <th>1300017J02Rik</th>\n",
       "      <th>Scn1b</th>\n",
       "      <th>Rab38</th>\n",
       "      <th>ENSMUSG00000096970</th>\n",
       "      <th>Tnfaip2</th>\n",
       "      <th>Hdac2</th>\n",
       "      <th>Ccr7</th>\n",
       "      <th>Irf6</th>\n",
       "      <th>Mndal</th>\n",
       "      <th>Fut4</th>\n",
       "      <th>Casp1</th>\n",
       "      <th>Slc14a1</th>\n",
       "      <th>Rit1</th>\n",
       "      <th>Ifi203</th>\n",
       "      <th>Zfp93</th>\n",
       "      <th>Calr3</th>\n",
       "      <th>2210010C04Rik</th>\n",
       "      <th>Gm38243</th>\n",
       "      <th>Gm43313</th>\n",
       "      <th>Slc25a5</th>\n",
       "      <th>Bcam</th>\n",
       "      <th>Ighv1-74</th>\n",
       "      <th>Traf3ip3</th>\n",
       "      <th>Lgals9</th>\n",
       "      <th>Gm26982</th>\n",
       "      <th>Clec10a</th>\n",
       "      <th>Klhl8</th>\n",
       "      <th>Islr</th>\n",
       "      <th>Ighv1-76</th>\n",
       "      <th>Crlf3</th>\n",
       "      <th>Ighv1-77</th>\n",
       "      <th>Lcp2</th>\n",
       "      <th>Rapsn</th>\n",
       "      <th>Cd38</th>\n",
       "      <th>Gm37423</th>\n",
       "      <th>Selplg</th>\n",
       "      <th>Clip3</th>\n",
       "      <th>Ccdc157</th>\n",
       "      <th>Uggt2</th>\n",
       "      <th>Mpo</th>\n",
       "      <th>Eya1</th>\n",
       "      <th>Ctso</th>\n",
       "      <th>Gm26917</th>\n",
       "      <th>Rarb</th>\n",
       "      <th>Cmtm6</th>\n",
       "      <th>Metrnl</th>\n",
       "      <th>Rgs1</th>\n",
       "      <th>Ptpn6</th>\n",
       "      <th>Cd1d1</th>\n",
       "      <th>Lyrm1</th>\n",
       "      <th>Prkaa2</th>\n",
       "      <th>Gm4759</th>\n",
       "      <th>Aplp2</th>\n",
       "      <th>Gm8995</th>\n",
       "      <th>Gng11</th>\n",
       "      <th>Tmem150b</th>\n",
       "      <th>Abhd6</th>\n",
       "      <th>Gm5111</th>\n",
       "      <th>Ankrd50</th>\n",
       "      <th>Neurl3</th>\n",
       "      <th>Slc44a2</th>\n",
       "      <th>Gm128</th>\n",
       "      <th>Fam132a</th>\n",
       "      <th>2810414N06Rik</th>\n",
       "      <th>M6pr</th>\n",
       "      <th>Ankrd33b</th>\n",
       "      <th>Hexa</th>\n",
       "      <th>Pdcd1lg2</th>\n",
       "      <th>Dnajc9</th>\n",
       "      <th>Lck</th>\n",
       "      <th>Ccnb2</th>\n",
       "      <th>Wls</th>\n",
       "      <th>Gramd1a</th>\n",
       "      <th>Gm37642</th>\n",
       "      <th>St8sia4</th>\n",
       "      <th>Igkv12-44</th>\n",
       "      <th>Sqstm1</th>\n",
       "      <th>Slc25a35</th>\n",
       "      <th>Tipin</th>\n",
       "      <th>4930555A03Rik</th>\n",
       "      <th>Rn18s-rs5</th>\n",
       "      <th>Samd12</th>\n",
       "      <th>B9d1</th>\n",
       "      <th>Ncf4</th>\n",
       "      <th>Myc</th>\n",
       "      <th>Uaca</th>\n",
       "      <th>Irf9</th>\n",
       "      <th>Paics</th>\n",
       "      <th>Gstm1</th>\n",
       "      <th>Padi4</th>\n",
       "      <th>Tor3a</th>\n",
       "      <th>Rgs18</th>\n",
       "      <th>Slc26a6</th>\n",
       "      <th>Ppm1g</th>\n",
       "      <th>Zfand5</th>\n",
       "      <th>Smpd5</th>\n",
       "      <th>Hdac9</th>\n",
       "      <th>Gnl3</th>\n",
       "      <th>Ecm1</th>\n",
       "      <th>Rnase6</th>\n",
       "      <th>Lgals3bp</th>\n",
       "      <th>Slc2a10</th>\n",
       "      <th>Rdh14</th>\n",
       "      <th>Icos</th>\n",
       "      <th>Dnah1</th>\n",
       "      <th>Amt</th>\n",
       "      <th>Fut8</th>\n",
       "      <th>Itpkc</th>\n",
       "      <th>Ap1s2</th>\n",
       "      <th>Rgs11</th>\n",
       "      <th>Hpcal1</th>\n",
       "      <th>Gm43178</th>\n",
       "      <th>Rnase4</th>\n",
       "      <th>Adgrl4</th>\n",
       "      <th>Nek6</th>\n",
       "      <th>Clk1</th>\n",
       "      <th>Eya2</th>\n",
       "      <th>Csf3r</th>\n",
       "      <th>Fkbp1b</th>\n",
       "      <th>Pirb</th>\n",
       "      <th>Msmo1</th>\n",
       "      <th>Gm15133</th>\n",
       "      <th>Pim2</th>\n",
       "      <th>Cldn15</th>\n",
       "      <th>Gm28557</th>\n",
       "      <th>Ric8a</th>\n",
       "      <th>Triqk</th>\n",
       "      <th>Spns2</th>\n",
       "      <th>Cebpe</th>\n",
       "      <th>Tspan14</th>\n",
       "      <th>Itm2a</th>\n",
       "      <th>Ddx1</th>\n",
       "      <th>Sh3tc1</th>\n",
       "      <th>Slc22a18</th>\n",
       "      <th>Lcp1</th>\n",
       "      <th>Casp2</th>\n",
       "      <th>Epsti1</th>\n",
       "      <th>Ifi44</th>\n",
       "      <th>Phf11b</th>\n",
       "      <th>Ctsb</th>\n",
       "      <th>H2afy</th>\n",
       "      <th>Ctsg</th>\n",
       "      <th>Gimap9</th>\n",
       "      <th>Hsd3b7</th>\n",
       "      <th>Smpdl3a</th>\n",
       "      <th>Gm43162</th>\n",
       "      <th>Trim47</th>\n",
       "      <th>Cpa3</th>\n",
       "      <th>Cat</th>\n",
       "      <th>Cers2</th>\n",
       "      <th>Ifit3</th>\n",
       "      <th>Ifit1bl1</th>\n",
       "      <th>Slc9a3r2</th>\n",
       "      <th>Mcm3</th>\n",
       "      <th>Fscn1</th>\n",
       "      <th>Thbd</th>\n",
       "      <th>Cd93</th>\n",
       "      <th>Sept6</th>\n",
       "      <th>Irf5</th>\n",
       "      <th>Rab19</th>\n",
       "      <th>Ctsz</th>\n",
       "      <th>Rhbdf1</th>\n",
       "      <th>Wfs1</th>\n",
       "      <th>Mef2c</th>\n",
       "      <th>Blvrb</th>\n",
       "      <th>Gimap1</th>\n",
       "      <th>Gimap7</th>\n",
       "      <th>Gimap4</th>\n",
       "      <th>4930515G01Rik</th>\n",
       "      <th>Hgfac</th>\n",
       "      <th>Gimap6</th>\n",
       "      <th>Gm15201</th>\n",
       "      <th>Gimap5</th>\n",
       "      <th>BC035044</th>\n",
       "      <th>Ly6e</th>\n",
       "      <th>Hs3st1</th>\n",
       "      <th>Zfp62</th>\n",
       "      <th>Vpreb3</th>\n",
       "      <th>Kcnh2</th>\n",
       "      <th>Hn1l</th>\n",
       "      <th>Hlf</th>\n",
       "      <th>9930012K11Rik</th>\n",
       "      <th>Sgsm1</th>\n",
       "      <th>Gmpr</th>\n",
       "      <th>Lancl1</th>\n",
       "      <th>Slc16a1</th>\n",
       "      <th>Plcb2</th>\n",
       "      <th>Sdpr</th>\n",
       "      <th>Rhoh</th>\n",
       "      <th>Gm38250</th>\n",
       "      <th>Gm26532</th>\n",
       "      <th>Dock2</th>\n",
       "      <th>Pdlim2</th>\n",
       "      <th>Clk4</th>\n",
       "      <th>Nubp2</th>\n",
       "      <th>Treml2</th>\n",
       "      <th>Sell</th>\n",
       "      <th>Polm</th>\n",
       "      <th>Pglyrp2</th>\n",
       "      <th>Gm37829</th>\n",
       "      <th>Tsc22d1</th>\n",
       "      <th>9130008F23Rik</th>\n",
       "      <th>Gm37558</th>\n",
       "      <th>Gm37706</th>\n",
       "      <th>Ptprcap</th>\n",
       "      <th>Hck</th>\n",
       "      <th>Adamts10</th>\n",
       "      <th>Ly6a</th>\n",
       "      <th>Gm37598</th>\n",
       "      <th>Anxa5</th>\n",
       "      <th>Parvg</th>\n",
       "      <th>Acer2</th>\n",
       "      <th>Cd79a</th>\n",
       "      <th>Tmem175</th>\n",
       "      <th>Fcrl1</th>\n",
       "      <th>Dusp22</th>\n",
       "      <th>Ms4a4c</th>\n",
       "      <th>Bank1</th>\n",
       "      <th>Gm43149</th>\n",
       "      <th>Gatsl3</th>\n",
       "      <th>BC026585</th>\n",
       "      <th>Wfdc2</th>\n",
       "      <th>Camk1</th>\n",
       "      <th>Dgkq</th>\n",
       "      <th>Treml1</th>\n",
       "      <th>Ebi3</th>\n",
       "      <th>Gm27252</th>\n",
       "      <th>Magee1</th>\n",
       "      <th>Sdc2</th>\n",
       "      <th>Ldhb</th>\n",
       "      <th>Kmo</th>\n",
       "      <th>Bpifb5</th>\n",
       "      <th>Trim30a</th>\n",
       "      <th>Ifitm1</th>\n",
       "      <th>Ddx58</th>\n",
       "      <th>Ifitm3</th>\n",
       "      <th>Rmnd5b</th>\n",
       "      <th>Gm26772</th>\n",
       "      <th>Gm16151</th>\n",
       "      <th>Gm16150</th>\n",
       "      <th>Tagln2</th>\n",
       "      <th>3110083C13Rik</th>\n",
       "      <th>Cyp39a1</th>\n",
       "      <th>Tspyl3</th>\n",
       "      <th>Dennd1c</th>\n",
       "      <th>Slc2a1</th>\n",
       "      <th>Large1</th>\n",
       "      <th>AB124611</th>\n",
       "      <th>Gm17096</th>\n",
       "      <th>Stxbp4</th>\n",
       "      <th>Muc3a</th>\n",
       "      <th>Awat2</th>\n",
       "      <th>Plac8</th>\n",
       "      <th>AI506816</th>\n",
       "      <th>Canx</th>\n",
       "      <th>Alox5</th>\n",
       "      <th>Prdx6</th>\n",
       "      <th>F11r</th>\n",
       "      <th>Alg2</th>\n",
       "      <th>Lurap1</th>\n",
       "      <th>Ttc30b</th>\n",
       "      <th>Inpp5k</th>\n",
       "      <th>Dcdc2b</th>\n",
       "      <th>Tnfsf4</th>\n",
       "      <th>Atp6ap1</th>\n",
       "      <th>Dapp1</th>\n",
       "      <th>1500002C15Rik</th>\n",
       "      <th>Tuba4a</th>\n",
       "      <th>Gm43254</th>\n",
       "      <th>ENSMUSG00000029333</th>\n",
       "      <th>Gclm</th>\n",
       "      <th>Ruvbl2</th>\n",
       "      <th>Apobr</th>\n",
       "      <th>Nap1l3</th>\n",
       "      <th>Cct6a</th>\n",
       "      <th>Vav1</th>\n",
       "      <th>Rfc2</th>\n",
       "      <th>Gm16001</th>\n",
       "      <th>Atp1b1</th>\n",
       "      <th>Gm19708</th>\n",
       "      <th>Nradd</th>\n",
       "      <th>Ypel3</th>\n",
       "      <th>Tor4a</th>\n",
       "      <th>Gm19590</th>\n",
       "      <th>Ivns1abp</th>\n",
       "      <th>Gm15991</th>\n",
       "      <th>Upp1</th>\n",
       "      <th>Gm6157</th>\n",
       "      <th>Eif5a2</th>\n",
       "      <th>C1qtnf6</th>\n",
       "      <th>4933424M12Rik</th>\n",
       "      <th>Mageh1</th>\n",
       "      <th>Gm11696</th>\n",
       "      <th>Cbfb</th>\n",
       "      <th>Zfp422</th>\n",
       "      <th>Mfng</th>\n",
       "      <th>Klf8</th>\n",
       "      <th>Stk17b</th>\n",
       "      <th>Met</th>\n",
       "      <th>Ufsp1</th>\n",
       "      <th>Tmem150a</th>\n",
       "      <th>AI606181</th>\n",
       "      <th>Ikzf2</th>\n",
       "      <th>Slc34a2</th>\n",
       "      <th>1700066B19Rik</th>\n",
       "      <th>Mcm7</th>\n",
       "      <th>Fbxo48</th>\n",
       "      <th>Ecscr</th>\n",
       "      <th>Lyplal1</th>\n",
       "      <th>Ankrd37</th>\n",
       "      <th>Plek</th>\n",
       "      <th>Mapkbp1</th>\n",
       "      <th>Ppp1r3d</th>\n",
       "      <th>Exoc3l2</th>\n",
       "      <th>Tmem53</th>\n",
       "      <th>Wsb1</th>\n",
       "      <th>Rtkn</th>\n",
       "      <th>Tfr2</th>\n",
       "      <th>Leprot</th>\n",
       "      <th>Arhgap25</th>\n",
       "      <th>Nupr1</th>\n",
       "      <th>Plpp7</th>\n",
       "      <th>G730003C15Rik</th>\n",
       "      <th>Rsrp1</th>\n",
       "      <th>Samsn1</th>\n",
       "      <th>Prdx3</th>\n",
       "      <th>Gm37663</th>\n",
       "      <th>Itgb3</th>\n",
       "      <th>Rtn4r</th>\n",
       "      <th>Nrros</th>\n",
       "      <th>Dennd2d</th>\n",
       "      <th>Por</th>\n",
       "      <th>Tspear</th>\n",
       "      <th>Hsd17b12</th>\n",
       "      <th>Capza2</th>\n",
       "      <th>Osgin1</th>\n",
       "      <th>Spns3</th>\n",
       "      <th>Cd34</th>\n",
       "      <th>4921531C22Rik</th>\n",
       "      <th>Trmt2a</th>\n",
       "      <th>Gm26692</th>\n",
       "      <th>Col16a1</th>\n",
       "      <th>Cd46</th>\n",
       "      <th>Ptgds</th>\n",
       "      <th>Uqcrc1</th>\n",
       "      <th>Gm28512</th>\n",
       "      <th>Hacd4</th>\n",
       "      <th>Ttc39c</th>\n",
       "      <th>Igkv1-135</th>\n",
       "      <th>Fam129c</th>\n",
       "      <th>Rhof</th>\n",
       "      <th>Sdcbp</th>\n",
       "      <th>Rcsd1</th>\n",
       "      <th>Mmrn1</th>\n",
       "      <th>Ccser1</th>\n",
       "      <th>Ccdc92b</th>\n",
       "      <th>Tnfaip8l1</th>\n",
       "      <th>Tprgl</th>\n",
       "      <th>Gem</th>\n",
       "      <th>D630039A03Rik</th>\n",
       "      <th>Lrrc36</th>\n",
       "      <th>Map10</th>\n",
       "      <th>Gm38071</th>\n",
       "      <th>Ccng2</th>\n",
       "      <th>Ldha</th>\n",
       "      <th>Ramp1</th>\n",
       "      <th>Ucp2</th>\n",
       "      <th>Sdsl</th>\n",
       "      <th>Ttc4</th>\n",
       "      <th>Afap1l1</th>\n",
       "      <th>Ccnd2</th>\n",
       "      <th>Sult1a1</th>\n",
       "      <th>Ube2l6</th>\n",
       "      <th>Clec12a</th>\n",
       "      <th>Gm43643</th>\n",
       "      <th>Smtnl1</th>\n",
       "      <th>Aqp1</th>\n",
       "      <th>Large2</th>\n",
       "      <th>Creg1</th>\n",
       "      <th>Fam221a</th>\n",
       "      <th>Muc13</th>\n",
       "      <th>Slc25a11</th>\n",
       "      <th>Grap</th>\n",
       "      <th>Ncf1</th>\n",
       "      <th>B4galt4</th>\n",
       "      <th>Gm16758</th>\n",
       "      <th>Nucb1</th>\n",
       "      <th>Fam69b</th>\n",
       "      <th>Tmem35b</th>\n",
       "      <th>Mx1</th>\n",
       "      <th>Ppp1r26</th>\n",
       "      <th>Mx2</th>\n",
       "      <th>Ppp1r15a</th>\n",
       "      <th>Pdzk1ip1</th>\n",
       "      <th>Coro1a</th>\n",
       "      <th>Mob3c</th>\n",
       "      <th>Oas2</th>\n",
       "      <th>Kctd12b</th>\n",
       "      <th>Mefv</th>\n",
       "      <th>Gm12250</th>\n",
       "      <th>Sh2d5</th>\n",
       "      <th>Igtp</th>\n",
       "      <th>Ermap</th>\n",
       "      <th>Smu1</th>\n",
       "      <th>Maged2</th>\n",
       "      <th>Mcm4</th>\n",
       "      <th>Gm42576</th>\n",
       "      <th>Anxa6</th>\n",
       "      <th>1700123M08Rik</th>\n",
       "      <th>Rtp4</th>\n",
       "      <th>Sh3bgrl</th>\n",
       "      <th>Napsa</th>\n",
       "      <th>Kctd21</th>\n",
       "      <th>Chpf</th>\n",
       "      <th>Gm7160</th>\n",
       "      <th>Neurl1b</th>\n",
       "      <th>Cct3</th>\n",
       "      <th>Nkx2-3</th>\n",
       "      <th>Wdr78</th>\n",
       "      <th>Dusp1</th>\n",
       "      <th>Def8</th>\n",
       "      <th>Rbm5</th>\n",
       "      <th>BC016579</th>\n",
       "      <th>Emb</th>\n",
       "      <th>Elovl1</th>\n",
       "      <th>Myo1f</th>\n",
       "      <th>Lax1</th>\n",
       "      <th>Cyp2j6</th>\n",
       "      <th>Pstpip1</th>\n",
       "      <th>Emp1</th>\n",
       "      <th>Cd44</th>\n",
       "      <th>Mtch2</th>\n",
       "      <th>Pgm2</th>\n",
       "      <th>Krt7</th>\n",
       "      <th>Mir155hg</th>\n",
       "      <th>1110028F11Rik</th>\n",
       "      <th>Acot7</th>\n",
       "      <th>Rnd2</th>\n",
       "      <th>Irf1</th>\n",
       "      <th>H2-DMa</th>\n",
       "      <th>Zfp263</th>\n",
       "      <th>Rxra</th>\n",
       "      <th>Klhl6</th>\n",
       "      <th>A130014A01Rik</th>\n",
       "      <th>Ssbp2</th>\n",
       "      <th>Rbp1</th>\n",
       "      <th>Rsl1d1</th>\n",
       "      <th>Gjb3</th>\n",
       "      <th>Asah1</th>\n",
       "      <th>Tmem37</th>\n",
       "      <th>Spcs3</th>\n",
       "      <th>Cenpa</th>\n",
       "      <th>Gfpt2</th>\n",
       "      <th>Tmprss3</th>\n",
       "      <th>Pqlc3</th>\n",
       "      <th>Dhrs3</th>\n",
       "      <th>Jak3</th>\n",
       "      <th>Fam213b</th>\n",
       "      <th>Pafah2</th>\n",
       "      <th>Jun</th>\n",
       "      <th>Lamp1</th>\n",
       "      <th>Camsap3</th>\n",
       "      <th>Grtp1</th>\n",
       "      <th>Gm16794</th>\n",
       "      <th>Cacybp</th>\n",
       "      <th>Slfn8</th>\n",
       "      <th>Stom</th>\n",
       "      <th>H2-DMb1</th>\n",
       "      <th>H2-D1</th>\n",
       "      <th>Oas1c</th>\n",
       "      <th>Tas1r1</th>\n",
       "      <th>Rab44</th>\n",
       "      <th>4930579K19Rik</th>\n",
       "      <th>Fos</th>\n",
       "      <th>Nedd4</th>\n",
       "      <th>Cyb561</th>\n",
       "      <th>Mapkapk3</th>\n",
       "      <th>Inpp4b</th>\n",
       "      <th>Ccdc69</th>\n",
       "      <th>Slfn2</th>\n",
       "      <th>Tmem220</th>\n",
       "      <th>2610035D17Rik</th>\n",
       "      <th>Snap23</th>\n",
       "      <th>Nprl2</th>\n",
       "      <th>Rasal3</th>\n",
       "      <th>Ighv1-82</th>\n",
       "      <th>Batf</th>\n",
       "      <th>Tmem121</th>\n",
       "      <th>Rrm2</th>\n",
       "      <th>Surf4</th>\n",
       "      <th>Zfp386</th>\n",
       "      <th>Mamdc4</th>\n",
       "      <th>B230217C12Rik</th>\n",
       "      <th>Zbtb42</th>\n",
       "      <th>Sstr2</th>\n",
       "      <th>Pld2</th>\n",
       "      <th>Cers5</th>\n",
       "      <th>Ighd</th>\n",
       "      <th>Ighm</th>\n",
       "      <th>Cyp4v3</th>\n",
       "      <th>Limd2</th>\n",
       "      <th>Nlrp1a</th>\n",
       "      <th>H2-Q6</th>\n",
       "      <th>P4hb</th>\n",
       "      <th>Grn</th>\n",
       "      <th>H2-Q7</th>\n",
       "      <th>Gm10863</th>\n",
       "      <th>Hhex</th>\n",
       "      <th>Pcbp4</th>\n",
       "      <th>Tubb5</th>\n",
       "      <th>Itgb7</th>\n",
       "      <th>Gpam</th>\n",
       "      <th>Cbx5</th>\n",
       "      <th>Fam184a</th>\n",
       "      <th>Tmem106a</th>\n",
       "      <th>Adamtsl5</th>\n",
       "      <th>Cd79b</th>\n",
       "      <th>Nfe2</th>\n",
       "      <th>H2-Ob</th>\n",
       "      <th>Tcp11l2</th>\n",
       "      <th>Shisa5</th>\n",
       "      <th>Samd10</th>\n",
       "      <th>Hspa12b</th>\n",
       "      <th>Tcea2</th>\n",
       "      <th>Ctsd</th>\n",
       "      <th>Rgs19</th>\n",
       "      <th>Procr</th>\n",
       "      <th>Tnni2</th>\n",
       "      <th>Acap1</th>\n",
       "      <th>Bin2</th>\n",
       "      <th>Ropn1l</th>\n",
       "      <th>Lmbrd2</th>\n",
       "      <th>Laptm4b</th>\n",
       "      <th>Il7r</th>\n",
       "      <th>Capsl</th>\n",
       "      <th>Angpt1</th>\n",
       "      <th>Spag1</th>\n",
       "      <th>H2-Ab1</th>\n",
       "      <th>Gfi1b</th>\n",
       "      <th>H2-Aa</th>\n",
       "      <th>Ctsw</th>\n",
       "      <th>Hspa5</th>\n",
       "      <th>Tbc1d10c</th>\n",
       "      <th>Rom1</th>\n",
       "      <th>Smpd2</th>\n",
       "      <th>Ctsf</th>\n",
       "      <th>Fgd2</th>\n",
       "      <th>Cct8</th>\n",
       "      <th>Mamdc2</th>\n",
       "      <th>Stip1</th>\n",
       "      <th>Gm28043</th>\n",
       "      <th>Cd27</th>\n",
       "      <th>Rdh5</th>\n",
       "      <th>Aldoc</th>\n",
       "      <th>Prss57</th>\n",
       "      <th>Arpc1b</th>\n",
       "      <th>Fut7</th>\n",
       "      <th>Cd86</th>\n",
       "      <th>Serpinf1</th>\n",
       "      <th>Cd9</th>\n",
       "      <th>Gm37274</th>\n",
       "      <th>Wdr38</th>\n",
       "      <th>Angptl6</th>\n",
       "      <th>Smad6</th>\n",
       "      <th>Chad</th>\n",
       "      <th>Arhgdib</th>\n",
       "      <th>Eogt</th>\n",
       "      <th>Llgl2</th>\n",
       "      <th>Cst7</th>\n",
       "      <th>E130215H24Rik</th>\n",
       "      <th>Gm27201</th>\n",
       "      <th>Pcna</th>\n",
       "      <th>Rnf114</th>\n",
       "      <th>Gchfr</th>\n",
       "      <th>Cacfd1</th>\n",
       "      <th>Jam3</th>\n",
       "      <th>Fkbp1a</th>\n",
       "      <th>Eps8</th>\n",
       "      <th>Epb41l1</th>\n",
       "      <th>Traf1</th>\n",
       "      <th>Pdia3</th>\n",
       "      <th>Mgst1</th>\n",
       "      <th>Slc2a6</th>\n",
       "      <th>Spint1</th>\n",
       "      <th>Lmo2</th>\n",
       "      <th>Eng</th>\n",
       "      <th>Skap2</th>\n",
       "      <th>Vamp5</th>\n",
       "      <th>Plxna1</th>\n",
       "      <th>Klrb1f</th>\n",
