020.mouse_liver_development.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n@文件        :020.mouse_liver_development.ipynb\\n@说明        :used to analyse the mouse liver development data\\n@时间        :2023/08/20 10:37:35\\n@作者        :Wu Baosheng\\n@版本        :1.0\\n'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#!/usr/bin/env python\n",
    "# -*- encoding: utf-8 -*-\n",
    "'''\n",
    "@文件        :020.mouse_liver_development.ipynb\n",
    "@说明        :used to analyse the mouse liver development data\n",
    "@时间        :2023/08/20 10:37:35\n",
    "@作者        :Wu Baosheng\n",
    "@版本        :1.0\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys, re, anndata\n",
    "import scanpy as sc\n",
    "import scanpy.external as sce\n",
    "from numpy.random import seed\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from anndata import read_mtx\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "import plotly.io as pio\n",
    "mpl.rcParams['pdf.fonttype'] = 42\n",
    "mpl.rcParams['ps.fonttype'] = 42\n",
    "def read_geneExpress(file_path=\"\", sep=\"\\t\", header=0, index_col=0, min_genes=200, max_genes=2500, min_cells=3):\n",
    "    df = pd.read_csv(file_path, sep=sep, header=header, index_col=index_col).T\n",
    "    cellinfo = pd.DataFrame(df.index, index=df.index, columns=['sample_index'])\n",
    "    geneinfo = pd.DataFrame(df.columns, index=df.columns, columns=['genes_index'])\n",
    "    adata = sc.AnnData(df, obs=cellinfo, var=geneinfo)\n",
    "    adata.var_names = [i.upper() for i in list(adata.var_names)]\n",
    "    #    sc.pp.filter_cells(adata, min_genes=min_genes)\n",
    "    #    sc.pp.filter_cells(adata, max_genes=max_genes)\n",
    "    #    sc.pp.filter_genes(adata, min_cells=min_cells)\n",
    "    #    sc.pp.normalize_total(adata, inplace=True)\n",
    "    #    sc.pp.log1p(adata)\n",
    "    return adata\n",
    "os.chdir('H:/002.singcell/01.liver/10.mutileSpecies/05.SAMap/014.mouse_liver_development')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\wubaosheng\\AppData\\Local\\Temp\\ipykernel_6636\\1595173753.py:21: FutureWarning: X.dtype being converted to np.float32 from int64. In the next version of anndata (0.9) conversion will not be automatic. Pass dtype explicitly to avoid this warning. Pass `AnnData(X, dtype=X.dtype, ...)` to get the future behavour.\n",
      "  adata = sc.AnnData(df, obs=cellinfo, var=geneinfo)\n"
     ]
    }
   ],
   "source": [
    "adata = read_geneExpress('./GSE136689_Counts_Matrix_AllCells_E8.5_E9.0_E9.5.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "annotation = pd.read_csv('./GSE136689_MetaFile_AllCells_E8.5_E9.0_E9.5_Final.txt',sep='\\t',index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.obs['stage'] = annotation['Stages'].astype('category')\n",
    "adata.obs['cell_type'] = annotation['LineageAnnotations'].astype('category')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.layers[\"counts\"] = adata.X.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.obs['n_counts'] = adata.X.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pp.normalize_total(adata,target_sum=1e4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.layers[\"CPM\"]=adata.X.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.var['genes_index'] = adata.var['genes_index'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.write('./mouse_liver_development.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pp.log1p(adata)\n",
    "adata.raw = adata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pp.highly_variable_genes(adata, flavor='seurat', n_top_genes=2000, inplace=True,batch_key=\"stage\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pp.scale(adata)\n",