       "      <th>Tmem176b</th>\n",
       "      <th>Mcm2</th>\n",
       "      <th>Slc52a3</th>\n",
       "      <th>Cct7</th>\n",
       "      <th>Ptgs1</th>\n",
       "      <th>Cnn2</th>\n",
       "      <th>Prim1</th>\n",
       "      <th>Rwdd2a</th>\n",
       "      <th>Matk</th>\n",
       "      <th>Atp5b</th>\n",
       "      <th>Cdk4</th>\n",
       "      <th>Robo4</th>\n",
       "      <th>Gm15915</th>\n",
       "      <th>Gm10101</th>\n",
       "      <th>Abhd4</th>\n",
       "      <th>Rab36</th>\n",
       "      <th>Metap2</th>\n",
       "      <th>Fzr1</th>\n",
       "      <th>Pa2g4</th>\n",
       "      <th>Smim24</th>\n",
       "      <th>Gm16104</th>\n",
       "      <th>Perp</th>\n",
       "      <th>Fig4</th>\n",
       "      <th>Myct1</th>\n",
       "      <th>Lsp1</th>\n",
       "      <th>Alpk3</th>\n",
       "      <th>Zfp28</th>\n",
       "      <th>Zfp78</th>\n",
       "      <th>Zfp773</th>\n",
       "      <th>Mill2</th>\n",
       "      <th>Gpr4</th>\n",
       "      <th>Nectin2</th>\n",
       "      <th>Tyrobp</th>\n",
       "      <th>Plekhf1</th>\n",
       "      <th>Nkg7</th>\n",
       "      <th>Osbpl1a</th>\n",
       "      <th>Slc27a6</th>\n",
       "      <th>Gm4951</th>\n",
       "      <th>Zfp438</th>\n",
       "      <th>Rab18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HSPC_025</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.392129</td>\n",
       "      <td>8.852337</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.614548</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.711045</td>\n",
       "      <td>7.977803</td>\n",
       "      <td>8.117451</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.195180</td>\n",
       "      <td>7.977803</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.173640</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.912375</td>\n",
       "      <td>8.427644</td>\n",
       "      <td>7.839405</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.189716</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.935712</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.021134</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.309000</td>\n",
       "      <td>10.293674</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.275971</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.014787</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.832751</td>\n",
       "      <td>8.774285</td>\n",
       "      <td>1.189716</td>\n",
       "      <td>1.189716</td>\n",
       "      <td>1.832751</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.118770</td>\n",
       "      <td>7.285499</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.135838</td>\n",
       "      <td>8.744431</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.852337</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.832751</td>\n",
       "      <td>1.189716</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.189716</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.225016</td>\n",
       "      <td>8.876132</td>\n",
       "      <td>7.940672</td>\n",
       "      <td>6.184685</td>\n",
       "      <td>1.189716</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.691769</td>\n",
       "      <td>1.189716</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.491677</td>\n",
       "      <td>7.161885</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.832751</td>\n",
       "      <td>6.779938</td>\n",
       "      <td>8.490658</td>\n",
       "      <td>11.925211</td>\n",
       "      <td>7.410259</td>\n",
       "      <td>3.317239</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.416407</td>\n",
       "      <td>4.242991</td>\n",
       "      <td>2.275971</td>\n",
       "      <td>1.832751</td>\n",
       "      <td>1.189716</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.894815</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.681046</td>\n",
       "      <td>1.189716</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.723202</td>\n",
       "      <td>4.141925</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.574401</td>\n",
       "      <td>7.174734</td>\n",
       "      <td>2.275971</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.614548</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.888576</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.806760</td>\n",
       "      <td>8.268934</td>\n",
       "      <td>3.491677</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.585122</td>\n",
       "      <td>2.888576</td>\n",
       "      <td>2.614548</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.344797</td>\n",
       "      <td>6.461289</td>\n",
       "      <td>7.285499</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.593666</td>\n",
       "      <td>6.657127</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.275971</td>\n",
       "      <td>8.655561</td>\n",
       "      <td>9.077708</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.832751</td>\n",
       "      <td>1.189716</td>\n",
       "      <td>7.695253</td>\n",
       "      <td>10.437884</td>\n",
       "      <td>1.832751</td>\n",
       "      <td>7.814991</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.189716</td>\n",
       "      <td>5.935712</td>\n",
       "      <td>4.588530</td>\n",
       "      <td>2.275971</td>\n",
       "      <td>9.025466</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.189716</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.118771</td>\n",
       "      <td>1.189716</td>\n",
       "      <td>1.189716</td>\n",
       "      <td>7.161885</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.103505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.888576</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.977803</td>\n",
       "      <td>6.675324</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.054751</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.101455</td>\n",
       "      <td>7.040782</td>\n",
       "      <td>9.449536</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.310278</td>\n",
       "      <td>8.495788</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.888576</td>\n",
       "      <td>8.687292</td>\n",
       "      <td>7.940672</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.189716</td>\n",
       "      <td>11.307754</td>\n",
       "      <td>7.781782</td>\n",
       "      <td>7.894815</td>\n",
       "      <td>8.361752</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.888576</td>\n",
       "      <td>9.067410</td>\n",
       "      <td>4.509590</td>\n",
       "      <td>8.310278</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.343549</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.671352</td>\n",
       "      <td>7.564672</td>\n",
       "      <td>10.551846</td>\n",
       "      <td>6.763021</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.189716</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.189716</td>\n",
       "      <td>9.272583</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.189716</td>\n",
       "      <td>8.021134</td>\n",
       "      <td>8.150349</td>\n",
       "      <td>2.275971</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.747791</td>\n",
       "      <td>8.130700</td>\n",
       "      <td>3.647281</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.593666</td>\n",
       "      <td>1.832751</td>\n",
       "      <td>1.189716</td>\n",
       "      <td>7.790156</td>\n",
       "      <td>7.421084</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.779938</td>\n",
       "      <td>2.275971</td>\n",
       "      <td>6.079295</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.275971</td>\n",
       "      <td>2.888576</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.014787</td>\n",
       "      <td>1.189716</td>\n",
       "      <td>8.182512</td>\n",
       "      <td>7.977803</td>\n",
       "      <td>4.141925</td>\n",
       "      <td>1.189716</td>\n",
       "      <td>9.826553</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.822478</td>\n",
       "      <td>8.594715</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.796659</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.273603</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.141925</td>\n",
       "      <td>1.189716</td>\n",
       "      <td>6.845691</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.189716</td>\n",
       "      <td>6.923879</td>\n",
       "      <td>2.888576</td>\n",
       "      <td>5.776244</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.118771</td>\n",
       "      <td>8.475161</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.807313</td>\n",
       "      <td>7.122636</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.711045</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.242991</td>\n",
       "      <td>7.525084</td>\n",
       "      <td>2.275971</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.033241</td>\n",
       "      <td>9.167225</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.317239</td>\n",
       "      <td>7.747791</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.816341</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.189716</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.088305</td>\n",
       "      <td>7.955639</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.315027</td>\n",
       "      <td>7.631440</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.189716</td>\n",
       "      <td>6.079295</td>\n",
       "      <td>8.356122</td>\n",
       "      <td>7.554876</td>\n",
       "      <td>9.390178</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.887028</td>\n",
       "      <td>1.832751</td>\n",
       "      <td>9.681046</td>\n",
       "      <td>9.032541</td>\n",
       "      <td>10.105184</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.282898</td>\n",
       "      <td>6.502641</td>\n",
       "      <td>3.787723</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.730490</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.992392</td>\n",
       "      <td>7.902559</td>\n",
       "      <td>8.274914</td>\n",
       "      <td>7.668250</td>\n",
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       "      <td>1.318950</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            8430408G22Rik  Plp1  Zfp947    Bhlhb9     Vps35   Slc18a1  \\\n",
       "HSPC_025              0.0   0.0     0.0  5.392129  8.852337  0.000000   \n",
       "HSPC_031              0.0   0.0     0.0  0.686872  7.637939  0.000000   \n",
       "HSPC_037              0.0   0.0     0.0  1.869808  7.938080  0.000000   \n",
       "LT-HSC_001            0.0   0.0     0.0  7.965715  5.885018  0.000000   \n",
       "HSPC_001              0.0   0.0     0.0  8.395500  0.000000  0.377367   \n",
       "\n",
       "             Fam107b  Gm14230   Plekhn1  Ankrd6  9030619P08Rik     Prtn3  \\\n",
       "HSPC_025    2.614548      0.0  0.000000     0.0       6.711045  7.977803   \n",
       "HSPC_031    6.838205      0.0  0.000000     0.0       8.951078  8.584765   \n",
       "HSPC_037    9.405107      0.0  0.000000     0.0       7.199831  9.495433   \n",
       "LT-HSC_001  8.962827      0.0  0.000000     0.0       2.364517  8.080749   \n",
       "HSPC_001    0.676211      0.0  0.676211     0.0       0.000000  8.923709   \n",
       "\n",
       "               Lrrn4  Mrgpre   Gm25926    Plppr3     Memo1    Cdkn1c  \\\n",
       "HSPC_025    8.117451     0.0  0.000000  8.195180  7.977803  0.000000   \n",
       "HSPC_031    0.000000     0.0  0.000000  6.956259  7.579348  0.000000   \n",
       "HSPC_037    0.000000     0.0  0.000000  7.394504  3.164226  0.000000   \n",
       "LT-HSC_001  0.000000     0.0  4.137438  3.749470  2.364517  0.000000   \n",
       "HSPC_001    0.377367     0.0  0.000000  8.851780  9.012407  7.123321   \n",
       "\n",
       "                Gm2a  Phxr4        B2m    Tbxas1    Glipr2  Tcf15    Trim58  \\\n",
       "HSPC_025    9.173640    0.0  12.912375  8.427644  7.839405    0.0  1.189716   \n",
       "HSPC_031    4.175851    0.0  13.622584  7.800662  0.000000    0.0  0.686872   \n",
       "HSPC_037    8.086368    0.0  13.104234  9.083711  8.014129    0.0  0.000000   \n",
       "LT-HSC_001  7.347331    0.0  13.944537  8.516418  3.749470    0.0  0.000000   \n",
       "HSPC_001    7.640356    0.0  12.746870  9.232729  9.914646    0.0  0.000000   \n",
       "\n",
       "            D930028M14Rik  1700010I14Rik   Gm10384      Sla2  Trp53i13  \\\n",
       "HSPC_025              0.0            0.0  0.000000  0.000000  5.935712   \n",
       "HSPC_031              0.0            0.0  0.686872  0.000000  0.000000   \n",
       "HSPC_037              0.0            0.0  0.000000  8.418471  0.000000   \n",
       "LT-HSC_001            0.0            0.0  0.000000  0.000000  0.000000   \n",
       "HSPC_001              0.0            0.0  5.069857  0.000000  0.000000   \n",
       "\n",
       "            Gm17586      Gypc     Clic5    Tubb4b  AC149090.1   Gm26580  \\\n",
       "HSPC_025        0.0  8.021134  0.000000  7.309000   10.293674  0.000000   \n",
       "HSPC_031        0.0  7.018651  1.150286  3.253543    7.387780  0.686872   \n",
       "HSPC_037        0.0  2.657566  0.000000  8.337902   10.692584  0.000000   \n",
       "LT-HSC_001      0.0  7.552600  0.000000  3.749470    9.893764  6.820748   \n",
       "HSPC_001        0.0  1.628905  0.000000  9.412471   11.118833  0.000000   \n",
       "\n",
       "              Clec1a      Ccl9    Gimap3  Zscan18      Spi1  Ccl6  \\\n",
       "HSPC_025    0.000000  2.275971  0.000000      0.0  9.014787   0.0   \n",
       "HSPC_031    0.000000  0.000000  0.000000      0.0  6.348589   0.0   \n",
       "HSPC_037    1.218731  8.500077  0.000000      0.0  8.308001   0.0   \n",
       "LT-HSC_001  2.364517  0.000000  0.000000      0.0  6.872760   0.0   \n",
       "HSPC_001    0.000000  0.676211  0.377367      0.0  6.802432   0.0   \n",
       "\n",
       "            2900041M22Rik      Klk8   Gm37637       Gp9     Idh3a   Akr1c13  \\\n",
       "HSPC_025         0.000000  0.000000  0.000000  1.832751  8.774285  1.189716   \n",
       "HSPC_031         0.686872  0.686872  0.000000  0.000000  2.827390  8.409573   \n",
       "HSPC_037         0.000000  7.275944  0.000000  0.000000  8.862937  0.000000   \n",
       "LT-HSC_001       0.000000  7.520353  0.000000  0.000000  2.364517  0.000000   \n",
       "HSPC_001         0.000000  4.414804  0.377367  7.107768  8.010188  5.867946   \n",
       "\n",
       "            2810408A11Rik      Npr2  Ephx1   Pik3ip1  ENSMUSG00000043858  \\\n",
       "HSPC_025         1.189716  1.832751    0.0  3.118770            7.285499   \n",
       "HSPC_031         0.686872  1.782055    0.0  8.765434            8.059983   \n",
       "HSPC_037         0.000000  1.218731    0.0  8.954567            2.316800   \n",
       "LT-HSC_001       0.000000  3.217169    0.0  7.232592            7.107933   \n",
       "HSPC_001         0.000000  9.303379    0.0  8.824449            1.134796   \n",
       "\n",
       "            Gm26789     Grina      Txnip   Gm26541  Dnajb3       Msn  Gm37124  \\\n",
       "HSPC_025        0.0  7.135838   8.744431  0.000000     0.0  8.852337      0.0   \n",
       "HSPC_031        0.0  8.638735   9.676164  0.686872     0.0  1.782055      0.0   \n",
       "HSPC_037        0.0  2.316800  11.106246  0.000000     0.0  8.510617      0.0   \n",
       "LT-HSC_001      0.0  8.564652  11.130502  0.000000     0.0  9.931038      0.0   \n",
       "HSPC_001        0.0  1.482235   8.987981  0.000000     0.0  1.318950      0.0   \n",
       "\n",
       "            Srd5a1  Bhlhe40   Pomgnt2    Fcer1g  Gm37298  Lmo1  Rab20  \\\n",
       "HSPC_025       0.0      0.0  1.832751  1.189716      0.0   0.0    0.0   \n",
       "HSPC_031       0.0      0.0  1.150286  1.150286      0.0   0.0    0.0   \n",
       "HSPC_037       0.0      0.0  1.869808  5.397461      0.0   0.0    0.0   \n",
       "LT-HSC_001     0.0      0.0  0.000000  7.584142      0.0   0.0    0.0   \n",
       "HSPC_001       0.0      0.0  0.377367  0.000000      0.0   0.0    0.0   \n",
       "\n",
       "                Cd69  Gm2788   Gm14276      Napa      Cd24a     Nsun2  \\\n",
       "HSPC_025    1.189716     0.0  0.000000  7.225016   8.876132  7.940672   \n",
       "HSPC_031    0.000000     0.0  0.000000  9.235683  10.222019  2.946297   \n",
       "HSPC_037    2.316800     0.0  0.000000  8.006701   9.250198  6.744074   \n",
       "LT-HSC_001  8.397178     0.0  5.095857  8.414827   9.184807  3.217169   \n",
       "HSPC_001    0.377367     0.0  0.000000  1.482235   7.239191  8.617097   \n",
       "\n",
       "            Marveld2    Ms4a4b  Gm17590       Cdk6    Tada2b  Gm5113  \\\n",
       "HSPC_025    6.184685  1.189716      0.0   8.691769  1.189716     0.0   \n",
       "HSPC_031    0.000000  0.000000      0.0   9.531507  0.000000     0.0   \n",
       "HSPC_037    1.869808  0.000000      0.0  10.085909  0.000000     0.0   \n",
       "LT-HSC_001  0.000000  2.364517      0.0   4.442877  0.000000     0.0   \n",
       "HSPC_001    0.000000  0.000000      0.0   2.921687  0.000000     0.0   \n",
       "\n",
       "               Mki67      Btg2  Gnat2  Tcaf1  Slc35e4     Reep2  \\\n",
       "HSPC_025    3.491677  7.161885    0.0    0.0      0.0  1.832751   \n",
       "HSPC_031    1.500480  0.686872    0.0    0.0      0.0  0.000000   \n",
       "HSPC_037    1.218731  6.928313    0.0    0.0      0.0  0.000000   \n",
       "LT-HSC_001  2.364517  0.000000    0.0    0.0      0.0  0.000000   \n",
       "HSPC_001    1.762030  0.000000    0.0    0.0      0.0  0.000000   \n",
       "\n",
       "            8430419K02Rik     Pdgfd      H2-K1     Sesn1    Bcap29   Gm21814  \\\n",
       "HSPC_025         6.779938  8.490658  11.925211  7.410259  3.317239  0.000000   \n",
       "HSPC_031         0.000000  0.000000  11.665664  9.216587  5.672502  0.000000   \n",
       "HSPC_037         0.000000  1.218731  12.131326  5.568630  8.093396  1.218731   \n",
       "LT-HSC_001       0.000000  0.000000  13.092126  4.137438  3.217169  0.000000   \n",
       "HSPC_001         0.000000  0.000000  12.396286  0.000000  0.676211  0.000000   \n",
       "\n",
       "                Eif5a     Stk25    Maged1    Zfp449  9130604C24Rik  \\\n",
       "HSPC_025    10.416407  4.242991  2.275971  1.832751       1.189716   \n",
       "HSPC_031     9.484386  0.000000  1.150286  0.000000       0.000000   \n",
       "HSPC_037    10.313640  2.657566  1.869808  1.218731       0.000000   \n",
       "LT-HSC_001   9.639814  3.217169  8.187283  0.000000       0.000000   \n",
       "HSPC_001    11.169149  0.377367  9.699115  0.000000       0.000000   \n",
       "\n",
       "            6430590A07Rik    Tcirg1       Il4   Slc35a4   Cysltr2  Ap3m2  \\\n",
       "HSPC_025         0.000000  7.894815  0.000000  9.681046  1.189716    0.0   \n",
       "HSPC_031         0.000000  0.000000  0.000000  6.991254  0.000000    0.0   \n",
       "HSPC_037         7.682451  1.869808  1.218731  2.933024  0.000000    0.0   \n",
       "LT-HSC_001       0.000000  3.749470  6.074609  2.364517  2.364517    0.0   \n",
       "HSPC_001         0.377367  0.000000  0.000000  8.285068  0.000000    0.0   \n",
       "\n",
       "            Dsel      Cmtm7     Arl8b      Gbx2  Smim10l2a     Cyth4  \\\n",
       "HSPC_025     0.0   9.723202  4.141925  0.000000        0.0  7.574401   \n",
       "HSPC_031     0.0   9.159273  3.158217  7.403453        0.0  8.372889   \n",
       "HSPC_037     0.0   9.998750  7.772788  0.000000        0.0  1.218731   \n",
       "LT-HSC_001   0.0   8.742914  8.888979  0.000000        0.0  0.000000   \n",
       "HSPC_001     0.0  11.201561  0.923649  0.377367        0.0  0.676211   \n",
       "\n",
       "               S1pr4      Gdi1  Hdhd3      Vill   Slc38a6      Calu   Gm16565  \\\n",
       "HSPC_025    7.174734  2.275971    0.0  2.614548  0.000000  2.888576  0.000000   \n",
       "HSPC_031    8.587055  1.500480    0.0  8.401790  0.686872  2.017546  0.686872   \n",
       "HSPC_037    1.218731  8.148427    0.0  1.218731  0.000000  2.657566  1.218731   \n",
       "LT-HSC_001  8.227787  3.217169    0.0  2.364517  0.000000  8.685906  0.000000   \n",
       "HSPC_001    5.216270  9.912410    0.0  8.639983  0.000000  2.374501  7.993360   \n",
       "\n",
       "               Smim3     Sar1a  4833407H14Rik  B230217O12Rik  Ccdc39  Spata2l  \\\n",
       "HSPC_025    7.806760  8.268934       3.491677            0.0     0.0      0.0   \n",
       "HSPC_031    0.686872  1.500480       7.574740            0.0     0.0      0.0   \n",
       "HSPC_037    8.621744  7.815920       0.000000            0.0     0.0      0.0   \n",
       "LT-HSC_001  7.271861  9.312700       8.914017            0.0     0.0      0.0   \n",
       "HSPC_001    0.000000  7.224846       0.676211            0.0     0.0      0.0   \n",
       "\n",
       "            Arxes2       Dut    Ifngr1     Rasa3     Fbxl2     Pde4b  \\\n",
       "HSPC_025       0.0  8.585122  2.888576  2.614548  0.000000  8.344797   \n",
       "HSPC_031       0.0  7.527841  3.342958  0.000000  1.150286  8.598449   \n",
       "HSPC_037       0.0  4.637700  8.546913  2.316800  0.000000  3.538458   \n",
       "LT-HSC_001     0.0  7.487369  4.909226  0.000000  0.000000  7.271861   \n",
       "HSPC_001       0.0  7.224846  9.392715  0.000000  0.000000  9.060862   \n",
       "\n",
       "              Ptp4a3      Cdt1     Fxyd1     Arl11    Shisa8  Dleu7   Tmem173  \\\n",
       "HSPC_025    6.461289  7.285499  0.000000  7.593666  6.657127    0.0  2.275971   \n",
       "HSPC_031    1.782055  1.782055  0.000000  0.686872  1.150286    0.0  2.219938   \n",
       "HSPC_037    8.592276  1.218731  0.000000  0.000000  1.218731    0.0  7.615671   \n",
       "LT-HSC_001  2.364517  3.217169  5.982925  0.000000  8.548753    0.0  4.137438   \n",
       "HSPC_001    1.628905  0.923649  0.000000  0.000000  4.434885    0.0  8.771148   \n",
       "\n",
       "              Atxn10       Rpn2      Ccl3    Nudt16     Kif3c      Hars  \\\n",
       "HSPC_025    8.655561   9.077708  0.000000  1.832751  1.189716  7.695253   \n",
       "HSPC_031    2.219938   9.404856  0.686872  1.150286  0.686872  2.017546   \n",
       "HSPC_037    1.218731   7.514567  8.833854  2.316800  1.218731  2.657566   \n",
       "LT-HSC_001  2.364517   3.749470  0.000000  2.364517  0.000000  3.749470   \n",
       "HSPC_001    1.628905  10.008081  8.286449  8.276747  8.061140  1.762030   \n",
       "\n",
       "                 Ccl4     Myo1g       Cpq  Cox6a2  Sdc3      Dok2     Fcgrt  \\\n",
       "HSPC_025    10.437884  1.832751  7.814991     0.0   0.0  1.189716  5.935712   \n",
       "HSPC_031     0.000000  0.686872  0.000000     0.0   0.0  7.824144  7.148337   \n",
       "HSPC_037     4.475001  6.943951  3.694521     0.0   0.0  1.218731  0.000000   \n",
       "LT-HSC_001   7.645229  2.364517  3.749470     0.0   0.0  3.217169  7.917005   \n",
       "HSPC_001     0.000000  8.663575  4.586200     0.0   0.0  8.923709  0.000000   \n",
       "\n",
       "               Elmo1      Cyc1      Calr  Gpr18    Tbxa2r    Gpr183  Naaa  \\\n",
       "HSPC_025    4.588530  2.275971  9.025466    0.0  1.189716  0.000000   0.0   \n",
       "HSPC_031    2.827390  2.397399  9.564413    0.0  6.623022  0.000000   0.0   \n",
       "HSPC_037    8.664848  6.490844  7.482801    0.0  5.647156  1.218731   0.0   \n",
       "LT-HSC_001  7.989468  7.674830  8.397178    0.0  2.364517  0.000000   0.0   \n",
       "HSPC_001    7.925790  1.482235  9.740544    0.0  8.240127  0.000000   0.0   \n",
       "\n",
       "            Gm11110      Alad       Taz    Echdc3    Cd300a  Slc39a4  Zfp128  \\\n",
       "HSPC_025        0.0  3.118771  1.189716  1.189716  7.161885      0.0     0.0   \n",
       "HSPC_031        0.0  2.397399  7.032157  0.000000  0.000000      0.0     0.0   \n",
       "HSPC_037        0.0  2.316800  1.218731  0.000000  0.000000      0.0     0.0   \n",
       "LT-HSC_001      0.0  8.207678  7.866592  0.000000  0.000000      0.0     0.0   \n",
       "HSPC_001        0.0  1.883902  8.577000  0.000000  0.000000      0.0     0.0   \n",
       "\n",
       "            Gm26819      Ispd      Itm2b  Zfp647  Gm16861  Nek3     Gata2  \\\n",
       "HSPC_025        0.0  0.000000  10.103505     0.0      0.0   0.0  2.888576   \n",
       "HSPC_031        0.0  0.000000  11.892010     0.0      0.0   0.0  1.782055   \n",
       "HSPC_037        0.0  0.000000  10.392692     0.0      0.0   0.0  7.160213   \n",
       "LT-HSC_001      0.0  0.000000  10.297362     0.0      0.0   0.0  7.584142   \n",
       "HSPC_001        0.0  7.520972   9.818564     0.0      0.0   0.0  0.000000   \n",
       "\n",
       "              Prss16    Lppos     Lhcgr    Fundc1       Lat  Tnfrsf26  \\\n",
       "HSPC_025    0.000000  0.00000  0.000000  7.977803  6.675324  0.000000   \n",
       "HSPC_031    0.000000  0.00000  1.150286  0.000000  7.828021  1.150286   \n",
       "HSPC_037    0.000000  0.00000  0.000000  2.316800  1.218731  8.355550   \n",
       "LT-HSC_001  0.000000  5.77998  0.000000  8.267186  4.137438  2.364517   \n",
       "HSPC_001    0.377367  0.00000  0.000000  0.377367  9.422563  0.377367   \n",
       "\n",
       "               Prr36     Vldlr     Mfge8     Enkur  Rcor2       Ndn      Btla  \\\n",
       "HSPC_025    0.000000  7.054751  0.000000  0.000000    0.0  0.000000  0.000000   \n",
       "HSPC_031    5.925034  0.000000  0.686872  0.000000    0.0  0.000000  0.686872   \n",
       "HSPC_037    0.000000  2.933024  0.000000  1.218731    0.0  0.000000  0.000000   \n",
       "LT-HSC_001  0.000000  2.364517  2.364517  0.000000    0.0  0.000000  0.000000   \n",
       "HSPC_001    0.000000  8.668884  0.377367  0.000000    0.0  5.918739  0.000000   \n",
       "\n",
       "                 Zyx      Cd33      Dkc1     Rab37     Cdcp1     Hoxa9  \\\n",
       "HSPC_025    9.101455  7.040782  9.449536  0.000000  0.000000  8.310278   \n",
       "HSPC_031    8.915083  0.000000  9.006865  8.155637  0.000000  9.640235   \n",
       "HSPC_037    8.669560  9.162687  9.873687  7.755167  1.218731  7.615671   \n",
       "LT-HSC_001  7.645229  3.217169  8.058464  7.107933  0.000000  7.232592   \n",
       "HSPC_001    4.265819  0.000000  9.042166  0.923649  0.000000  3.519027   \n",
       "\n",
       "                 Oat  Vegfc      Gas6     Oasl2     Tacc1  Colgalt1  Xdh  \\\n",
       "HSPC_025    8.495788    0.0  0.000000  2.888576  8.687292  7.940672  0.0   \n",
       "HSPC_031    2.397399    0.0  0.000000  2.017546  9.543345  0.686872  0.0   \n",
       "HSPC_037    1.218731    0.0  0.000000  8.626598  9.247050  2.316800  0.0   \n",
       "LT-HSC_001  7.760152    0.0  8.267186  4.137438  9.967373  0.000000  0.0   \n",
       "HSPC_001    8.720921    0.0  0.000000  8.950983  8.410789  0.676211  0.0   \n",
       "\n",
       "               Rab32      Dusp2  Tmem176a     Gnai3     Rnf13     Elane  \\\n",
       "HSPC_025    1.189716  11.307754  7.781782  7.894815  8.361752  0.000000   \n",
       "HSPC_031    0.000000   9.419062  9.750561  1.150286  0.686872  0.686872   \n",
       "HSPC_037    7.371559   6.779369  7.133183  1.869807  8.246273  0.000000   \n",
       "LT-HSC_001  8.187283   6.971475  8.926375  9.143059  7.063866  0.000000   \n",
       "HSPC_001    0.000000   0.676211  8.584884  6.136699  7.233470  1.628905   \n",
       "\n",
       "                Cd48    Mthfd1       Igkc      Fnta  Bok   Ripply3    Ptger2  \\\n",
       "HSPC_025    2.888576  9.067410   4.509590  8.310278  0.0  0.000000  0.000000   \n",
       "HSPC_031    6.549791  8.233698   3.653748  1.500480  0.0  0.000000  0.000000   \n",
       "HSPC_037    6.183499  7.348242   3.963623  8.808450  0.0  1.218731  1.218731   \n",
       "LT-HSC_001  3.749470  3.217169  12.461628  5.543737  0.0  0.000000  0.000000   \n",
       "HSPC_001    0.377367  1.996276   2.288830  9.442536  0.0  0.000000  0.000000   \n",
       "\n",
       "               P2rx7     Wdfy2     Eif1a     Gstm7   Tmem14c  2900018N21Rik  \\\n",
       "HSPC_025    0.000000  0.000000  7.343549  0.000000  5.671352       7.564672   \n",
       "HSPC_031    0.000000  0.000000  8.662811  0.000000  3.253543       0.000000   \n",
       "HSPC_037    1.218731  1.218731  7.914462  4.190334  7.133183       0.000000   \n",
       "LT-HSC_001  0.000000  0.000000  9.110936  0.000000  3.217169       0.000000   \n",
       "HSPC_001    0.000000  0.000000  6.111968  0.000000  2.604657       0.000000   \n",
       "\n",
       "                  Mpl     Sash3      Tgm2   Gm19569      Ehd3      Cish  \\\n",
       "HSPC_025    10.551846  6.763021  0.000000  1.189716  0.000000  0.000000   \n",
       "HSPC_031     2.219938  9.646834  0.000000  0.000000  0.000000  0.000000   \n",
       "HSPC_037     1.869807  6.813823  0.000000  0.000000  0.000000  1.218731   \n",
       "LT-HSC_001  10.136473  8.483344  0.000000  0.000000  2.364517  9.033078   \n",
       "HSPC_001    10.683373  8.965724  3.853357  0.000000  0.377367  0.000000   \n",
       "\n",
       "              Calml4    Mesdc2     Nfil3      Tcn2     Saraf     Meis1  \\\n",
       "HSPC_025    1.189716  9.272583  0.000000  1.189716  8.021134  8.150349   \n",
       "HSPC_031    0.000000  9.962022  0.686872  8.785511  9.802712  8.826781   \n",
       "HSPC_037    0.000000  9.162687  0.000000  6.689457  9.741228  7.605873   \n",
       "LT-HSC_001  0.000000  8.397178  0.000000  8.305537  8.466517  7.760152   \n",
       "HSPC_001    0.000000  1.482235  0.000000  0.000000  9.960822  7.587515   \n",
       "\n",
       "            Serpina3g       Pcx    Gpr171     Cks1b      Coa5  Arhgap27os3  \\\n",
       "HSPC_025     2.275971  0.000000  7.747791  8.130700  3.647281          0.0   \n",
       "HSPC_031     0.686872  0.000000  8.545278  2.697797  9.449579          0.0   \n",
       "HSPC_037     2.316800  1.218731  8.760682  3.363442  2.933024          0.0   \n",
       "LT-HSC_001   9.522611  0.000000  7.760152  2.364517  2.364517          0.0   \n",
       "HSPC_001     1.318950  0.000000  9.564895  7.530335  1.628905          0.0   \n",
       "\n",
       "               Farsa      Ctsl     Iigp1     P2ry14      Cd82   Slc18a2  \\\n",
       "HSPC_025    7.593666  1.832751  1.189716   7.790156  7.421084  0.000000   \n",
       "HSPC_031    8.201186  0.000000  0.000000   8.900430  2.397399  1.500480   \n",
       "HSPC_037    8.636256  7.160213  1.869807  10.783395  8.057904  0.000000   \n",
       "LT-HSC_001  4.137438  8.058464  4.909226   3.749470  3.749470  0.000000   \n",
       "HSPC_001    8.609387  8.121239  0.676211   8.514683  1.762030  0.377367   \n",
       "\n",
       "               Cd302    Parp12    Isyna1    S100a8  B130034C11Rik  Arhgap27  \\\n",
       "HSPC_025    0.000000  6.779938  2.275971  6.079295       0.000000  2.275971   \n",
       "HSPC_031    0.000000  0.686872  3.582099  0.000000       0.000000  3.506705   \n",
       "HSPC_037    0.000000  6.235258  3.164226  0.000000       0.000000  8.429623   \n",
       "LT-HSC_001  2.364517  8.700371  0.000000  0.000000       2.364517  8.080749   \n",
       "HSPC_001    0.000000  0.000000  1.628905  0.377367       0.000000  0.000000   \n",
       "\n",
       "                Klf1  Gm26740       Cd84      Bag2   Tmem123   Emilin1  \\\n",
       "HSPC_025    2.888576      0.0   9.014787  1.189716  8.182512  7.977803   \n",
       "HSPC_031    2.219938      0.0  10.152610  1.150286  6.348589  0.000000   \n",
       "HSPC_037    3.694520      0.0   1.869807  0.000000  7.710151  1.218731   \n",
       "LT-HSC_001  0.000000      0.0   8.532677  8.580378  7.674830  8.305537   \n",
       "HSPC_001    2.288830      0.0   1.628905  1.134796  1.134796  0.000000   \n",
       "\n",
       "            4930568A12Rik   Clec11a      Mat2a    Tespa1     Wfdc17  \\\n",
       "HSPC_025         4.141925  1.189716   9.826553  0.000000   9.822478   \n",
       "HSPC_031         0.000000  0.000000   7.780798  1.150286   1.150286   \n",
       "HSPC_037         0.000000  0.000000   9.498083  0.000000  10.651504   \n",
       "LT-HSC_001       0.000000  0.000000   4.137438  0.000000   7.645229   \n",
       "HSPC_001         0.000000  0.377367  10.135483  0.676211   1.318950   \n",
       "\n",
       "              Tspan13  Dyrk2    S100a9  F10  Pde1b     Gcnt2      Ppic  \\\n",
       "HSPC_025     8.594715    0.0  6.796659  0.0    0.0  7.273603  0.000000   \n",
       "HSPC_031     8.951078    0.0  0.000000  0.0    0.0  0.000000  0.000000   \n",
       "HSPC_037    10.660998    0.0  0.000000  0.0    0.0  7.482801  0.000000   \n",
       "LT-HSC_001   8.685906    0.0  0.000000  0.0    0.0  8.901552  2.364517   \n",
       "HSPC_001     8.785881    0.0  0.000000  0.0    0.0  7.854899  0.377367   \n",
       "\n",
       "               Zfp72    Gm4841       Ckb  C130013H08Rik     Spo11      Ctss  \\\n",
       "HSPC_025    4.141925  1.189716  6.845691       0.000000  1.189716  6.923879   \n",
       "HSPC_031    0.000000  0.000000  6.293577       8.896743  7.116992  0.686872   \n",
       "HSPC_037    0.000000  0.000000  0.000000       0.000000  0.000000  8.729440   \n",
       "LT-HSC_001  0.000000  2.364517  2.364517       0.000000  8.227787  5.885018   \n",
       "HSPC_001    0.000000  7.663942  0.000000       0.377367  0.000000  5.670382   \n",
       "\n",
       "               Vwa5a  Serpinb1a     Trib3   Slc28a2      Xbp1   Tsc22d3  \\\n",
       "HSPC_025    2.888576   5.776244  0.000000  0.000000  3.118771  8.475161   \n",
       "HSPC_031    8.445345   8.842198  0.000000  0.686872  7.045538  1.782055   \n",
       "HSPC_037    1.218731   8.854687  1.218731  0.000000  5.303604  9.218403   \n",
       "LT-HSC_001  0.000000   3.217169  5.982925  7.645229  5.543737  9.705823   \n",
       "HSPC_001    0.000000   7.499681  0.000000  0.000000  9.520952  9.448104   \n",
       "\n",
       "                 Cd74  Nhlrc1       Rac2      Il15     Ift43  Tnfrsf13c  \\\n",
       "HSPC_025     0.000000     0.0  10.807313  7.122636  0.000000   6.711045   \n",
       "HSPC_031     0.000000     0.0   8.900430  0.686872  1.500480   9.076959   \n",
       "HSPC_037     1.869807     0.0  11.052258  1.218731  0.000000   0.000000   \n",
       "LT-HSC_001  10.273376     0.0   9.438827  0.000000  0.000000   8.863498   \n",
       "HSPC_001     0.000000     0.0   8.703439  1.318950  6.563421   0.377367   \n",
       "\n",
       "            Gm10451     Plod2    Minpp1        Cs  Gm21967    Trim27  \\\n",
       "HSPC_025        0.0  4.242991  7.525084  2.275971      0.0  4.033241   \n",
       "HSPC_031        0.0  0.000000  2.219938  9.871007      0.0  8.938581   \n",
       "HSPC_037        0.0  0.000000  3.164226  6.156907      0.0  7.755167   \n",
       "LT-HSC_001      0.0  0.000000  8.166596  4.694814      0.0  3.217169   \n",
       "HSPC_001        0.0  0.000000  8.443370  1.318950      0.0  9.606986   \n",
       "\n",
       "                Rrm1     Krt18     Rap1b     Anxa2  D130040H23Rik     Tmsb4x  \\\n",
       "HSPC_025    9.167225  0.000000  3.317239  7.747791       0.000000   9.816341   \n",
       "HSPC_031    2.017546  6.977358  2.697797  1.150286       8.195195  10.778556   \n",
       "HSPC_037    2.933024  0.000000  2.933024  3.164226       0.000000   9.700377   \n",
       "LT-HSC_001  3.217169  8.516418  9.464477  2.364517       3.217169  10.330287   \n",
       "HSPC_001    6.723001  0.377367  8.671001  0.377367       0.000000  10.844519   \n",
       "\n",
       "            1810034E14Rik  2810021J22Rik  F630028O10Rik    Cyb5r3  B3gnt3  \\\n",
       "HSPC_025         0.000000       0.000000       0.000000  1.189716     0.0   \n",
       "HSPC_031         0.000000       0.000000       0.000000  2.017546     0.0   \n",
       "HSPC_037         0.000000       0.000000       8.977543  8.577312     0.0   \n",
       "LT-HSC_001       2.364517       2.364517       0.000000  2.364517     0.0   \n",
       "HSPC_001         0.000000       0.000000       0.676211  1.883902     0.0   \n",
       "\n",
       "               Laptm5    Adam11     Rab17     Pkia  Hist3h2ba  Cyp27a1  \\\n",
       "HSPC_025    10.088305  7.955639  0.000000  0.00000   0.000000      0.0   \n",
       "HSPC_031    10.784055  0.000000  0.000000  0.00000   5.004467      0.0   \n",
       "HSPC_037     9.332705  1.869807  0.000000  0.00000   0.000000      0.0   \n",
       "LT-HSC_001  10.442098  0.000000  7.107933  3.74947   5.885018      0.0   \n",
       "HSPC_001    10.464271  3.882894  0.000000  0.00000   0.000000      0.0   \n",
       "\n",
       "                Zfp36      Mcm5     Ssc4d   Gm45837  Lmcd1      Fgf3  \\\n",
       "HSPC_025    11.315027  7.631440  0.000000  0.000000    0.0  1.189716   \n",
       "HSPC_031     5.565109  3.506705  0.000000  0.000000    0.0  0.000000   \n",
       "HSPC_037     3.835337  2.933024  1.218731  1.218731    0.0  1.218731   \n",
       "LT-HSC_001   8.449492  2.364517  0.000000  0.000000    0.0  0.000000   \n",
       "HSPC_001     8.356553  1.482235  0.000000  7.281391    0.0  0.000000   \n",
       "\n",
       "                Car1    Adssl1     Il21r   Slc50a1  Serpinb9   Pttg1ip  \\\n",
       "HSPC_025    6.079295  8.356122  7.554876  9.390178  0.000000  7.887028   \n",
       "HSPC_031    5.706594  0.686872  7.135881  7.615685  0.000000  9.234223   \n",
       "HSPC_037    6.016053  7.348242  7.999236  8.367196  6.285223  8.531469   \n",
       "LT-HSC_001  4.442877  0.000000  7.192224  8.286489  3.217169  3.749470   \n",
       "HSPC_001    4.173225  8.508774  0.923649  7.330454  0.000000  9.775414   \n",
       "\n",
       "              Fam83d    Tspan32  Tnfrsf13b       Gdi2  Nim1k    Mapk12  \\\n",
       "HSPC_025    1.832751   9.681046   9.032541  10.105184    0.0  6.282898   \n",
       "HSPC_031    0.000000   9.659944   1.500480   8.627656    0.0  0.000000   \n",
       "HSPC_037    0.000000  10.283226   2.316800   7.930251    0.0  0.000000   \n",
       "LT-HSC_001  0.000000   9.312700   7.150694   8.595934    0.0  0.000000   \n",
       "HSPC_001    0.377367   8.996450   0.377367   9.137149    0.0  0.000000   \n",
       "\n",
       "              Wfdc18     Thbs1  Chil5  Mpst   Selenop   Gm43852       Hk3  \\\n",
       "HSPC_025    6.502641  3.787723    0.0   0.0  7.730490  0.000000  7.992392   \n",
       "HSPC_031    0.000000  2.017546    0.0   0.0  9.060576  0.000000  8.904107   \n",
       "HSPC_037    9.087235  0.000000    0.0   0.0  9.492777  0.000000  1.869807   \n",
       "LT-HSC_001  2.364517  2.364517    0.0   0.0  8.671295  0.000000  3.217169   \n",
       "HSPC_001    0.000000  0.000000    0.0   0.0  9.482276  7.088883  0.377367   \n",
       "\n",
       "               Irgm1     Nceh1     Myl10      Tmx1  Zcchc24     Acsl5  \\\n",
       "HSPC_025    7.902559  8.274914  7.668250  9.339726      0.0  8.063203   \n",
       "HSPC_031    1.782055  6.942020  8.069839  8.233698      0.0  4.631208   \n",
       "HSPC_037    6.864006  2.657566  1.869807  5.303604      0.0  7.336441   \n",
       "LT-HSC_001  8.058464  3.217169  2.364517  3.749470      0.0  8.166596   \n",
       "HSPC_001    1.318950  8.542711  7.809477  1.482235      0.0  9.920888   \n",
       "\n",
       "             Tnfaip8  4632427E13Rik     Hspa9  Hid1     Gstt1   Il12rb1  Ficd  \\\n",
       "HSPC_025    9.710022       1.189716  4.337438   0.0  7.593666  7.320609   0.0   \n",
       "HSPC_031    9.284465       6.783355  7.214989   0.0  0.000000  0.000000   0.0   \n",
       "HSPC_037    7.450320       1.869807  4.190334   0.0  0.000000  0.000000   0.0   \n",
       "LT-HSC_001  7.018409       7.150694  6.160812   0.0  0.000000  0.000000   0.0   \n",
       "HSPC_001    3.968042       1.318950  9.261827   0.0  0.000000  0.000000   0.0   \n",
       "\n",
       "            Bbs10      Esam   Unc93b1     Casp4       F2r    Dynlt3  \\\n",
       "HSPC_025      0.0  1.832751  7.871328  7.584065  1.189716  1.832751   \n",
       "HSPC_031      0.0  7.377236  0.686872  0.000000  0.686872  0.686872   \n",
       "HSPC_037      0.0  1.218731  9.111674  4.386210  1.869807  8.467986   \n",
       "LT-HSC_001    0.0  2.364517  4.442877  2.364517  0.000000  2.364517   \n",
       "HSPC_001      0.0  9.772458  6.786892  0.676211  0.676211  5.030808   \n",
       "\n",
       "               Adgrg1   Tmem38a     Stap1   Cep170b  Lpxn     Frmd8  Doc2g  \\\n",
       "HSPC_025    10.034692  0.000000  1.832751  3.317239   0.0  5.347433    0.0   \n",
       "HSPC_031    10.663397  0.000000  0.000000  0.000000   0.0  7.255855    0.0   \n",
       "HSPC_037     7.849521  0.000000  9.859326  0.000000   0.0  7.953614    0.0   \n",
       "LT-HSC_001  11.081615  4.137438  3.217169  3.749470   0.0  9.594069    0.0   \n",
       "HSPC_001    11.404691  0.000000  7.758600  7.079347   0.0  7.278616    0.0   \n",
       "\n",
       "            Ighv1-23  Hrh2       Bgn    Ifi206       Fes    Apol7e  Srl  \\\n",
       "HSPC_025         0.0   0.0  0.000000  5.634622  7.554876  6.502641  0.0   \n",
       "HSPC_031         0.0   0.0  0.686872  1.150286  4.021900  0.000000  0.0   \n",
       "HSPC_037         0.0   0.0  1.218731  0.000000  1.218731  0.000000  0.0   \n",
       "LT-HSC_001       0.0   0.0  2.364517  0.000000  2.364517  0.000000  0.0   \n",
       "HSPC_001         0.0   0.0  6.625896  0.000000  0.000000  0.000000  0.0   \n",
       "\n",
       "               Ifi47     Cdca7       Srm  Smagp    Zfp418    Zfp882      Mmp2  \\\n",
       "HSPC_025    7.640730  9.278547  8.971261    0.0  0.000000  7.012433  0.000000   \n",
       "HSPC_031    2.219938  3.849535  2.827391    0.0  0.000000  0.000000  8.467882   \n",
       "HSPC_037    6.813823  7.428252  3.164226    0.0  0.000000  0.000000  2.316800   \n",
       "LT-HSC_001  2.364517  3.217169  0.000000    0.0  0.000000  0.000000  0.000000   \n",
       "HSPC_001    9.288282  8.392936  9.193486    0.0  0.377367  0.000000  0.000000   \n",
       "\n",
       "              Fam32a      Flna       Lyz2       Cd53  Mapk11    Sptbn1  \\\n",
       "HSPC_025    8.035294  1.832751   9.438924   7.879200     0.0  6.159051   \n",
       "HSPC_031    9.385257  8.095797  10.225698   7.393023     0.0  9.179137   \n",
       "HSPC_037    8.907487  3.164226   4.386210  10.124163     0.0  9.413556   \n",
       "LT-HSC_001  8.532677  6.820749   9.979284   8.124310     0.0  6.461820   \n",
       "HSPC_001    0.923649  9.289661  10.363761   7.273049     0.0  4.099659   \n",
       "\n",
       "              Mapre3  Nr0b2     Ptprc     Gulp1      Cd63  Nsg1   Fam131a  \\\n",
       "HSPC_025    0.000000    0.0  8.028232  0.000000  8.156838   0.0  0.000000   \n",
       "HSPC_031    0.000000    0.0  1.782055  5.637585  8.561668   0.0  5.449074   \n",
       "HSPC_037    3.164226    0.0  9.759018  0.000000  1.218731   0.0  0.000000   \n",
       "LT-HSC_001  0.000000    0.0  5.261087  0.000000  8.305537   0.0  0.000000   \n",
       "HSPC_001    1.134796    0.0  2.977514  0.000000  9.935939   0.0  0.000000   \n",
       "\n",
       "                Dntt    Ifi213      Tcp1  Stard8   Clptm1l    Sigirr  \\\n",
       "HSPC_025    2.888576  1.832751  8.400554     0.0  4.426081  1.832751   \n",
       "HSPC_031    9.228369  4.223722  2.946297     0.0  6.380618  9.852094   \n",
       "HSPC_037    3.164226  0.000000  7.275944     0.0  7.238389  2.933024   \n",
       "LT-HSC_001  4.694814  0.000000  9.522611     0.0  8.124310  7.703835   \n",
       "HSPC_001    1.996276  0.000000  4.493504     0.0  1.134796  9.102259   \n",
       "\n",
       "               Robo3  Il17rb     Mcfd2    Vpreb1    Casp12  Zbtb3      Ppt1  \\\n",
       "HSPC_025    0.000000     0.0  9.345421  0.000000  0.000000    0.0  8.400554   \n",
       "HSPC_031    0.000000     0.0  9.311023  0.000000  0.686872    0.0  0.000000   \n",
       "HSPC_037    0.000000     0.0  3.164226  0.000000  1.869807    0.0  4.978682   \n",
       "LT-HSC_001  0.000000     0.0  0.000000  3.217169  3.217169    0.0  9.849024   \n",
       "HSPC_001    3.592379     0.0  0.923649  0.000000  7.607558    0.0  0.377367   \n",
       "\n",
       "                H1f0  C730034F03Rik     Tpst2     Pnrc1    Sdhaf2   Hnrnpdl  \\\n",
       "HSPC_025    3.118771            0.0  8.989556  7.174734  7.704143  7.273603   \n",
       "HSPC_031    7.273021            0.0  1.500480  9.495397  7.209055  3.427154   \n",
       "HSPC_037    9.915931            0.0  7.173540  2.933024  0.000000  9.615036   \n",
       "LT-HSC_001  7.674830            0.0  8.770595  6.652419  3.749470  2.364517   \n",
       "HSPC_001    0.377367            0.0  0.000000  3.995344  7.902545  9.643577   \n",
       "\n",
       "              Cyp7b1      Ptpre  Slc25a45     Wdr35      Capg     Sf3b3  \\\n",
       "HSPC_025    0.000000   8.433001  4.337438  0.000000  8.124090  9.656065   \n",
       "HSPC_031    0.000000  10.735345  6.022986  0.000000  7.565481  2.555402   \n",
       "HSPC_037    0.000000   8.678935  7.874218  3.164226  7.383077  2.316800   \n",
       "LT-HSC_001  0.000000   4.442877  3.217169  0.000000  4.442877  9.455977   \n",
       "HSPC_001    0.377367   0.923649  8.056288  0.000000  1.883902  1.628905   \n",
       "\n",
       "               Gata1  Spry1     Abce1  Rnf180   Golph3l    P2ry10    Adgrg3  \\\n",
       "HSPC_025    0.000000    0.0  7.095865     0.0  8.454235  0.000000  6.132952   \n",
       "HSPC_031    2.397399    0.0  8.570951     0.0  8.009667  8.874422  1.150286   \n",
       "HSPC_037    1.869807    0.0  2.316800     0.0  3.363442  0.000000  9.080177   \n",
       "LT-HSC_001  2.364517    0.0  3.749470     0.0  9.055756  0.000000  2.364517   \n",
       "HSPC_001    1.628905    0.0  8.057907     0.0  9.902980  0.676211  1.482235   \n",
       "\n",
       "               Fkbp4  F2rl2      Gbp2     Plcg2  Ccdc189  4930486L24Rik  \\\n",
       "HSPC_025    7.040782    0.0  0.000000  5.905197      0.0       0.000000   \n",
       "HSPC_031    9.864415    0.0  0.000000  6.984322      0.0       0.000000   \n",
       "HSPC_037    9.040723    0.0  0.000000  8.283626      0.0       0.000000   \n",
       "LT-HSC_001  9.264843    0.0  9.174482  9.530727      0.0       7.917005   \n",
       "HSPC_001    8.315172    0.0  0.000000  8.858300      0.0       0.000000   \n",
       "\n",
       "            E230001N04Rik     Itih5    Chrnb1  Gm16386  Thnsl2   Gm16712  \\\n",
       "HSPC_025         1.189716  7.839405  5.809578      0.0     0.0  7.297297   \n",
       "HSPC_031         0.000000  7.694245  0.000000      0.0     0.0  0.000000   \n",
       "HSPC_037         0.000000  1.218731  0.000000      0.0     0.0  0.000000   \n",
       "LT-HSC_001       0.000000  8.080749  3.217169      0.0     0.0  0.000000   \n",
       "HSPC_001         0.000000  3.083081  0.000000      0.0     0.0  0.000000   \n",
       "\n",
       "               Top2a    Gm5577     Nt5c3  Nlrp10      Ctf1  Il18bp    Il1rl2  \\\n",
       "HSPC_025    3.491677  0.000000  8.490658     0.0  0.000000     0.0  0.000000   \n",
       "HSPC_031    2.397399  0.686872  8.259764     0.0  0.000000     0.0  0.686872   \n",
       "HSPC_037    1.869807  0.000000  8.314031     0.0  0.000000     0.0  4.190334   \n",
       "LT-HSC_001  8.286489  0.000000  3.217169     0.0  0.000000     0.0  0.000000   \n",
       "HSPC_001    2.921687  0.000000  9.107741     0.0  0.377367     0.0  0.000000   \n",
       "\n",
       "              Gm5148       Car2       Vim  A630033H20Rik      Cd81    Zfp563  \\\n",
       "HSPC_025    0.000000  11.130318  8.207737       0.000000  8.993188  0.000000   \n",
       "HSPC_031    0.000000   9.967304  8.682216       0.000000  4.520574  5.723342   \n",
       "HSPC_037    0.000000  10.867803  1.218731       1.218731  8.014129  0.000000   \n",
       "LT-HSC_001  5.779980   8.227787  3.217169       0.000000  9.055756  0.000000   \n",
       "HSPC_001    6.608321  10.655834  1.628905       0.000000  9.932853  0.000000   \n",
       "\n",
       "              Ctla2a    Mfsd2b      Idh2  Tert  Pwwp2b   Atp6ap2     Ces2g  \\\n",
       "HSPC_025    9.521714  2.614548  9.882450   0.0     0.0  9.506549  2.275971   \n",
       "HSPC_031    8.989663  1.782055  2.827391   0.0     0.0  8.030005  3.342958   \n",
       "HSPC_037    9.506006  3.164226  7.472055   0.0     0.0  7.119476  3.164226   \n",
       "LT-HSC_001  8.414827  0.000000  7.453613   0.0     0.0  3.749470  2.364517   \n",
       "HSPC_001    8.069193  0.676211  8.487300   0.0     0.0  8.833932  1.628905   \n",
       "\n",
       "                Gba2  4931428F04Rik      Apoe      Sdha      Dlg3  Igkv4-50  \\\n",
       "HSPC_025    3.915699            0.0  6.923879  9.690023  1.189716       0.0   \n",
       "HSPC_031    0.000000            0.0  8.137007  2.017546  0.686872       0.0   \n",
       "HSPC_037    1.869807            0.0  2.316800  7.428252  0.000000       0.0   \n",
       "LT-HSC_001  0.000000            0.0  7.965715  6.971475  0.000000       0.0   \n",
       "HSPC_001    5.509264            0.0  2.604657  1.883902  8.511140       0.0   \n",
       "\n",
       "               Dhx58  Zfp661      Ccr9      Rgs2      Ctsc     Parp8  \\\n",
       "HSPC_025    0.000000     0.0  0.000000  7.285499  7.631440  3.118771   \n",
       "HSPC_031    0.686872     0.0  0.000000  8.477787  8.566318  6.502060   \n",
       "HSPC_037    4.558643     0.0  1.218731  9.457801  2.316800  2.316800   \n",
       "LT-HSC_001  0.000000     0.0  2.364517  9.078081  7.271861  2.364517   \n",
       "HSPC_001    7.281391     0.0  0.000000  0.377367  9.534391  0.676211   \n",
       "\n",
       "               Tspo2  5430420F09Rik     Pear1       Ltb  Gm26512  AA467197  \\\n",
       "HSPC_025    2.275971            0.0  7.161885  8.926381      0.0  1.189716   \n",
       "HSPC_031    1.500480            0.0  0.000000  1.500480      0.0  0.000000   \n",
       "HSPC_037    2.316800            0.0  1.218731  2.933024      0.0  0.000000   \n",
       "LT-HSC_001  0.000000            0.0  8.837559  9.021605      0.0  0.000000   \n",
       "HSPC_001    1.318950            0.0  0.676211  7.709989      0.0  0.000000   \n",
       "\n",
       "              Unc5cl      Mtpn       Mcm6    Atp2a3      Tyms     Il12a  \\\n",
       "HSPC_025    0.000000  9.342576  10.287772  5.742120  2.275971  8.350471   \n",
       "HSPC_031    0.686872  2.397399   3.253543  8.393966  2.017546  8.251127   \n",
       "HSPC_037    0.000000  9.855197   7.968982  1.869807  2.933024  9.090753   \n",
       "LT-HSC_001  0.000000  7.941566   8.548753  8.286489  0.000000  0.000000   \n",
       "HSPC_001    6.074058  0.923649   6.970060  0.676211  1.628905  1.134796   \n",
       "\n",
       "               Celf2       Axl      Tpm4  Gm3739    Cyp2j9     Fxyd5  \\\n",
       "HSPC_025    8.744431  7.847452  2.614548     0.0  1.189716  9.812235   \n",
       "HSPC_031    7.719509  0.000000  0.686872     0.0  1.500480  9.514768   \n",
       "HSPC_037    8.577312  1.218731  8.148427     0.0  8.320036  9.018738   \n",
       "LT-HSC_001  9.078081  0.000000  4.137438     0.0  7.347332  9.078081   \n",
       "HSPC_001    3.519027  0.000000  0.676211     0.0  8.524086  8.845230   \n",
       "\n",
       "            B230216N24Rik     Trbc2   Fam161b   Gm30948     Apbb1    Igfbp4  \\\n",
       "HSPC_025         3.915699  0.000000  1.189716  1.189716  0.000000  6.796659   \n",
       "HSPC_031         0.000000  0.000000  0.686872  8.533455  0.686872  7.664200   \n",
       "HSPC_037         0.000000  0.000000  7.535363  0.000000  0.000000  7.849521   \n",
       "LT-HSC_001       0.000000  2.364517  0.000000  2.364517  0.000000  3.217169   \n",
       "HSPC_001         0.377367  0.000000  0.000000  0.000000  0.000000  4.394439   \n",
       "\n",
       "              Pcp4l1    Rassf5    Cyp2r1  Smoc1       Srgn      Pygm  Gm43200  \\\n",
       "HSPC_025    7.695253  7.659135  0.000000    0.0   9.716627  7.320609      0.0   \n",
       "HSPC_031    8.304972  1.150286  0.000000    0.0  10.264835  0.000000      0.0   \n",
       "HSPC_037    2.657566  5.150271  5.303604    0.0  11.012021  0.000000      0.0   \n",
       "LT-HSC_001  3.217169  4.442877  2.364517    0.0  10.437787  8.483344      0.0   \n",
       "HSPC_001    0.000000  9.883934  0.000000    0.0  10.435905  0.000000      0.0   \n",
       "\n",
       "            Gca   Arhgef6       Fah  BC017643       Ak3     Icam1   Gm10505  \\\n",
       "HSPC_025    0.0  9.428233  0.000000  1.189716  6.983514  1.189716  0.000000   \n",
       "HSPC_031    0.0  9.858742  9.715412  8.730652  1.500480  6.462704  0.686872   \n",
       "HSPC_037    0.0  2.933024  0.000000  0.000000  9.275141  1.869807  0.000000   \n",
       "LT-HSC_001  0.0  4.909226  0.000000  3.749470  8.449492  0.000000  0.000000   \n",
       "HSPC_001    0.0  9.766528  0.377367  9.098330  6.086805  0.000000  0.000000   \n",
       "\n",
       "               C1qbp     Efna1   AW112010  Tnks1bp1     Anxa1     Fgf11  \\\n",
       "HSPC_025    8.536176  0.000000   8.156838       0.0  1.832751  0.000000   \n",
       "HSPC_031    4.595264  0.000000  10.762946       0.0  0.686872  0.000000   \n",
       "HSPC_037    7.644670  0.000000   8.289759       0.0  7.146761  0.000000   \n",
       "LT-HSC_001  8.124310  2.364517   4.694814       0.0  3.217169  6.766791   \n",
       "HSPC_001    8.816817  0.000000   7.525660       0.0  8.147335  0.377367   \n",
       "\n",
       "                Layn  Gm19331  Rbpms2    Tspan3  Ighv9-1   Gm43201     Arrdc1  \\\n",
       "HSPC_025    0.000000      0.0     0.0  7.535083      0.0  0.000000   7.515015   \n",
       "HSPC_031    0.000000      0.0     0.0  8.989663      0.0  0.686872   0.686872   \n",
       "HSPC_037    3.164226      0.0     0.0  8.331971      0.0  0.000000  10.386982   \n",
       "LT-HSC_001  7.941566      0.0     0.0  5.666688      0.0  0.000000   8.286489   \n",
       "HSPC_001    3.401495      0.0     0.0  6.237319      0.0  0.000000   1.134796   \n",
       "\n",
       "                Hjurp  4921507P07Rik       Sla    Ms4a6b  Zfp551  \\\n",
       "HSPC_025     8.274914        0.00000  4.242991  6.711045     0.0   \n",
       "HSPC_031     7.892380        0.00000  1.782055  2.697797     0.0   \n",
       "HSPC_037    10.113830        6.55335  2.657566  1.218731     0.0   \n",
       "LT-HSC_001   8.863498        0.00000  3.217169  2.364517     0.0   \n",
       "HSPC_001     1.482235        0.00000  8.566798  0.000000     0.0   \n",
       "\n",
       "            2810468N07Rik  Tnf     Serp1  Mfap2  Arhgef18  Slc25a29     Hmox1  \\\n",
       "HSPC_025              0.0  0.0  7.095865    0.0  0.000000       0.0  0.000000   \n",
       "HSPC_031              0.0  0.0  2.219938    0.0  2.697797       0.0  0.686872   \n",
       "HSPC_037              0.0  0.0  7.077557    0.0  4.916631       0.0  0.000000   \n",
       "LT-HSC_001            0.0  0.0  8.305537    0.0  3.749470       0.0  0.000000   \n",
       "HSPC_001              0.0  0.0  1.134796    0.0  9.515068       0.0  0.000000   \n",
       "\n",
       "                  Pkm  Arhgap15     Ddx39  Fv1  Gnb5       Tes    Lysmd2  \\\n",
       "HSPC_025     9.000423  7.712979  5.045572  0.0   0.0  7.871328  1.189716   \n",
       "HSPC_031     8.785511  6.830496  7.317821  0.0   0.0  7.659856  0.000000   \n",
       "HSPC_037    10.516827  8.050700  8.395907  0.0   0.0  6.707893  1.218731   \n",
       "LT-HSC_001  10.229170  4.137438  3.749470  0.0   0.0  8.187283  0.000000   \n",
       "HSPC_001     9.761568  8.943118  1.628905  0.0   0.0  9.188320  7.544266   \n",
       "\n",
       "            Mmp11    Pgrmc1  Gm42979     Efna4      Uba7       Xist     Sumo3  \\\n",
       "HSPC_025      0.0  8.774285      0.0  0.000000  8.256901   9.091325  9.254540   \n",
       "HSPC_031      0.0  2.219938      0.0  0.000000  2.219938   9.460858  3.342958   \n",
       "HSPC_037      0.0  8.107351      0.0  7.700976  8.515859  10.286297  8.546913   \n",
       "LT-HSC_001    0.0  7.674830      0.0  0.000000  9.055756   9.377142  7.787507   \n",
       "HSPC_001      0.0  0.676211      0.0  5.974679  8.515861   9.857676  9.503825   \n",
       "\n",
       "                 Flt3      Mycn  1300017J02Rik     Scn1b      Rab38  \\\n",
       "HSPC_025     7.054751  2.888576       1.832751  0.000000   7.399354   \n",
       "HSPC_031     8.124452  8.716082       0.000000  7.672848   1.500480   \n",
       "HSPC_037    10.304583  2.657566       0.000000  0.000000   2.933024   \n",
       "LT-HSC_001   3.217169  3.217169       0.000000  2.364517  10.104202   \n",
       "HSPC_001     3.882894  8.969171       0.000000  0.000000   1.134796   \n",
       "\n",
       "            ENSMUSG00000096970   Tnfaip2     Hdac2  Ccr7  Irf6     Mndal  \\\n",
       "HSPC_025                   0.0  0.000000  9.681046   0.0   0.0  8.570612   \n",
       "HSPC_031                   0.0  2.017546  8.830651   0.0   0.0  0.686872   \n",
       "HSPC_037                   0.0  2.933024  2.316800   0.0   0.0  8.760682   \n",
       "LT-HSC_001                 0.0  0.000000  9.078081   0.0   0.0  9.153609   \n",
       "HSPC_001                   0.0  0.676211  0.676211   0.0   0.0  5.082642   \n",
       "\n",
       "                Fut4     Casp1   Slc14a1      Rit1     Ifi203  Zfp93  \\\n",
       "HSPC_025    0.000000  6.418716  1.832751  1.189716   1.832751    0.0   \n",
       "HSPC_031    0.000000  8.568637  6.101957  0.000000  10.293858    0.0   \n",
       "HSPC_037    1.218731  7.906503  3.164226  1.869807   8.833854    0.0   \n",
       "LT-HSC_001  0.000000  3.749470  0.000000  0.000000   9.705823    0.0   \n",
       "HSPC_001    7.000696  0.000000  0.923649  0.676211   1.482235    0.0   \n",
       "\n",
       "               Calr3  2210010C04Rik   Gm38243   Gm43313   Slc25a5      Bcam  \\\n",
       "HSPC_025    0.000000            0.0  0.000000  0.000000  9.903830  1.189716   \n",
       "HSPC_031    0.686872            0.0  8.186162  0.000000  3.427154  0.000000   \n",
       "HSPC_037    0.000000            0.0  1.869807  1.218731  8.484121  0.000000   \n",
       "LT-HSC_001  0.000000            0.0  0.000000  0.000000  9.489678  0.000000   \n",
       "HSPC_001    0.000000            0.0  0.377367  0.000000  9.410571  0.000000   \n",
       "\n",
       "            Ighv1-74  Traf3ip3    Lgals9   Gm26982   Clec10a     Klhl8  Islr  \\\n",
       "HSPC_025    1.189716  1.189716  8.459496  0.000000  0.000000  1.832751   0.0   \n",
       "HSPC_031    0.000000  6.076110  4.075063  0.686872  0.686872  0.000000   0.0   \n",
       "HSPC_037    0.000000  9.076634  8.372984  0.000000  6.285223  0.000000   0.0   \n",
       "LT-HSC_001  0.000000  3.217169  9.066961  0.000000  0.000000  0.000000   0.0   \n",
       "HSPC_001    0.000000  8.086751  2.531943  0.000000  0.000000  0.000000   0.0   \n",
       "\n",
       "            Ighv1-76     Crlf3  Ighv1-77      Lcp2  Rapsn  Cd38   Gm37423  \\\n",
       "HSPC_025         0.0  1.832751  1.189716  5.935712    0.0   0.0  1.189716   \n",
       "HSPC_031         0.0  7.065378  0.000000  5.387358    0.0   0.0  0.000000   \n",
       "HSPC_037         0.0  8.484121  0.000000  1.218731    0.0   0.0  0.000000   \n",
       "LT-HSC_001       0.0  2.364517  0.000000  8.466517    0.0   0.0  0.000000   \n",
       "HSPC_001         0.0  8.571341  0.000000  0.377367    0.0   0.0  0.000000   \n",
       "\n",
       "              Selplg     Clip3  Ccdc157  Uggt2       Mpo      Eya1      Ctso  \\\n",
       "HSPC_025    2.275971  1.189716  0.00000    0.0  2.275971  0.000000  1.189716   \n",
       "HSPC_031    2.017546  0.000000  0.00000    0.0  2.697797  7.877493  5.672502   \n",
       "HSPC_037    3.363442  0.000000  0.00000    0.0  2.657566  0.000000  8.765091   \n",
       "LT-HSC_001  9.164084  0.000000  0.00000    0.0  3.217169  0.000000  4.694814   \n",
       "HSPC_001    0.676211  0.000000  6.38397    0.0  3.031264  5.004176  6.759286   \n",
       "\n",
       "              Gm26917      Rarb     Cmtm6  Metrnl       Rgs1     Ptpn6  \\\n",
       "HSPC_025     9.707812  0.000000  0.000000     0.0   8.786894  8.978607   \n",
       "HSPC_031    11.297833  0.686872  2.697797     0.0   1.500480  7.999391   \n",
       "HSPC_037    10.404042  0.000000  7.566005     0.0   3.694520  8.923351   \n",
       "LT-HSC_001   8.756821  0.000000  0.000000     0.0   3.749470  9.121723   \n",
       "HSPC_001    10.834868  0.000000  7.183903     0.0  10.077985  8.673117   \n",
       "\n",
       "               Cd1d1  Lyrm1    Prkaa2    Gm4759     Aplp2    Gm8995     Gng11  \\\n",
       "HSPC_025    8.691769    0.0  0.000000  9.785260  1.832751  8.967574  0.000000   \n",
       "HSPC_031    1.500480    0.0  0.686872  5.967830  2.827391  8.502254  0.686872   \n",
       "HSPC_037    7.545649    0.0  0.000000  5.986154  8.467986  4.190334  0.000000   \n",
       "LT-HSC_001  3.749470    0.0  0.000000  9.184806  8.595934  6.591640  7.271861   \n",
       "HSPC_001    8.596073    0.0  0.377367  0.676211  1.628905  1.628905  8.208397   \n",
       "\n",
       "            Tmem150b  Abhd6    Gm5111  Ankrd50    Neurl3   Slc44a2     Gm128  \\\n",
       "HSPC_025         0.0    0.0  6.983514   0.0000  6.779938  3.491677  0.000000   \n",
       "HSPC_031         0.0    0.0  8.161794   4.0219  8.753254  8.177073  0.686872   \n",
       "HSPC_037         0.0    0.0  6.553350   0.0000  8.301946  7.635068  7.394504   \n",
       "LT-HSC_001       0.0    0.0  0.000000   0.0000  6.591640  8.449492  0.000000   \n",
       "HSPC_001         0.0    0.0  0.000000   0.0000  0.923649  0.676211  0.000000   \n",
       "\n",
       "             Fam132a  2810414N06Rik      M6pr  Ankrd33b      Hexa  Pdcd1lg2  \\\n",
       "HSPC_025    2.614548            0.0  8.782703  0.000000  9.004027  0.000000   \n",
       "HSPC_031    3.722005            0.0  5.865923  8.880034  2.219938  0.000000   \n",
       "HSPC_037    7.348242            0.0  9.388059  0.000000  8.361384  1.218731   \n",
       "LT-HSC_001  3.217169            0.0  4.137438  0.000000  8.548753  0.000000   \n",
       "HSPC_001    4.787026            0.0  8.127421  0.377367  9.011572  7.007417   \n",
       "\n",
       "              Dnajc9       Lck     Ccnb2       Wls   Gramd1a   Gm37642  \\\n",
       "HSPC_025    4.802330  4.734524  3.491677  1.189716  7.320609  0.000000   \n",
       "HSPC_031    2.397399  0.000000  1.500480  7.005018  9.698571  0.686872   \n",
       "HSPC_037    7.682451  8.641061  1.869807  8.636256  9.756806  6.309572   \n",
       "LT-HSC_001  3.217169  0.000000  0.000000  2.364517  9.789383  0.000000   \n",
       "HSPC_001    1.482235  0.676211  0.377367  0.000000  0.377367  0.377367   \n",
       "\n",
       "             St8sia4  Igkv12-44    Sqstm1  Slc25a35     Tipin  4930555A03Rik  \\\n",
       "HSPC_025    9.299230        0.0  9.501458  1.189716  8.555954       0.000000   \n",
       "HSPC_031    1.150286        0.0  9.231299  0.000000  5.449074       6.492322   \n",
       "HSPC_037    9.612592        0.0  9.883858  1.218731  7.615671       2.657566   \n",
       "LT-HSC_001  4.137438        0.0  6.591640  8.641622  4.137438       0.000000   \n",
       "HSPC_001    0.676211        0.0  1.482235  0.377367  8.144289       6.763263   \n",
       "\n",
       "            Rn18s-rs5    Samd12  B9d1      Ncf4        Myc  Uaca       Irf9  \\\n",
       "HSPC_025    15.299863  0.000000   0.0  1.832751   9.365173   0.0   6.106372   \n",
       "HSPC_031    14.849717  0.686872   0.0  0.000000   2.697797   0.0   1.500480   \n",
       "HSPC_037    15.812094  0.000000   0.0  6.074055  10.234780   0.0   7.359948   \n",
       "LT-HSC_001  15.593200  0.000000   0.0  7.063866   9.421470   0.0  10.020216   \n",
       "HSPC_001    14.466537  0.000000   0.0  0.377367   6.021909   0.0   0.676211   \n",
       "\n",
       "               Paics     Gstm1     Padi4     Tor3a     Rgs18  Slc26a6  \\\n",
       "HSPC_025    9.656065  8.176136  6.258964  2.275971  9.118180      0.0   \n",
       "HSPC_031    3.722005  8.149453  8.417314  0.686872  8.817061      0.0   \n",
       "HSPC_037    8.271282  7.312546  1.869807  0.000000  9.998750      0.0   \n",
       "LT-HSC_001  5.261087  7.674830  0.000000  0.000000  7.760152      0.0   \n",
       "HSPC_001    8.618195  9.168968  8.661447  0.000000  8.059525      0.0   \n",
       "\n",
       "               Ppm1g    Zfand5     Smpd5  Hdac9      Gnl3  Ecm1    Rnase6  \\\n",
       "HSPC_025    3.491677  9.901899  0.000000    0.0  9.362368   0.0  1.189716   \n",
       "HSPC_031    7.209055  8.634314  0.686872    0.0  5.925034   0.0  0.686872   \n",
       "HSPC_037    7.857800  9.885883  0.000000    0.0  8.808450   0.0  2.316800   \n",
       "LT-HSC_001  3.217169  7.584142  0.000000    0.0  5.885018   0.0  0.000000   \n",
       "HSPC_001    1.996276  1.996276  0.000000    0.0  8.620389   0.0  0.000000   \n",
       "\n",
       "            Lgals3bp   Slc2a10     Rdh14  Icos     Dnah1  Amt      Fut8  \\\n",
       "HSPC_025    7.764886  0.000000  1.189716   0.0  0.000000  0.0  5.392129   \n",
       "HSPC_031    8.972252  0.000000  7.839589   0.0  0.000000  0.0  8.354193   \n",
       "HSPC_037    7.898499  0.000000  0.000000   0.0  0.000000  0.0  1.869807   \n",
       "LT-HSC_001  8.548753  2.364517  2.364517   0.0  7.107933  0.0  7.018409   \n",
       "HSPC_001    9.508570  0.000000  1.482235   0.0  0.000000  0.0  1.134796   \n",
       "\n",
       "               Itpkc     Ap1s2  Rgs11    Hpcal1   Gm43178  Rnase4     Adgrl4  \\\n",
       "HSPC_025    2.275971  0.000000    0.0  0.000000  0.000000     0.0   8.226370   \n",
       "HSPC_031    0.000000  0.000000    0.0  3.158217  0.686872     0.0   9.163881   \n",
       "HSPC_037    1.218731  2.657566    0.0  0.000000  0.000000     0.0  10.679799   \n",
       "LT-HSC_001  0.000000  0.000000    0.0  4.137438  0.000000     0.0   8.035830   \n",
       "HSPC_001    0.000000  0.000000    0.0  7.676647  0.000000     0.0   8.396780   \n",
       "\n",
       "                Nek6      Clk1      Eya2     Csf3r  Fkbp1b      Pirb  \\\n",
       "HSPC_025    0.000000  9.475731  2.275971  1.832751     0.0  1.189716   \n",
       "HSPC_031    5.084431  9.607904  0.686872  8.894896     0.0  0.686872   \n",
       "HSPC_037    0.000000  9.476740  2.316800  4.291594     0.0  0.000000   \n",
       "LT-HSC_001  3.749470  4.694814  6.710738  0.000000     0.0  8.499976   \n",
       "HSPC_001    0.000000  2.100526  9.437569  0.676211     0.0  0.000000   \n",
       "\n",
       "               Msmo1   Gm15133      Pim2  Cldn15  Gm28557     Ric8a  Triqk  \\\n",
       "HSPC_025    1.832751  3.491677  1.189716     0.0      0.0  7.148920    0.0   \n",
       "HSPC_031    0.000000  0.000000  0.686872     0.0      0.0  1.500480    0.0   \n",
       "HSPC_037    1.218731  0.000000  1.218731     0.0      0.0  9.889926    0.0   \n",
       "LT-HSC_001  9.447428  5.666688  0.000000     0.0      0.0  9.594069    0.0   \n",
       "HSPC_001    0.000000  0.000000  8.639983     0.0      0.0  9.758583    0.0   \n",
       "\n",
       "               Spns2  Cebpe   Tspan14     Itm2a      Ddx1  Sh3tc1  Slc22a18  \\\n",
       "HSPC_025    1.832751    0.0  9.018355  0.000000  8.280868     0.0  0.000000   \n",
       "HSPC_031    0.000000    0.0  6.861088  0.000000  9.492957     0.0  8.775508   \n",
       "HSPC_037    1.218731    0.0  8.233604  0.000000  8.384491     0.0  0.000000   \n",
       "LT-HSC_001  8.483344    0.0  9.340672  2.364517  0.000000     0.0  3.217169   \n",
       "HSPC_001    5.600985    0.0  9.989108  0.000000  1.318950     0.0  0.000000   \n",
       "\n",
       "                 Lcp1     Casp2    Epsti1     Ifi44    Phf11b      Ctsb  \\\n",
       "HSPC_025     9.322510  8.536176  0.000000  8.722721  7.756364  9.457444   \n",
       "HSPC_031     9.517171  0.000000  7.928939  2.397399  0.000000  3.056145   \n",
       "HSPC_037    10.199386  7.824394  1.869807  5.038175  0.000000  6.896517   \n",
       "LT-HSC_001  10.219159  0.000000  3.217169  9.078081  0.000000  9.887456   \n",
       "HSPC_001     9.627852  0.676211  7.553480  0.000000  0.000000  7.478070   \n",
       "\n",
       "                H2afy      Ctsg    Gimap9    Hsd3b7   Smpdl3a   Gm43162  \\\n",
       "HSPC_025     9.874597  0.000000  1.189716  1.832751  1.189716  6.763021   \n",
       "HSPC_031    11.210691  0.686872  9.372041  6.511734  9.587467  0.000000   \n",
       "HSPC_037    11.337700  0.000000  1.218731  2.933024  1.218731  0.000000   \n",
       "LT-HSC_001   8.342895  2.364517  8.876294  2.364517  9.368110  0.000000   \n",
       "HSPC_001     9.471989  1.628905  0.377367  8.708603  0.676211  0.000000   \n",
       "\n",
       "              Trim47      Cpa3       Cat     Cers2     Ifit3  Ifit1bl1  \\\n",
       "HSPC_025    0.000000  1.189716  3.317239  8.887885  1.189716  1.189716   \n",
       "HSPC_031    9.053970  1.500480  9.685810  5.184588  0.000000  0.000000   \n",
       "HSPC_037    1.218731  0.000000  8.867044  9.933662  8.367196  5.527704   \n",
       "LT-HSC_001  0.000000  0.000000  5.666688  5.982925  2.364517  5.095857   \n",
       "HSPC_001    0.000000  0.377367  8.396780  8.817774  0.377367  0.000000   \n",
       "\n",
       "            Slc9a3r2      Mcm3    Fscn1  Thbd      Cd93     Sept6      Irf5  \\\n",
       "HSPC_025    1.832751  8.922578  0.00000   0.0  2.275971  2.888576  0.000000   \n",
       "HSPC_031    0.000000  9.000010  0.00000   0.0  4.700515  8.140128  0.000000   \n",
       "HSPC_037    0.000000  7.359948  0.00000   0.0  8.246273  9.231205  1.218731   \n",
       "LT-HSC_001  0.000000  5.261087  0.00000   0.0  4.442877  3.217169  0.000000   \n",
       "HSPC_001    0.000000  1.883902  4.83315   0.0  0.000000  8.618195  0.000000   \n",
       "\n",
       "               Rab19      Ctsz  Rhbdf1      Wfs1     Mef2c     Blvrb  \\\n",
       "HSPC_025    1.189716  9.454813     0.0  6.542841  8.117451  1.832751   \n",
       "HSPC_031    1.500480  8.654102     0.0  0.000000  9.431856  3.909308   \n",
       "HSPC_037    0.000000  8.557117     0.0  0.000000  7.048918  3.963623   \n",
       "LT-HSC_001  0.000000  3.217169     0.0  0.000000  3.217169  3.217169   \n",
       "HSPC_001    0.000000  1.762030     0.0  0.000000  2.977514  3.031264   \n",
       "\n",
       "               Gimap1  Gimap7  Gimap4  4930515G01Rik     Hgfac     Gimap6  \\\n",
       "HSPC_025     8.378509     0.0     0.0            0.0  0.000000   8.627797   \n",
       "HSPC_031     3.506705     0.0     0.0            0.0  5.655149   8.634314   \n",
       "HSPC_037     6.129815     0.0     0.0            0.0  0.000000   9.427529   \n",
       "LT-HSC_001  10.082279     0.0     0.0            0.0  0.000000   7.760152   \n",
       "HSPC_001     8.331331     0.0     0.0            0.0  0.000000  10.336666   \n",
       "\n",
       "             Gm15201    Gimap5  BC035044       Ly6e  Hs3st1     Zfp62  \\\n",
       "HSPC_025    1.189716  1.189716  2.275971  11.533395     0.0  5.842161   \n",
       "HSPC_031    1.500480  6.861088  9.823251  10.901846     0.0  0.000000   \n",
       "HSPC_037    0.000000  7.857800  8.655380  10.146300     0.0  0.000000   \n",
       "LT-HSC_001  0.000000  7.232592  3.749470  11.172608     0.0  0.000000   \n",
       "HSPC_001    0.000000  0.377367  1.134796   9.692875     0.0  5.069857   \n",
       "\n",
       "              Vpreb3  Kcnh2      Hn1l        Hlf  9930012K11Rik  Sgsm1  \\\n",
       "HSPC_025    0.000000    0.0  4.663373   7.814991            0.0    0.0   \n",
       "HSPC_031    0.000000    0.0  1.782055   9.126594            0.0    0.0   \n",
       "HSPC_037    0.000000    0.0  6.235258  10.552968            0.0    0.0   \n",
       "LT-HSC_001  3.217169    0.0  6.591640   7.419049            0.0    0.0   \n",
       "HSPC_001    0.000000    0.0  1.482235   8.778042            0.0    0.0   \n",
       "\n",
       "                Gmpr    Lancl1   Slc16a1     Plcb2      Sdpr      Rhoh  \\\n",
       "HSPC_025    1.189716  1.189716  8.256901  8.333382  1.189716  9.164007   \n",
       "HSPC_031    0.000000  8.728580  2.219938  4.595264  0.000000  9.771850   \n",
       "HSPC_037    1.218731  0.000000  1.218731  8.014129  0.000000  4.783895   \n",
       "LT-HSC_001  8.267186  2.364517  0.000000  8.166596  0.000000  3.217169   \n",
       "HSPC_001    0.000000  0.923649  3.853357  8.359182  0.000000  8.839592   \n",
       "\n",
       "            Gm38250  Gm26532      Dock2    Pdlim2      Clk4     Nubp2  \\\n",
       "HSPC_025        0.0      0.0   7.721761  6.418716  7.879200  7.535083   \n",
       "HSPC_031        0.0      0.0   8.789493  0.000000  1.782055  9.018787   \n",
       "HSPC_037        0.0      0.0   6.943952  6.726097  2.933024  4.291594   \n",
       "LT-HSC_001      0.0      0.0  10.115040  7.732268  4.442877  4.694814   \n",
       "HSPC_001        0.0      0.0   0.923649  0.000000  4.690105  8.598301   \n",
       "\n",
       "              Treml2      Sell      Polm   Pglyrp2  Gm37829   Tsc22d1  \\\n",
       "HSPC_025    6.877476  0.000000  0.000000  6.968835      0.0  9.384659   \n",
       "HSPC_031    3.787179  8.822902  0.000000  5.322884      0.0  2.397399   \n",
       "HSPC_037    0.000000  3.164226  0.000000  8.582317      0.0  9.529518   \n",
       "LT-HSC_001  8.124310  4.137438  0.000000  7.584142      0.0  8.714692   \n",
       "HSPC_001    0.000000  1.318950  0.377367  7.408690      0.0  8.307024   \n",
       "\n",
       "            9130008F23Rik  Gm37558  Gm37706    Ptprcap       Hck  Adamts10  \\\n",
       "HSPC_025         0.000000      0.0      0.0   9.351091  0.000000  3.118771   \n",
       "HSPC_031         1.150286      0.0      0.0  10.055096  0.686872  0.000000   \n",
       "HSPC_037         0.000000      0.0      0.0   9.828064  1.218731  8.214389   \n",
       "LT-HSC_001       0.000000      0.0      0.0   9.912520  0.000000  5.095857   \n",
       "HSPC_001         0.000000      0.0      0.0  11.226616  0.000000  0.377367   \n",
       "\n",
       "                 Ly6a  Gm37598      Anxa5     Parvg  Acer2     Cd79a  \\\n",
       "HSPC_025     9.428233      0.0   7.545014  8.872193    0.0  0.000000   \n",
       "HSPC_031     8.612002      0.0   3.056145  8.751214    0.0  0.000000   \n",
       "HSPC_037     3.363442      0.0   2.316800  9.215184    0.0  0.000000   \n",
       "LT-HSC_001  10.283019      0.0  10.093283  4.442877    0.0  4.909226   \n",
       "HSPC_001     8.840533      0.0   8.171473  8.984579    0.0  0.000000   \n",
       "\n",
       "             Tmem175  Fcrl1  Dusp22    Ms4a4c     Bank1  Gm43149    Gatsl3  \\\n",
       "HSPC_025    6.693295    0.0     0.0  0.000000  1.189716      0.0  0.000000   \n",
       "HSPC_031    9.337102    0.0     0.0  0.000000  0.000000      0.0  0.000000   \n",
       "HSPC_037    1.218731    0.0     0.0  0.000000  0.000000      0.0  0.000000   \n",
       "LT-HSC_001  0.000000    0.0     0.0  2.364517  5.095857      0.0  2.364517   \n",
       "HSPC_001    0.000000    0.0     0.0  0.000000  5.953955      0.0  0.000000   \n",
       "\n",
       "            BC026585     Wfdc2     Camk1  Dgkq    Treml1      Ebi3  Gm27252  \\\n",
       "HSPC_025    2.275971  0.000000  9.124817   0.0  1.832751  6.581951      0.0   \n",
       "HSPC_031    6.807118  0.000000  8.318813   0.0  0.000000  1.150286      0.0   \n",
       "HSPC_037    1.218731  0.000000  1.869807   0.0  0.000000  6.912502      0.0   \n",
       "LT-HSC_001  4.694814  0.000000  3.217169   0.0  0.000000  8.466517      0.0   \n",
       "HSPC_001    0.000000  7.377903  0.923649   0.0  0.000000  7.458585      0.0   \n",
       "\n",
       "              Magee1  Sdc2      Ldhb  Kmo    Bpifb5   Trim30a     Ifitm1  \\\n",
       "HSPC_025    0.000000   0.0  8.464736  0.0  1.832751  8.744431  11.084242   \n",
       "HSPC_031    1.150286   0.0  0.686872  0.0  1.150286  7.925325  11.145000   \n",
       "HSPC_037    0.000000   0.0  2.316800  0.0  1.869807  3.363442   9.865499   \n",
       "LT-HSC_001  0.000000   0.0  8.207678  0.0  0.000000  4.442877  11.771131   \n",
       "HSPC_001    0.000000   0.0  8.116584  0.0  8.717852  9.644115  11.752090   \n",
       "\n",
       "               Ddx58     Ifitm3    Rmnd5b  Gm26772  Gm16151  Gm16150  \\\n",
       "HSPC_025    1.189716  10.748367  7.525084      0.0      0.0      0.0   \n",
       "HSPC_031    0.686872  10.561133  8.130743      0.0      0.0      0.0   \n",
       "HSPC_037    5.859945  10.011792  8.181786      0.0      0.0      0.0   \n",
       "LT-HSC_001  8.012835  10.802043  9.089116      0.0      0.0      0.0   \n",
       "HSPC_001    0.000000  10.211298  9.051950      0.0      0.0      0.0   \n",
       "\n",
       "               Tagln2  3110083C13Rik  Cyp39a1    Tspyl3   Dennd1c    Slc2a1  \\\n",
       "HSPC_025     9.263590            0.0      0.0  1.832751  8.832203  7.012433   \n",
       "HSPC_031     9.722719            0.0      0.0  2.946297  8.765435  0.000000   \n",
       "HSPC_037     9.476740            0.0      0.0  7.225650  7.186745  1.218731   \n",
       "LT-HSC_001  11.133169            0.0      0.0  2.364517  6.461820  3.217169   \n",
       "HSPC_001     9.945156            0.0      0.0  9.002350  9.150871  2.531943   \n",
       "\n",
       "              Large1  AB124611  Gm17096     Stxbp4     Muc3a     Awat2  \\\n",
       "HSPC_025    1.832751       0.0      0.0  10.351401  0.000000  0.000000   \n",
       "HSPC_031    0.000000       0.0      0.0   1.150286  7.267321  0.000000   \n",
       "HSPC_037    1.218731       0.0      0.0   0.000000  0.000000  1.869807   \n",
       "LT-HSC_001  0.000000       0.0      0.0   9.554808  2.364517  0.000000   \n",
       "HSPC_001    0.377367       0.0      0.0   0.377367  0.377367  0.000000   \n",
       "\n",
       "               Plac8  AI506816      Canx  Alox5     Prdx6      F11r      Alg2  \\\n",
       "HSPC_025    8.021134  2.275971  9.658353    0.0  9.000423  1.189716  0.000000   \n",
       "HSPC_031    9.810572  2.219938  9.793820    0.0  9.333015  4.700515  5.184588   \n",
       "HSPC_037    8.462568  3.538458  7.300448    0.0  9.935619  0.000000  1.869807   \n",
       "LT-HSC_001  4.442877  3.217169  5.543737    0.0  5.666688  6.591640  2.364517   \n",
       "HSPC_001    2.921687  7.820968  9.959956    0.0  9.118646  0.377367  8.493298   \n",
       "\n",
       "              Lurap1  Ttc30b    Inpp5k  Dcdc2b  Tnfsf4   Atp6ap1      Dapp1  \\\n",
       "HSPC_025    0.000000     0.0  3.118771     0.0     0.0  8.531189   8.753024   \n",
       "HSPC_031    0.000000     0.0  1.500480     0.0     0.0  1.782055   7.788776   \n",
       "HSPC_037    5.859945     0.0  3.538458     0.0     0.0  8.494778  10.042988   \n",
       "LT-HSC_001  0.000000     0.0  8.656534     0.0     0.0  3.217169   9.554808   \n",
       "HSPC_001    0.000000     0.0  0.923649     0.0     0.0  1.628905   2.197748   \n",
       "\n",
       "            1500002C15Rik    Tuba4a  Gm43254  ENSMUSG00000029333      Gclm  \\\n",
       "HSPC_025              0.0  7.421084      0.0                 0.0  3.118771   \n",
       "HSPC_031              0.0  3.342958      0.0                 0.0  3.253543   \n",
       "HSPC_037              0.0  4.475001      0.0                 0.0  3.363442   \n",
       "LT-HSC_001            0.0  8.532677      0.0                 0.0  2.364517   \n",
       "HSPC_001              0.0  9.216145      0.0                 0.0  3.519027   \n",
       "\n",
       "              Ruvbl2     Apobr  Nap1l3     Cct6a       Vav1      Rfc2  \\\n",
       "HSPC_025    8.367359  5.347433     0.0  9.160781   2.614548  8.327641   \n",
       "HSPC_031    6.807118  0.000000     0.0  7.537344   8.820957  8.288184   \n",
       "HSPC_037    8.355550  0.000000     0.0  3.538458   8.557117  4.978682   \n",
       "LT-HSC_001  6.710738  0.000000     0.0  2.364517   7.840709  3.217169   \n",
       "HSPC_001    1.134796  0.000000     0.0  6.167028  10.550200  7.941667   \n",
       "\n",
       "            Gm16001  Atp1b1  Gm19708     Nradd     Ypel3     Tor4a   Gm19590  \\\n",
       "HSPC_025        0.0     0.0      0.0  4.033241  6.693295  7.187470  9.514152   \n",
       "HSPC_031        0.0     0.0      0.0  0.000000  7.129612  0.686872  6.640763   \n",
       "HSPC_037        0.0     0.0      0.0  0.000000  8.121174  1.218731  8.587306   \n",
       "LT-HSC_001      0.0     0.0      0.0  0.000000  8.611324  5.409323  6.710738   \n",
       "HSPC_001        0.0     0.0      0.0  0.000000  5.537397  0.000000  0.676211   \n",
       "\n",
       "             Ivns1abp  Gm15991  Upp1  Gm6157  Eif5a2   C1qtnf6  4933424M12Rik  \\\n",
       "HSPC_025     8.213975      0.0   0.0     0.0     0.0  6.209872       0.000000   \n",
       "HSPC_031     6.472644      0.0   0.0     0.0     0.0  0.000000       0.000000   \n",
       "HSPC_037     9.111674      0.0   0.0     0.0     0.0  6.553350       0.000000   \n",
       "LT-HSC_001  10.509359      0.0   0.0     0.0     0.0  4.442877       3.217169   \n",
       "HSPC_001     8.250077      0.0   0.0     0.0     0.0  0.377367       0.000000   \n",
       "\n",
       "              Mageh1   Gm11696      Cbfb    Zfp422      Mfng      Klf8  \\\n",
       "HSPC_025    3.317239  0.000000  2.614548  7.122636  9.491221  1.189716   \n",
       "HSPC_031    0.000000  0.000000  3.582099  2.017546  8.043406  0.000000   \n",
       "HSPC_037    0.000000  6.813823  7.914462  8.175175  7.930251  0.000000   \n",
       "LT-HSC_001  0.000000  0.000000  5.261087  0.000000  8.714692  0.000000   \n",
       "HSPC_001    0.000000  0.000000  1.482235  0.377367  9.303379  0.000000   \n",
       "\n",
       "              Stk17b  Met  Ufsp1  Tmem150a  AI606181     Ikzf2    Slc34a2  \\\n",
       "HSPC_025    1.189716  0.0    0.0       0.0  0.000000  8.169733  11.876887   \n",
       "HSPC_031    0.000000  0.0    0.0       0.0  1.150286  7.261599  11.510276   \n",
       "HSPC_037    1.869807  0.0    0.0       0.0  0.000000  8.812716  12.473957   \n",
       "LT-HSC_001  0.000000  0.0    0.0       0.0  0.000000  8.342895  12.910433   \n",
       "HSPC_001    0.377367  0.0    0.0       0.0  0.000000  4.604046  10.497768   \n",
       "\n",
       "            1700066B19Rik      Mcm7  Fbxo48     Ecscr   Lyplal1   Ankrd37  \\\n",
       "HSPC_025         0.000000  8.316089     0.0  0.000000  0.000000  0.000000   \n",
       "HSPC_031         0.686872  2.555402     0.0  0.000000  0.000000  0.000000   \n",
       "HSPC_037         0.000000  5.757471     0.0  0.000000  1.218731  0.000000   \n",
       "LT-HSC_001       0.000000  4.442877     0.0  7.107933  0.000000  2.364517   \n",
       "HSPC_001         0.000000  2.531943     0.0  0.000000  0.000000  0.000000   \n",
       "\n",
       "                Plek  Mapkbp1   Ppp1r3d   Exoc3l2    Tmem53      Wsb1  \\\n",
       "HSPC_025    2.888576      0.0  0.000000  1.189716  6.184685  1.189716   \n",
       "HSPC_031    0.686872      0.0  0.000000  7.655500  0.000000  4.075063   \n",
       "HSPC_037    9.293568      0.0  0.000000  5.351295  0.000000  6.796699   \n",
       "LT-HSC_001  2.364517      0.0  2.364517  0.000000  0.000000  2.364517   \n",
       "HSPC_001    0.000000      0.0  0.000000  0.000000  0.000000  5.867946   \n",
       "\n",
       "                Rtkn      Tfr2    Leprot  Arhgap25  Nupr1     Plpp7  \\\n",
       "HSPC_025    5.558232  2.614548  7.082290  8.083785    0.0  0.000000   \n",
       "HSPC_031    0.000000  9.710171  1.782055  7.877493    0.0  0.000000   \n",
       "HSPC_037    0.000000  2.933024  8.451670  1.869807    0.0  1.218731   \n",
       "LT-HSC_001  0.000000  0.000000  3.217169  8.811145    0.0  0.000000   \n",
       "HSPC_001    0.000000  0.923649  1.762030  4.099659    0.0  0.000000   \n",
       "\n",
       "            G730003C15Rik      Rsrp1     Samsn1     Prdx3   Gm37663     Itgb3  \\\n",
       "HSPC_025              0.0   8.989556   9.202161  3.317239  0.000000  0.000000   \n",
       "HSPC_031              0.0   9.001726   9.143804  6.009394  0.000000  1.150286   \n",
       "HSPC_037              0.0  11.369171  10.099937  7.186745  1.218731  0.000000   \n",
       "LT-HSC_001            0.0  10.120428   6.710738  7.732268  0.000000  0.000000   \n",
       "HSPC_001              0.0  10.043252   1.134796  1.762030  0.000000  0.000000   \n",
       "\n",
       "            Rtn4r      Nrros   Dennd2d        Por  Tspear  Hsd17b12    Capza2  \\\n",
       "HSPC_025      0.0   8.832203  1.189716   8.459496     0.0  7.695253  9.989955   \n",
       "HSPC_031      0.0  10.457334  0.686872   8.482714     0.0  7.220899  5.850759   \n",
       "HSPC_037      0.0   8.283626  0.000000   9.237565     0.0  2.316800  4.081427   \n",
       "LT-HSC_001    0.0   4.694814  0.000000  10.788568     0.0  8.742914  6.971475   \n",
       "HSPC_001      0.0   0.377367  0.000000   7.636025     0.0  1.134796  9.846475   \n",
       "\n",
       "            Osgin1     Spns3       Cd34  4921531C22Rik    Trmt2a   Gm26692  \\\n",
       "HSPC_025       0.0  0.000000  10.686910       0.000000  2.275971  1.189716   \n",
       "HSPC_031       0.0  0.000000   9.467087       7.350532  1.782055  0.000000   \n",
       "HSPC_037       0.0  0.000000  10.726889       7.336441  7.728326  0.000000   \n",
       "LT-HSC_001     0.0  2.364517   4.909226       0.000000  9.768942  0.000000   \n",
       "HSPC_001       0.0  0.676211   9.338449       0.000000  0.377367  0.377367   \n",
       "\n",
       "             Col16a1  Cd46  Ptgds    Uqcrc1  Gm28512     Hacd4  Ttc39c  \\\n",
       "HSPC_025    0.000000   0.0    0.0  7.068586      0.0  7.739166     0.0   \n",
       "HSPC_031    3.158217   0.0    0.0  8.994845      0.0  8.934992     0.0   \n",
       "HSPC_037    0.000000   0.0    0.0  3.835337      0.0  6.880354     0.0   \n",
       "LT-HSC_001  0.000000   0.0    0.0  6.820749      0.0  0.000000     0.0   \n",
       "HSPC_001    0.000000   0.0    0.0  9.600889      0.0  8.113473     0.0   \n",
       "\n",
       "            Igkv1-135   Fam129c      Rhof      Sdcbp     Rcsd1     Mmrn1  \\\n",
       "HSPC_025          0.0  1.189716  0.000000   9.892208  7.970453  1.189716   \n",
       "HSPC_031          0.0  0.000000  0.686872   6.511734  2.219938  0.686872   \n",
       "HSPC_037          0.0  0.000000  0.000000   7.781518  3.164226  1.218731   \n",
       "LT-HSC_001        0.0  0.000000  6.319152   9.340672  8.626553  4.137438   \n",
       "HSPC_001          0.0  0.000000  0.377367  10.449541  2.673882  9.999258   \n",
       "\n",
       "            Ccser1  Ccdc92b  Tnfaip8l1     Tprgl       Gem  D630039A03Rik  \\\n",
       "HSPC_025       0.0      0.0        0.0  7.484379  9.141275            0.0   \n",
       "HSPC_031       0.0      0.0        0.0  0.686872  0.686872            0.0   \n",
       "HSPC_037       0.0      0.0        0.0  4.475001  8.473385            0.0   \n",
       "LT-HSC_001     0.0      0.0        0.0  8.124310  8.247621            0.0   \n",
       "HSPC_001       0.0      0.0        0.0  0.676211  0.000000            0.0   \n",
       "\n",
       "            Lrrc36  Map10   Gm38071     Ccng2       Ldha     Ramp1       Ucp2  \\\n",
       "HSPC_025       0.0    0.0  0.000000  2.275971   8.511066  8.244766  10.309781   \n",
       "HSPC_031       0.0    0.0  0.000000  0.000000  10.249019  9.670777   9.728952   \n",
       "HSPC_037       0.0    0.0  2.933024  7.737329   8.289759  9.379458   9.663070   \n",
       "LT-HSC_001     0.0    0.0  0.000000  0.000000  10.002816  8.926375   5.982925   \n",
       "HSPC_001       0.0    0.0  0.377367  0.000000  10.079579  9.070521  10.222171   \n",
       "\n",
       "                Sdsl      Ttc4   Afap1l1      Ccnd2  Sult1a1    Ube2l6  \\\n",
       "HSPC_025    0.000000  7.068586  5.518464   7.332125      0.0  8.832203   \n",
       "HSPC_031    2.555402  1.500480  9.080214   6.807118      0.0  8.846027   \n",
       "HSPC_037    1.869807  8.424058  7.719267   8.981337      0.0  9.076634   \n",
       "LT-HSC_001  0.000000  3.749470  3.749470   9.669524      0.0  5.982925   \n",
       "HSPC_001    8.300199  0.676211  8.450785  10.698247      0.0  7.622956   \n",
       "\n",
       "             Clec12a   Gm43643  Smtnl1      Aqp1  Large2     Creg1  Fam221a  \\\n",
       "HSPC_025    1.189716  5.965594     0.0  3.118771     0.0  8.384051      0.0   \n",
       "HSPC_031    0.000000  0.000000     0.0  2.397399     0.0  5.278242      0.0   \n",
       "HSPC_037    1.218731  6.333516     0.0  2.657566     0.0  3.694520      0.0   \n",
       "LT-HSC_001  0.000000  0.000000     0.0  0.000000     0.0  8.397178      0.0   \n",
       "HSPC_001    0.923649  0.000000     0.0  0.676211     0.0  8.228669      0.0   \n",
       "\n",
       "               Muc13  Slc25a11  Grap      Ncf1   B4galt4   Gm16758     Nucb1  \\\n",
       "HSPC_025    2.275971  8.238660   0.0  4.588530  1.189716  0.000000  8.411450   \n",
       "HSPC_031    2.397399  7.429204   0.0  7.740229  0.686872  0.000000  2.219938   \n",
       "HSPC_037    4.190334  9.094260   0.0  8.141662  0.000000  0.000000  8.155161   \n",
       "LT-HSC_001  4.137438  7.383635   0.0  0.000000  5.409323  0.000000  9.293747   \n",
       "HSPC_001    8.283684  9.610853   0.0  0.000000  0.000000  2.604657  9.476233   \n",
       "\n",
       "              Fam69b   Tmem35b       Mx1   Ppp1r26       Mx2   Ppp1r15a  \\\n",
       "HSPC_025    6.877476  7.421084  1.832751  6.258964  0.000000   5.842161   \n",
       "HSPC_031    9.839666  8.596177  0.000000  0.000000  0.000000   0.000000   \n",
       "HSPC_037    8.114280  7.790196  4.712649  0.000000  0.000000   1.218731   \n",
       "LT-HSC_001  8.626553  6.160812  0.000000  0.000000  4.694814  10.805393   \n",
       "HSPC_001    6.599451  0.923649  0.000000  0.000000  0.000000   0.000000   \n",
       "\n",
       "            Pdzk1ip1     Coro1a     Mob3c      Oas2   Kctd12b  Mefv   Gm12250  \\\n",
       "HSPC_025    0.000000  11.228325  1.189716  5.253659  1.189716   0.0  6.923879   \n",
       "HSPC_031    0.000000   9.762764  1.150286  0.686872  0.000000   0.0  0.686872   \n",
       "HSPC_037    1.218731   9.991242  1.869807  2.933024  8.378749   0.0  9.149260   \n",
       "LT-HSC_001  6.528191   8.247621  9.205236  0.000000  3.217169   0.0  8.756821   \n",
       "HSPC_001    8.156434   9.612506  0.377367  0.000000  0.000000   0.0  7.873411   \n",
       "\n",
       "               Sh2d5      Igtp     Ermap      Smu1   Maged2      Mcm4  \\\n",
       "HSPC_025    0.000000  1.832751  1.832751  3.118771  0.00000  9.157549   \n",
       "HSPC_031    0.000000  0.000000  1.782055  9.107582  0.00000  2.697797   \n",
       "HSPC_037    6.129815  1.218731  3.363442  8.181786  0.00000  5.150271   \n",
       "LT-HSC_001  0.000000  9.143059  2.364517  7.347332  0.00000  6.319152   \n",
       "HSPC_001    0.000000  0.000000  1.628905  6.414630  5.52808  8.885003   \n",
       "\n",
       "             Gm42576      Anxa6  1700123M08Rik      Rtp4   Sh3bgrl     Napsa  \\\n",
       "HSPC_025    0.000000   0.000000            0.0  8.433001  7.442493  7.261610   \n",
       "HSPC_031    0.686872  10.531721            0.0  1.782055  1.150286  5.255393   \n",
       "HSPC_037    0.000000   1.869807            0.0  8.577312  8.331971  7.439328   \n",
       "LT-HSC_001  0.000000   5.409323            0.0  9.768942  3.217169  3.217169   \n",
       "HSPC_001    0.676211   0.676211            0.0  7.242044  1.318950  2.921687   \n",
       "\n",
       "              Kctd21      Chpf    Gm7160   Neurl1b       Cct3    Nkx2-3  \\\n",
       "HSPC_025    0.000000  7.704143  0.000000  0.000000   9.588054  1.189716   \n",
       "HSPC_031    0.686872  0.686872  0.000000  0.000000   3.506705  4.919811   \n",
       "HSPC_037    0.000000  0.000000  7.394504  0.000000   9.379458  8.510617   \n",
       "LT-HSC_001  7.645229  2.364517  2.364517  0.000000   7.383635  6.319152   \n",
       "HSPC_001    0.000000  0.377367  0.000000  0.377367  10.542702  8.477653   \n",
       "\n",
       "               Wdr78      Dusp1      Def8       Rbm5  BC016579       Emb  \\\n",
       "HSPC_025    0.000000  11.117064  2.275971   9.563837       0.0  8.967574   \n",
       "HSPC_031    0.000000   0.686872  5.565109   9.814485       0.0  9.297106   \n",
       "HSPC_037    1.218731   2.933024  2.316800  10.080613       0.0  9.895967   \n",
       "LT-HSC_001  2.364517   6.392249  3.217169   7.150694       0.0  8.671295   \n",
       "HSPC_001    0.000000   0.923649  8.533429   2.288830       0.0  9.603663   \n",
       "\n",
       "              Elovl1     Myo1f      Lax1    Cyp2j6   Pstpip1  Emp1      Cd44  \\\n",
       "HSPC_025    6.893110  8.575464  1.832751  1.189716  8.673779   0.0  9.114851   \n",
       "HSPC_031    5.689649  8.781518  0.000000  9.254531  7.154525   0.0  2.555402   \n",
       "HSPC_037    3.694520  1.218731  7.091666  0.000000  8.692885   0.0  6.490843   \n",
       "LT-HSC_001  6.766791  5.885018  0.000000  2.364517  7.107933   0.0  8.685906   \n",
       "HSPC_001    9.580752  0.000000  0.000000  5.370192  5.712118   0.0  1.482235   \n",
       "\n",
       "               Mtch2      Pgm2  Krt7  Mir155hg  1110028F11Rik     Acot7  Rnd2  \\\n",
       "HSPC_025    3.118771  0.000000   0.0       0.0       0.000000  8.316089   0.0   \n",
       "HSPC_031    7.873747  1.150286   0.0       0.0       1.150286  8.878166   0.0   \n",
       "HSPC_037    8.277468  0.000000   0.0       0.0       0.000000  8.706702   0.0   \n",
       "LT-HSC_001  8.012835  2.364517   0.0       0.0       0.000000  4.137438   0.0   \n",
       "HSPC_001    1.628905  0.000000   0.0       0.0       0.676211  1.134796   0.0   \n",
       "\n",
       "                 Irf1    H2-DMa    Zfp263  Rxra     Klhl6  A130014A01Rik  \\\n",
       "HSPC_025    10.128487  6.968835  7.174734   0.0  8.056275       1.189716   \n",
       "HSPC_031     1.150286  9.615775  1.500480   0.0  1.782055       0.000000   \n",
       "HSPC_037     1.218731  8.833854  3.538458   0.0  8.923351       0.000000   \n",
       "LT-HSC_001   2.364517  3.217169  7.453613   0.0  8.671295       0.000000   \n",
       "HSPC_001     0.923649  7.362259  9.059246   0.0  0.377367       0.000000   \n",
       "\n",
       "               Ssbp2      Rbp1    Rsl1d1  Gjb3     Asah1    Tmem37     Spcs3  \\\n",
       "HSPC_025    7.442493  1.189716  8.848332   0.0  9.296293  0.000000  7.747791   \n",
       "HSPC_031    8.959939  6.853501  9.008575   0.0  4.358484  0.686872  1.782055   \n",
       "HSPC_037    8.271282  1.218731  8.747376   0.0  1.218731  7.324543  2.657566   \n",
       "LT-HSC_001  7.760152  6.160812  3.217169   0.0  8.499976  0.000000  7.520354   \n",
       "HSPC_001    8.791733  5.661887  8.810106   0.0  0.000000  0.000000  1.482235   \n",
       "\n",
       "               Cenpa  Gfpt2   Tmprss3     Pqlc3     Dhrs3      Jak3   Fam213b  \\\n",
       "HSPC_025    2.275971    0.0  1.189716  0.000000  1.832751  7.730490  0.000000   \n",
       "HSPC_031    2.827391    0.0  0.000000  6.623022  0.000000  0.000000  0.000000   \n",
       "HSPC_037    3.363442    0.0  1.869807  0.000000  1.218731  1.869807  1.218731   \n",
       "LT-HSC_001  0.000000    0.0  0.000000  3.749470  7.814354  8.499976  0.000000   \n",
       "HSPC_001    0.377367    0.0  0.000000  0.000000  0.676211  0.676211  0.000000   \n",
       "\n",
       "              Pafah2       Jun     Lamp1   Camsap3     Grtp1   Gm16794  \\\n",
       "HSPC_025    7.999631  7.535083  9.438924  0.000000  1.189716  0.000000   \n",
       "HSPC_031    1.150286  5.672502  2.017546  0.000000  0.686872  0.000000   \n",
       "HSPC_037    1.869807  2.933024  8.825436  0.000000  0.000000  0.000000   \n",
       "LT-HSC_001  6.766791  9.205236  8.102695  2.364517  0.000000  0.000000   \n",
       "HSPC_001    8.979461  7.441317  0.923649  0.000000  0.000000  0.377367   \n",
       "\n",
       "              Cacybp     Slfn8      Stom   H2-DMb1      H2-D1  Oas1c  Tas1r1  \\\n",
       "HSPC_025    7.212608  9.084533  1.189716  0.000000  11.455247    0.0     0.0   \n",
       "HSPC_031    6.248005  4.595264  1.782055  6.876145  10.860805    0.0     0.0   \n",
       "HSPC_037    8.724920  0.000000  2.316800  1.869807  11.461703    0.0     0.0   \n",
       "LT-HSC_001  5.261087  9.089116  0.000000  3.217169  12.063208    0.0     0.0   \n",
       "HSPC_001    8.317878  0.676211  1.318950  8.222906  12.126537    0.0     0.0   \n",
       "\n",
       "               Rab44  4930579K19Rik       Fos     Nedd4    Cyb561  Mapkapk3  \\\n",
       "HSPC_025    1.189716       0.000000  0.000000  6.418716  0.000000  0.000000   \n",
       "HSPC_031    1.150286       1.150286  0.686872  8.236617  0.000000  8.697130   \n",
       "HSPC_037    0.000000       0.000000  2.316800  6.403053  0.000000  1.869807   \n",
       "LT-HSC_001  0.000000       0.000000  8.938628  9.421470  0.000000  3.217169   \n",
       "HSPC_001    0.000000       0.000000  0.676211  0.676211  0.377367  0.676211   \n",
       "\n",
       "              Inpp4b    Ccdc69     Slfn2  Tmem220  2610035D17Rik    Snap23  \\\n",
       "HSPC_025    0.000000  8.748734  1.189716      0.0       1.189716  6.159051   \n",
       "HSPC_031    0.000000  0.686872  0.000000      0.0       8.470365  0.686872   \n",
       "HSPC_037    6.016053  1.218731  0.000000      0.0       1.218731  7.586077   \n",
       "LT-HSC_001  0.000000  5.666688  9.918720      0.0       7.552600  3.217169   \n",
       "HSPC_001    0.000000  0.377367  0.000000      0.0       8.644301  8.425918   \n",
       "\n",
       "               Nprl2    Rasal3  Ighv1-82      Batf  Tmem121      Rrm2  \\\n",
       "HSPC_025    7.494663  8.718340  1.189716  7.649962      0.0  2.614548   \n",
       "HSPC_031    9.470811  6.152300  0.000000  0.000000      0.0  2.697797   \n",
       "HSPC_037    8.036182  9.734499  0.000000  0.000000      0.0  1.218731   \n",
       "LT-HSC_001  3.749470  7.674830  0.000000  2.364517      0.0  4.442877   \n",
       "HSPC_001    0.377367  0.000000  0.000000  0.000000      0.0  2.739936   \n",
       "\n",
       "               Surf4    Zfp386  Mamdc4  B230217C12Rik  Zbtb42     Sstr2  Pld2  \\\n",
       "HSPC_025    1.832751  6.306441     0.0            0.0     0.0  0.000000   0.0   \n",
       "HSPC_031    5.058266  8.477787     0.0            0.0     0.0  8.164863   0.0   \n",
       "HSPC_037    4.783895  1.869807     0.0            0.0     0.0  0.000000   0.0   \n",
       "LT-HSC_001  4.442877  8.499976     0.0            0.0     0.0  0.000000   0.0   \n",
       "HSPC_001    8.959672  0.377367     0.0            0.0     0.0  0.000000   0.0   \n",
       "\n",
       "               Cers5      Ighd       Ighm    Cyp4v3      Limd2    Nlrp1a  \\\n",
       "HSPC_025    7.135838  0.000000   5.253659  4.337438   8.395074  0.000000   \n",
       "HSPC_031    1.150286  0.000000   7.513468  0.000000   5.739898  5.655149   \n",
       "HSPC_037    7.405841  0.000000   9.115131  0.000000   8.911469  0.000000   \n",
       "LT-HSC_001  2.364517  0.000000  12.165795  0.000000   9.215343  0.000000   \n",
       "HSPC_001    0.000000  0.377367   1.883902  0.000000  10.312110  0.000000   \n",
       "\n",
       "                H2-Q6      P4hb       Grn     H2-Q7  Gm10863      Hhex  \\\n",
       "HSPC_025     8.350471  9.714428  8.526184  7.649962      0.0  8.718340   \n",
       "HSPC_031     9.317932  6.701193  0.000000  8.401790      0.0  1.150286   \n",
       "HSPC_037     7.874218  8.911469  2.657566  7.348242      0.0  6.045346   \n",
       "LT-HSC_001  10.471911  5.982925  0.000000  9.322084      0.0  7.674830   \n",
       "HSPC_001     4.124600  8.799498  1.134796  3.360089      0.0  7.558065   \n",
       "\n",
       "               Pcbp4      Tubb5     Itgb7      Gpam      Cbx5   Fam184a  \\\n",
       "HSPC_025    0.000000  10.165970  8.137279  0.000000  7.309000  1.189716   \n",
       "HSPC_031    0.686872   8.460409  0.686872  0.000000  2.946297  0.000000   \n",
       "HSPC_037    1.869807  10.078843  1.218731  1.218731  2.933024  0.000000   \n",
       "LT-HSC_001  0.000000  10.389508  2.364517  0.000000  9.601794  0.000000   \n",
       "HSPC_001    2.374501  10.136633  1.482235  0.000000  7.551182  0.000000   \n",
       "\n",
       "            Tmem106a  Adamtsl5     Cd79b       Nfe2     H2-Ob   Tcp11l2  \\\n",
       "HSPC_025    0.000000       0.0  1.832751   8.641746  7.992392  0.000000   \n",
       "HSPC_031    0.686872       0.0  0.686872   9.285875  1.782055  5.208578   \n",
       "HSPC_037    1.218731       0.0  0.000000   9.000159  7.417090  9.073083   \n",
       "LT-HSC_001  0.000000       0.0  4.442877  10.104202  0.000000  8.361217   \n",
       "HSPC_001    7.451210       0.0  0.676211  10.394527  8.633481  0.377367   \n",
       "\n",
       "              Shisa5    Samd10  Hspa12b     Tcea2      Ctsd     Rgs19  \\\n",
       "HSPC_025    9.331144  7.431828      0.0  1.189716  9.334011  9.403884   \n",
       "HSPC_031    9.719592  0.000000      0.0  9.479464  6.359344  8.380830   \n",
       "HSPC_037    7.383077  1.218731      0.0  2.316800  4.190334  9.083711   \n",
       "LT-HSC_001  9.164084  0.000000      0.0  7.866592  8.700371  8.548753   \n",
       "HSPC_001    9.589737  0.000000      0.0  0.000000  7.974620  9.727375   \n",
       "\n",
       "               Procr     Tnni2     Acap1       Bin2  Ropn1l    Lmbrd2  \\\n",
       "HSPC_025    0.000000  1.189716  9.007623   9.390178     0.0  0.000000   \n",
       "HSPC_031    0.686872  0.000000  9.978684   9.464599     0.0  0.686872   \n",
       "HSPC_037    2.657566  1.218731  3.164226  10.033882     0.0  0.000000   \n",
       "LT-HSC_001  9.143059  0.000000  0.000000   7.965715     0.0  0.000000   \n",
       "HSPC_001    9.550012  0.000000  9.245576   9.297222     0.0  0.000000   \n",
       "\n",
       "             Laptm4b  Il7r     Capsl     Angpt1     Spag1    H2-Ab1     Gfi1b  \\\n",
       "HSPC_025    8.028232   0.0  0.000000   7.721761  0.000000  0.000000  1.189716   \n",
       "HSPC_031    0.686872   0.0  0.000000   2.397399  0.000000  1.150286  2.017546   \n",
       "HSPC_037    2.657566   0.0  0.000000   8.418471  0.000000  1.869807  1.869807   \n",
       "LT-HSC_001  9.033078   0.0  0.000000  10.577548  0.000000  8.564652  2.364517   \n",
       "HSPC_001    8.703439   0.0  0.377367   8.125878  0.377367  0.000000  1.318950   \n",
       "\n",
       "                H2-Aa      Ctsw     Hspa5   Tbc1d10c      Rom1  Smpd2  \\\n",
       "HSPC_025     1.189716  6.581951  8.475161   7.054751  0.000000    0.0   \n",
       "HSPC_031     0.000000  7.574740  9.898919   0.686872  0.000000    0.0   \n",
       "HSPC_037     7.832819  1.218731  9.101251  10.376936  0.000000    0.0   \n",
       "LT-HSC_001  10.987443  0.000000  9.464477   8.824412  0.000000    0.0   \n",
       "HSPC_001     0.676211  0.676211  6.237319   6.833019  0.377367    0.0   \n",
       "\n",
       "                Ctsf      Fgd2      Cct8    Mamdc2     Stip1  Gm28043  \\\n",
       "HSPC_025    0.000000  6.939021  9.742748  6.829531  7.992392      0.0   \n",
       "HSPC_031    0.686872  0.686872  9.306862  0.000000  1.782055      0.0   \n",
       "HSPC_037    8.966101  5.924429  8.489459  0.000000  3.694520      0.0   \n",
       "LT-HSC_001  0.000000  0.000000  9.802852  0.000000  4.137438      0.0   \n",
       "HSPC_001    7.043829  0.000000  8.814903  0.000000  8.390367      0.0   \n",
       "\n",
       "                 Cd27  Rdh5  Aldoc    Prss57    Arpc1b      Fut7      Cd86  \\\n",
       "HSPC_025     8.130700   0.0    0.0  1.189716  9.214658  7.887028  0.000000   \n",
       "HSPC_031    10.211670   0.0    0.0  0.000000  9.347942  2.017546  0.000000   \n",
       "HSPC_037    10.911210   0.0    0.0  1.218731  9.804700  7.439328  1.218731   \n",
       "LT-HSC_001   9.943251   0.0    0.0  0.000000  9.937158  2.364517  0.000000   \n",
       "HSPC_001     8.732122   0.0    0.0  8.673117  9.525641  0.923649  0.000000   \n",
       "\n",
       "             Serpinf1       Cd9   Gm37274  Wdr38   Angptl6  Smad6  Chad  \\\n",
       "HSPC_025     7.399354  2.275971  1.189716    0.0  6.763021    0.0   0.0   \n",
       "HSPC_031     1.782055  7.985573  0.000000    0.0  0.000000    0.0   0.0   \n",
       "HSPC_037     2.657566  1.218731  0.000000    0.0  1.218731    0.0   0.0   \n",
       "LT-HSC_001   8.324337  2.364517  0.000000    0.0  0.000000    0.0   0.0   \n",
       "HSPC_001    10.337334  3.853357  0.000000    0.0  0.676211    0.0   0.0   \n",
       "\n",
       "              Arhgdib      Eogt  Llgl2      Cst7  E130215H24Rik  Gm27201  \\\n",
       "HSPC_025    10.830842  0.000000    0.0  1.832751            0.0      0.0   \n",
       "HSPC_031     8.713988  0.000000    0.0  0.686872            0.0      0.0   \n",
       "HSPC_037    11.608145  0.000000    0.0  0.000000            0.0      0.0   \n",
       "LT-HSC_001  10.420417  0.000000    0.0  2.364517            0.0      0.0   \n",
       "HSPC_001    11.414046  0.377367    0.0  0.923649            0.0      0.0   \n",
       "\n",
       "                Pcna    Rnf114  Gchfr    Cacfd1      Jam3    Fkbp1a      Eps8  \\\n",
       "HSPC_025    8.608986  8.316089    0.0  1.189716  0.000000  9.173640  5.809578   \n",
       "HSPC_031    3.582099  2.219938    0.0  0.686872  0.000000  8.504679  0.000000   \n",
       "HSPC_037    6.309572  2.657566    0.0  0.000000  0.000000  4.081427  0.000000   \n",
       "LT-HSC_001  8.080749  2.364517    0.0  0.000000  8.145608  6.319152  0.000000   \n",
       "HSPC_001    1.628905  0.676211    0.0  0.000000  0.000000  8.230106  0.000000   \n",
       "\n",
       "            Epb41l1     Traf1     Pdia3     Mgst1  Slc2a6  Spint1       Lmo2  \\\n",
       "HSPC_025        0.0  0.000000  9.414757  7.612677     0.0     0.0  10.214998   \n",
       "HSPC_031        0.0  0.000000  7.961069  7.943307     0.0     0.0  11.641084   \n",
       "HSPC_037        0.0  0.000000  8.816968  6.309572     0.0     0.0  10.319646   \n",
       "LT-HSC_001      0.0  8.811145  9.132430  0.000000     0.0     0.0  10.173233   \n",
       "HSPC_001        0.0  0.000000  9.630032  0.000000     0.0     0.0  10.754757   \n",
       "\n",
       "                 Eng     Skap2     Vamp5  Plxna1  Klrb1f   Tmem176b      Mcm2  \\\n",
       "HSPC_025    6.374848  7.545014  6.051698     0.0     0.0   9.921090  8.860312   \n",
       "HSPC_031    6.783355  8.111786  8.133878     0.0     0.0   9.748516  1.150286   \n",
       "HSPC_037    5.485582  7.019698  8.155161     0.0     0.0   9.396608  2.657566   \n",
       "LT-HSC_001  9.676858  7.989468  8.267186     0.0     0.0  10.812068  2.364517   \n",
       "HSPC_001    8.291965  7.555774  9.070521     0.0     0.0   9.177187  1.762030   \n",
       "\n",
       "             Slc52a3      Cct7     Ptgs1      Cnn2     Prim1  Rwdd2a  \\\n",
       "HSPC_025    0.000000  8.815891  9.269591  7.739166  7.545014     0.0   \n",
       "HSPC_031    0.000000  3.787179  0.000000  8.809238  1.782055     0.0   \n",
       "HSPC_037    0.000000  9.122022  0.000000  1.218731  2.933024     0.0   \n",
       "LT-HSC_001  2.364517  9.264843  7.552600  8.580378  2.364517     0.0   \n",
       "HSPC_001    0.000000  8.746254  0.377367  7.629506  1.134796     0.0   \n",
       "\n",
       "                Matk      Atp5b       Cdk4  Robo4   Gm15915   Gm10101  \\\n",
       "HSPC_025    0.000000   9.953140  10.011602    0.0  2.275971  0.000000   \n",
       "HSPC_031    7.454503  10.444765   8.362235    0.0  2.017546  0.000000   \n",
       "HSPC_037    0.000000  10.647927   8.931218    0.0  2.657566  0.000000   \n",
       "LT-HSC_001  8.166596  10.933363   7.732268    0.0  0.000000  0.000000   \n",
       "HSPC_001    0.000000  10.477341  10.039568    0.0  1.318950  0.377367   \n",
       "\n",
       "               Abhd4     Rab36    Metap2      Fzr1     Pa2g4    Smim24  \\\n",
       "HSPC_025    8.110780  0.000000  8.883978  7.273603  9.449536  0.000000   \n",
       "HSPC_031    0.686872  0.000000  5.508257  0.686872  7.615685  0.686872   \n",
       "HSPC_037    9.287452  0.000000  7.866032  3.363442  8.384491  8.114280   \n",
       "LT-HSC_001  2.364517  0.000000  8.166596  8.770595  2.364517  0.000000   \n",
       "HSPC_001    6.563421  0.377367  9.440054  1.134796  1.134796  7.707927   \n",
       "\n",
       "             Gm16104  Perp      Fig4     Myct1      Lsp1  Alpk3  Zfp28  Zfp78  \\\n",
       "HSPC_025    0.000000   0.0  6.282898  1.189716  9.245434    0.0    0.0    0.0   \n",
       "HSPC_031    0.000000   0.0  0.000000  0.000000  7.439376    0.0    0.0    0.0   \n",
       "HSPC_037    0.000000   0.0  8.320036  8.148427  7.615671    0.0    0.0    0.0   \n",
       "LT-HSC_001  2.364517   0.0  0.000000  9.121723  9.481327    0.0    0.0    0.0   \n",
       "HSPC_001    0.000000   0.0  0.377367  5.359726  9.581316    0.0    0.0    0.0   \n",
       "\n",
       "            Zfp773  Mill2      Gpr4  Nectin2    Tyrobp  Plekhf1      Nkg7  \\\n",
       "HSPC_025       0.0    0.0  0.000000      0.0  1.189716      0.0  2.614548   \n",
       "HSPC_031       0.0    0.0  2.827391      0.0  3.158217      0.0  2.219938   \n",
       "HSPC_037       0.0    0.0  0.000000      0.0  7.336441      0.0  8.407233   \n",
       "LT-HSC_001     0.0    0.0  0.000000      0.0  0.000000      0.0  3.749470   \n",
       "HSPC_001       0.0    0.0  0.000000      0.0  0.377367      0.0  1.134796   \n",
       "\n",
       "             Osbpl1a  Slc27a6    Gm4951    Zfp438     Rab18  \n",
       "HSPC_025    0.000000      0.0  0.000000  1.189716  9.263590  \n",
       "HSPC_031    2.017546      0.0  0.000000  0.000000  0.686872  \n",
       "HSPC_037    1.218731      0.0  0.000000  1.218731  8.641061  \n",
       "LT-HSC_001  6.820749      0.0  7.107933  2.364517  6.242153  \n",
       "HSPC_001    1.134796      0.0  1.883902  0.000000  1.318950  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref_data = pd.read_csv(\"data_filtered_vargenes/GSE81682_Hematopoiesis.csv\", index_col=0)\n",
    "ref_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f9384398",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>scBoolSeqBinarizer()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;scBoolSeqBinarizer<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>scBoolSeqBinarizer()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "scBoolSeqBinarizer()"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scbool = scBoolSeq().fit(ref_data)\n",
    "scbool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a9730412",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  5, 18, 26, 35])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(traj_df.index.map(lambda x: \"_to_\" not in x))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9eb3b01d",
   "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>x0</th>\n",
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       "      <th>x20</th>\n",
       "      <th>x21</th>\n",
       "      <th>x23</th>\n",
       "      <th>x24</th>\n",
       "      <th>x26</th>\n",
       "      <th>x3</th>\n",
       "      <th>x4</th>\n",
       "      <th>x6</th>\n",
       "      <th>x8</th>\n",
       "      <th>x9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>init</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bifurcation</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stable1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stable2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stable3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             x0  x1  x10  x11  x12  x13  x14  x15  x17  x2  x20  x21  x23  \\\n",
       "init          0   0    1    0    0    1    1    0    0   1    1    0    1   \n",
       "bifurcation   0   0    1    0    1    1    1    0    1   0    0    0    1   \n",
       "stable1       1   0    1    1    0    0    0    0    1   1    0    0    0   \n",
       "stable2       0   0    1    1    1    0    1    1    0   0    0    0    0   \n",
       "stable3       0   1    0    0    1    1    1    1    0   0    0    1    0   \n",
       "\n",
       "             x24  x26  x3  x4  x6  x8  x9  \n",
       "init           1    1   1   1   1   0   0  \n",
       "bifurcation    1    1   0   1   1   0   0  \n",
       "stable1        0    0   0   0   0   0   1  \n",
       "stable2        0    1   0   0   0   0   0  \n",
       "stable3        0    1   0   0   1   1   0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traj_df.iloc[np.where(traj_df.index.map(lambda x: \"_to_\" not in x))[0], :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0743b283",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "init           11\n",
       "bifurcation    10\n",
       "stable1         6\n",
       "stable2         6\n",
       "stable3         9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traj_df.iloc[np.where(traj_df.index.map(lambda x: \"_to_\" not in x))[0], :].sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "42ba3989",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([], dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traj_df.columns[(traj_df.var() == 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c2ef280e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([597, 148, 149, 100,  83, 597,  78,  90,  81,  95, 112,  79, 133,\n",
       "        89, 132,  95, 133, 147, 598,  91, 114, 105,  90, 118, 141, 139,\n",
       "       538, 139, 111, 123, 146, 122,  73, 128, 129, 517])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_samples = np.random.default_rng(SEED).integers(70, 150, size=len(traj_df.index))\n",
    "_non_transitory = np.where(traj_df.index.map(lambda x: \"_to_\" not in x))[0]\n",
    "n_samples[_non_transitory] = np.random.default_rng(SEED).integers(500, 600, size=len(_non_transitory))\n",
    "\n",
    "n_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "eb583adc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6360, 20)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = traj_df.copy(deep=True).values.repeat(n_samples, axis=0)\n",
    "bindata = pd.DataFrame(d, columns=traj_df.columns)\n",
    "bindata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "aa7fda22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "        text-align: right;\n",
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       "</style>\n",
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       "      <td>3.928711</td>\n",
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       "      <td>16.425950</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>3.139584</td>\n",
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       "      <td>10.984037</td>\n",
       "      <td>15.591004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.768003</td>\n",
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       "      <td>11.755922</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>1.077996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.469563</td>\n",
       "      <td>1.511944</td>\n",
       "      <td>11.849830</td>\n",
       "      <td>1.692185</td>\n",
       "      <td>4.213604</td>\n",
       "      <td>11.058825</td>\n",
       "      <td>16.287910</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.773210</td>\n",
       "      <td>9.472351</td>\n",
       "      <td>11.699550</td>\n",
       "      <td>3.937692</td>\n",
       "      <td>10.299978</td>\n",
       "      <td>8.135352</td>\n",
       "      <td>9.011899</td>\n",
       "      <td>2.208726</td>\n",
       "      <td>8.904294</td>\n",
       "      <td>7.919568</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.819151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.670469</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.900362</td>\n",
       "      <td>0.484062</td>\n",
       "      <td>3.776691</td>\n",
       "      <td>9.951019</td>\n",
       "      <td>15.854611</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.754616</td>\n",
       "      <td>9.250049</td>\n",
       "      <td>11.667626</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.795610</td>\n",
       "      <td>9.340702</td>\n",
       "      <td>8.964883</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.023884</td>\n",
       "      <td>9.864496</td>\n",
       "      <td>4.878799</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         x0        x1        x10       x11       x12        x13        x14  \\\n",
       "0  6.356687  4.144478  11.073216  0.000000  4.458495   9.192170  16.163407   \n",
       "1  0.835645  2.010283  12.316921  1.462056  3.928711   9.494834  16.425950   \n",
       "2  3.852430  3.139584   9.896885  2.567859  1.168861  10.984037  15.591004   \n",
       "3  0.469563  1.511944  11.849830  1.692185  4.213604  11.058825  16.287910   \n",
       "4  1.670469  0.000000   9.900362  0.484062  3.776691   9.951019  15.854611   \n",
       "\n",
       "   x15       x17        x2        x20       x21        x23       x24  \\\n",
       "0  0.0  4.591142  5.931198   9.789617  0.000000   8.545147  8.337190   \n",
       "1  0.0  3.341043  8.640534  11.174345  1.422358   8.078405  9.230566   \n",
       "2  0.0  2.768003  8.290386  11.755922  0.000000   7.578716  9.167828   \n",
       "3  0.0  3.773210  9.472351  11.699550  3.937692  10.299978  8.135352   \n",
       "4  0.0  1.754616  9.250049  11.667626  0.000000   8.795610  9.340702   \n",
       "\n",
       "         x26        x3         x4         x6        x8        x9  \n",
       "0  11.048758  1.202813   8.120571  11.889274  0.000000  0.199554  \n",
       "1  11.760354  0.000000  13.295775   5.718735  0.000000  0.436888  \n",
       "2  10.098188  0.000000  12.921173   6.217599  0.000000  1.077996  \n",
       "3   9.011899  2.208726   8.904294   7.919568  0.000000  1.819151  \n",
       "4   8.964883  0.000000  12.023884   9.864496  4.878799  0.000000  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = scbool.sample_counts(bindata, n_samples_per_state=1, random_state=SEED)\n",
    "counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "30887757",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>cellID</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>init_0</th>\n",
       "      <td>6.356687</td>\n",
       "      <td>4.144478</td>\n",
       "      <td>11.073216</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>8.337190</td>\n",
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       "      <td>1.202813</td>\n",
       "      <td>8.120571</td>\n",
       "      <td>11.889274</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.199554</td>\n",
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       "      <th>init_1</th>\n",
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       "      <td>2.010283</td>\n",
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       "      <td>1.462056</td>\n",
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       "      <td>3.341043</td>\n",
       "      <td>8.640534</td>\n",
       "      <td>11.174345</td>\n",
       "      <td>1.422358</td>\n",
       "      <td>8.078405</td>\n",
       "      <td>9.230566</td>\n",
       "      <td>11.760354</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.295775</td>\n",
       "      <td>5.718735</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.436888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>init_2</th>\n",
       "      <td>3.852430</td>\n",
       "      <td>3.139584</td>\n",
       "      <td>9.896885</td>\n",
       "      <td>2.567859</td>\n",
       "      <td>1.168861</td>\n",
       "      <td>10.984037</td>\n",
       "      <td>15.591004</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.768003</td>\n",
       "      <td>8.290386</td>\n",
       "      <td>11.755922</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.578716</td>\n",
       "      <td>9.167828</td>\n",
       "      <td>10.098188</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.921173</td>\n",
       "      <td>6.217599</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.077996</td>\n",
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       "    <tr>\n",
       "      <th>init_3</th>\n",
       "      <td>0.469563</td>\n",
       "      <td>1.511944</td>\n",
       "      <td>11.849830</td>\n",
       "      <td>1.692185</td>\n",
       "      <td>4.213604</td>\n",
       "      <td>11.058825</td>\n",
       "      <td>16.287910</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.773210</td>\n",
       "      <td>9.472351</td>\n",
       "      <td>11.699550</td>\n",
       "      <td>3.937692</td>\n",
       "      <td>10.299978</td>\n",
       "      <td>8.135352</td>\n",
       "      <td>9.011899</td>\n",
       "      <td>2.208726</td>\n",
       "      <td>8.904294</td>\n",
       "      <td>7.919568</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.819151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>init_4</th>\n",
       "      <td>1.670469</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.900362</td>\n",
       "      <td>0.484062</td>\n",
       "      <td>3.776691</td>\n",
       "      <td>9.951019</td>\n",
       "      <td>15.854611</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.754616</td>\n",
       "      <td>9.250049</td>\n",
       "      <td>11.667626</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.795610</td>\n",
       "      <td>9.340702</td>\n",
       "      <td>8.964883</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.023884</td>\n",
       "      <td>9.864496</td>\n",
       "      <td>4.878799</td>\n",
       "      <td>0.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stable3_512</th>\n",
       "      <td>1.300855</td>\n",
       "      <td>8.825579</td>\n",
       "      <td>7.985000</td>\n",
       "      <td>0.810334</td>\n",
       "      <td>10.846489</td>\n",
       "      <td>9.787957</td>\n",
       "      <td>15.642349</td>\n",
       "      <td>7.944253</td>\n",
       "      <td>5.088337</td>\n",
       "      <td>0.274981</td>\n",
       "      <td>4.116469</td>\n",
       "      <td>8.465650</td>\n",
       "      <td>0.424795</td>\n",
       "      <td>2.757575</td>\n",
       "      <td>9.170532</td>\n",
       "      <td>0.347563</td>\n",
       "      <td>3.955298</td>\n",
       "      <td>8.645170</td>\n",
       "      <td>6.032245</td>\n",
       "      <td>1.778593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stable3_513</th>\n",
       "      <td>4.199834</td>\n",
       "      <td>7.993578</td>\n",
       "      <td>8.270309</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.307799</td>\n",
       "      <td>9.681840</td>\n",
       "      <td>15.369320</td>\n",
       "      <td>6.869272</td>\n",
       "      <td>3.920587</td>\n",
       "      <td>1.151862</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.296890</td>\n",
       "      <td>3.405255</td>\n",
       "      <td>1.457118</td>\n",
       "      <td>8.582127</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.562706</td>\n",
       "      <td>7.944660</td>\n",
       "      <td>5.977704</td>\n",
       "      <td>1.293997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stable3_514</th>\n",
       "      <td>3.964094</td>\n",
       "      <td>8.388342</td>\n",
       "      <td>9.156939</td>\n",
       "      <td>5.837674</td>\n",
       "      <td>9.780715</td>\n",
       "      <td>9.857774</td>\n",
       "      <td>15.715133</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.564653</td>\n",
       "      <td>3.870135</td>\n",
       "      <td>3.941707</td>\n",
       "      <td>7.034641</td>\n",
       "      <td>4.114664</td>\n",
       "      <td>1.730180</td>\n",
       "      <td>9.014032</td>\n",
       "      <td>0.582223</td>\n",
       "      <td>4.082082</td>\n",
       "      <td>8.046503</td>\n",
       "      <td>6.155325</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stable3_515</th>\n",
       "      <td>3.208976</td>\n",
       "      <td>7.845625</td>\n",
       "      <td>8.703443</td>\n",
       "      <td>2.044151</td>\n",
       "      <td>6.673158</td>\n",
       "      <td>10.069703</td>\n",
       "      <td>15.674563</td>\n",
       "      <td>8.202008</td>\n",
       "      <td>5.028622</td>\n",
       "      <td>2.230930</td>\n",
       "      <td>4.098484</td>\n",
       "      <td>8.628150</td>\n",
       "      <td>2.762687</td>\n",
       "      <td>3.654045</td>\n",
       "      <td>10.527947</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.209269</td>\n",
       "      <td>11.592137</td>\n",
       "      <td>5.953017</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stable3_516</th>\n",
       "      <td>4.803261</td>\n",
       "      <td>5.711744</td>\n",
       "      <td>6.740230</td>\n",
       "      <td>0.036132</td>\n",
       "      <td>10.755981</td>\n",
       "      <td>10.121012</td>\n",
       "      <td>16.008064</td>\n",
       "      <td>7.343627</td>\n",
       "      <td>3.703978</td>\n",
       "      <td>1.591004</td>\n",
       "      <td>4.117606</td>\n",
       "      <td>7.208061</td>\n",
       "      <td>0.844144</td>\n",
       "      <td>3.306094</td>\n",
       "      <td>8.971130</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.227802</td>\n",
       "      <td>7.920768</td>\n",
       "      <td>7.117525</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6360 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   x0        x1        x10       x11        x12        x13  \\\n",
       "cellID                                                                       \n",
       "init_0       6.356687  4.144478  11.073216  0.000000   4.458495   9.192170   \n",
       "init_1       0.835645  2.010283  12.316921  1.462056   3.928711   9.494834   \n",
       "init_2       3.852430  3.139584   9.896885  2.567859   1.168861  10.984037   \n",
       "init_3       0.469563  1.511944  11.849830  1.692185   4.213604  11.058825   \n",
       "init_4       1.670469  0.000000   9.900362  0.484062   3.776691   9.951019   \n",
       "...               ...       ...        ...       ...        ...        ...   \n",
       "stable3_512  1.300855  8.825579   7.985000  0.810334  10.846489   9.787957   \n",
       "stable3_513  4.199834  7.993578   8.270309  0.000000  11.307799   9.681840   \n",
       "stable3_514  3.964094  8.388342   9.156939  5.837674   9.780715   9.857774   \n",
       "stable3_515  3.208976  7.845625   8.703443  2.044151   6.673158  10.069703   \n",
       "stable3_516  4.803261  5.711744   6.740230  0.036132  10.755981  10.121012   \n",
       "\n",
       "                   x14       x15       x17        x2        x20       x21  \\\n",
       "cellID                                                                      \n",
       "init_0       16.163407  0.000000  4.591142  5.931198   9.789617  0.000000   \n",
       "init_1       16.425950  0.000000  3.341043  8.640534  11.174345  1.422358   \n",
       "init_2       15.591004  0.000000  2.768003  8.290386  11.755922  0.000000   \n",
       "init_3       16.287910  0.000000  3.773210  9.472351  11.699550  3.937692   \n",
       "init_4       15.854611  0.000000  1.754616  9.250049  11.667626  0.000000   \n",
       "...                ...       ...       ...       ...        ...       ...   \n",
       "stable3_512  15.642349  7.944253  5.088337  0.274981   4.116469  8.465650   \n",
       "stable3_513  15.369320  6.869272  3.920587  1.151862   0.000000  6.296890   \n",
       "stable3_514  15.715133  0.000000  3.564653  3.870135   3.941707  7.034641   \n",
       "stable3_515  15.674563  8.202008  5.028622  2.230930   4.098484  8.628150   \n",
       "stable3_516  16.008064  7.343627  3.703978  1.591004   4.117606  7.208061   \n",
       "\n",
       "                   x23       x24        x26        x3         x4         x6  \\\n",
       "cellID                                                                        \n",
       "init_0        8.545147  8.337190  11.048758  1.202813   8.120571  11.889274   \n",
       "init_1        8.078405  9.230566  11.760354  0.000000  13.295775   5.718735   \n",
       "init_2        7.578716  9.167828  10.098188  0.000000  12.921173   6.217599   \n",
       "init_3       10.299978  8.135352   9.011899  2.208726   8.904294   7.919568   \n",
       "init_4        8.795610  9.340702   8.964883  0.000000  12.023884   9.864496   \n",
       "...                ...       ...        ...       ...        ...        ...   \n",
       "stable3_512   0.424795  2.757575   9.170532  0.347563   3.955298   8.645170   \n",
       "stable3_513   3.405255  1.457118   8.582127  0.000000   4.562706   7.944660   \n",
       "stable3_514   4.114664  1.730180   9.014032  0.582223   4.082082   8.046503   \n",
       "stable3_515   2.762687  3.654045  10.527947  0.000000   4.209269  11.592137   \n",
       "stable3_516   0.844144  3.306094   8.971130  0.000000   4.227802   7.920768   \n",
       "\n",
       "                   x8        x9  \n",
       "cellID                           \n",
       "init_0       0.000000  0.199554  \n",
       "init_1       0.000000  0.436888  \n",
       "init_2       0.000000  1.077996  \n",
       "init_3       0.000000  1.819151  \n",
       "init_4       4.878799  0.000000  \n",
       "...               ...       ...  \n",
       "stable3_512  6.032245  1.778593  \n",
       "stable3_513  5.977704  1.293997  \n",
       "stable3_514  6.155325  0.000000  \n",
       "stable3_515  5.953017  0.000000  \n",
       "stable3_516  7.117525  0.000000  \n",
       "\n",
       "[6360 rows x 20 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids = [f\"{x}_{y}\" for i,x in enumerate(traj_df.index) for y in range(n_samples[i])]\n",
    "counts.index = ids\n",
    "counts.index.name = \"cellID\"\n",
    "#counts.index.map(lambda x: x.split(\"_\")[0]).unique()\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1fd30145",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([], dtype='object', name='cellID')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "init = counts.index[ np.where(counts.index.map(lambda x: \"fp2\" in x))[0] ]\n",
    "init"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "21760a53",
   "metadata": {},
   "outputs": [],
   "source": [
    "if init.has_duplicates:\n",
    "    print(\"Index has duplicates\")\n",
    "    print(f\"{len(init)} elements, only {len(init.drop_duplicates())} are unique\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "93f34113",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 46.8 s, sys: 51.9 s, total: 1min 38s\n",
      "Wall time: 7.12 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>scBoolSeqBinarizer()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;scBoolSeqBinarizer<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>scBoolSeqBinarizer()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "scBoolSeqBinarizer()"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time synth_scbool = scBoolSeq().fit(counts)\n",
    "synth_scbool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b7ae1289",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Mean', ylabel='Variance'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "synth_scbool.criteria_.plot.scatter(x=\"Mean\", y=\"Variance\", c=\"DropOutRate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c3c084f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def label_of_index(idx):\n",
    "    return \"_\".join(idx.split(\"_\")[:-1]) if \"_to_\" in idx else idx\n",
    "\n",
    "labels = [label_of_index(idx) for idx in traj_df.index]\n",
    "\n",
    "color_map_1 = {\n",
    "    \"ES\": \"#00f7ff\", #\"#32a899\",\n",
    "    \"ES_to_ECTO\": \"#3277a8\",\n",
    "    \"ECTO\": \"#3252a8\",\n",
    "    \"ES_to_MESENDO\": \"#32a836\",\n",
    "    \"MESENDO_to_DEFENDO\": \"#99a832\",\n",
    "    \"MESENDO\": \"#50822f\",\n",
    "    \"DEFENDO\": \"#dbdb23\",\n",
    "    \"MESENDO_to_MESO\": \"#db7323\",\n",
    "    \"MESO\": \"#c24438\",\n",
    "}\n",
    "\n",
    "color_map = {\n",
    "    \"init\": \"#00f7ff\", #\"#32a899\",\n",
    "    \"init_to_stable1\": \"#3277a8\",\n",
    "    \"stable1\": \"#3252a8\",\n",
    "    \"init_to_bifurcation\": \"#32a836\",\n",
    "    \"bifurcation_to_stable3\": \"#99a832\",\n",
    "    \"bifurcation\": \"#50822f\",\n",
    "    \"stable3\": \"#dbdb23\",\n",
    "    \"bifurcation_to_stable2\": \"#db7323\",\n",
    "    \"stable2\": \"#c24438\",\n",
    "}\n",
    "\n",
    "\n",
    "assert len(color_map.values()) == len(set(color_map.values())), \"Colours are not unique\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ba5331e9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bifurcation',\n",
       " 'bifurcation_to_stable2',\n",
       " 'bifurcation_to_stable3',\n",
       " 'init',\n",
       " 'init_to_bifurcation',\n",
       " 'init_to_stable1',\n",
       " 'stable1',\n",
       " 'stable2',\n",
       " 'stable3']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(set(labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0eb54234",
   "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>label</th>\n",
       "      <th>label_color</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cellID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>init_0</th>\n",
       "      <td>init</td>\n",
       "      <td>#00f7ff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>init_1</th>\n",
       "      <td>init</td>\n",
       "      <td>#00f7ff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>init_2</th>\n",
       "      <td>init</td>\n",
       "      <td>#00f7ff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>init_3</th>\n",
       "      <td>init</td>\n",
       "      <td>#00f7ff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>init_4</th>\n",
       "      <td>init</td>\n",
       "      <td>#00f7ff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stable3_512</th>\n",
       "      <td>stable3</td>\n",
       "      <td>#dbdb23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stable3_513</th>\n",
       "      <td>stable3</td>\n",
       "      <td>#dbdb23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stable3_514</th>\n",
       "      <td>stable3</td>\n",
       "      <td>#dbdb23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stable3_515</th>\n",
       "      <td>stable3</td>\n",
       "      <td>#dbdb23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stable3_516</th>\n",
       "      <td>stable3</td>\n",
       "      <td>#dbdb23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6360 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               label label_color\n",
       "cellID                          \n",
       "init_0          init     #00f7ff\n",
       "init_1          init     #00f7ff\n",
       "init_2          init     #00f7ff\n",
       "init_3          init     #00f7ff\n",
       "init_4          init     #00f7ff\n",
       "...              ...         ...\n",
       "stable3_512  stable3     #dbdb23\n",
       "stable3_513  stable3     #dbdb23\n",
       "stable3_514  stable3     #dbdb23\n",
       "stable3_515  stable3     #dbdb23\n",
       "stable3_516  stable3     #dbdb23\n",
       "\n",
       "[6360 rows x 2 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata = [[l, color_map[l]] for i,l in enumerate(labels) for _ in range(n_samples[i])]\n",
    "metadata = pd.DataFrame(metadata, columns=[\"label\", \"label_color\"])\n",
    "metadata.index = counts.index\n",
    "metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8cbbf33d",
   "metadata": {},
   "outputs": [],
   "source": [
    "EXPORT = False\n",
    "\n",
    "if EXPORT:\n",
    "    counts.T.to_csv(checkpoint_dir / \"stream_expr_data.tsv\", sep=\"\\t\")\n",
    "    metadata.to_csv(checkpoint_dir / \"stream_expr_metadata.tsv\", sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c23ff0a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import LocallyLinearEmbedding, TSNE, SpectralEmbedding\n",
    "from sklearn.pipeline import Pipeline, FunctionTransformer\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "5b2c8ac7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rename_columns(df):\n",
    "    \"\"\" \"\"\"\n",
    "    df = pd.DataFrame(df, index=metadata.index)\n",
    "    df.columns = [f\"dim{i}\" for i in range(df.shape[1])]\n",
    "    return df\n",
    "\n",
    "N_COMPONENTS = 3\n",
    "\n",
    "vis_pipeline = Pipeline([\n",
    "    ('pca', PCA()),\n",
    "    ('subset_pca', FunctionTransformer(lambda x: x.iloc[:, :N_COMPONENTS] if isinstance(x, pd.DataFrame) else x[:, ][:, :N_COMPONENTS])),\n",
    "    ('rename_cols', FunctionTransformer(rename_columns))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "74dffc9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 0 ns, sys: 29.8 ms, total: 29.8 ms\n",
      "Wall time: 6.08 ms\n"
     ]
    }
   ],
   "source": [
    "%time projected = vis_pipeline.fit_transform(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "2042e384",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 53.8 s, sys: 198 ms, total: 54 s\n",
      "Wall time: 54.1 s\n"
     ]
    }
   ],
   "source": [
    "%time raw_vis_frame = TSNE(perplexity=100, random_state=SEED).fit_transform(projected)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "dfdf3a50-4205-4ec3-9168-17d817b8c494",
   "metadata": {},
   "outputs": [],
   "source": [
    "vis_frame = rename_columns(raw_vis_frame).join(metadata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "8cf920b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = None\n",
    "for label, frame in vis_frame.groupby('label'):\n",
    "    scatter_kwargs = dict(x='dim0', y='dim1', label=label, c=frame['label_color'], alpha=.5)\n",
    "    if ax is not None:\n",
    "        scatter_kwargs.update({'ax': ax})\n",
    "    ax = frame.plot.scatter(**scatter_kwargs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "9ea66df7",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = ax.get_figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "af7b4eca",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.tight_layout()\n",
    "fig.subplots_adjust(right=0.96)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "54640284",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d46e2267",
   "metadata": {},
   "source": [
    "for _f in ['svg', 'pdf', 'png']:\n",
    "    fig.savefig(f\"multilevel_random_network_tsne.{_f}\")\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}