    "sc.pp.pca(adata, n_comps=50, svd_solver='arpack',use_highly_variable=True)\n",
    "sc.pl.pca_variance_ratio(adata, log=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-08-20 11:58:53,076 - harmonypy - INFO - Iteration 1 of 20\n",
      "2023-08-20 11:59:02,570 - harmonypy - INFO - Iteration 2 of 20\n",
      "2023-08-20 11:59:12,275 - harmonypy - INFO - Iteration 3 of 20\n",
      "2023-08-20 11:59:22,392 - harmonypy - INFO - Iteration 4 of 20\n",
      "2023-08-20 11:59:32,062 - harmonypy - INFO - Iteration 5 of 20\n",
      "2023-08-20 11:59:40,214 - harmonypy - INFO - Iteration 6 of 20\n",
      "2023-08-20 11:59:49,583 - harmonypy - INFO - Iteration 7 of 20\n",
      "2023-08-20 11:59:59,118 - harmonypy - INFO - Iteration 8 of 20\n",
      "2023-08-20 12:00:06,121 - harmonypy - INFO - Iteration 9 of 20\n",
      "2023-08-20 12:00:13,652 - harmonypy - INFO - Iteration 10 of 20\n",
      "2023-08-20 12:00:19,788 - harmonypy - INFO - Iteration 11 of 20\n",
      "2023-08-20 12:00:25,965 - harmonypy - INFO - Iteration 12 of 20\n",
      "2023-08-20 12:00:31,667 - harmonypy - INFO - Iteration 13 of 20\n",
      "2023-08-20 12:00:37,535 - harmonypy - INFO - Iteration 14 of 20\n",
      "2023-08-20 12:00:43,248 - harmonypy - INFO - Iteration 15 of 20\n",
      "2023-08-20 12:00:49,485 - harmonypy - INFO - Iteration 16 of 20\n",
      "2023-08-20 12:00:55,445 - harmonypy - INFO - Converged after 16 iterations\n"
     ]
    }
   ],
   "source": [
    "sce.pp.harmony_integrate(adata, key=\"stage\",max_iter_harmony=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pp.neighbors(adata,n_neighbors=15, n_pcs=40,use_rep=\"X_pca_harmony\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.tl.umap(adata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 989.28x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.umap(adata, color=['stage','cell_type'], title=[\"stage\",'cell_type'], palette=sc.pl.palettes.default_20, show=True, alpha=1,size=20,add_outline=True,outline_color=[\"white\",\"white\"],frameon=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mouse = pd.read_csv('H:/002.singcell/01.liver/10.mutileSpecies/05.SAMap/13.homoGenes/mouse.homoGenes.txt',sep='\\t',header=0,index_col=0)\n",
    "mouse.fillna('no value', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "mouse_wgd = []\n",
    "for index, row in mouse.iterrows():\n",
    "    if row['lancelet']== 'no value':\n",
    "        line = row['mouse'].split(',')\n",
    "        line2 = [x.strip() for x in line]\n",
    "        mouse_wgd = mouse_wgd + line2\n",
    "    else:\n",
    "        tmp =len(row['mouse'].split(','))-len(row['lancelet'].split(','))\n",
    "        line = row['mouse'].split(',')\n",
    "        line2 = [x.strip() for x in line]\n",
    "        mouse_wgd = mouse_wgd + line2[tmp: ]\n",
    "\n",
    "\n",
    "    # line = i.split(',')\n",
    "    # line2 = [x.strip() for x in line]\n",
    "    # human_wgd = human_wgd + line2[0: ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5465"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mouse_wgd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_R1=pd.read_csv('H:/002.singcell/01.liver/10.mutileSpecies/05.SAMap/13.homoGenes/mouse_2R.txt',sep='\\t',header=0,index_col=0)\n",
    "df_R1.fillna('no value', inplace=True)\n",
    "R2 = []\n",
    "for index, row in df_R1.iterrows():\n",
    "    if row['lamprey']== 'no value':\n",
    "        line = row['mouse'].split(',')\n",
    "        line2 = [x.strip() for x in line]\n",
    "        R2 = R2 + line2\n",
    "    else:\n",
    "        tmp =len(row['mouse'].split(','))-len(row['lamprey'].split(','))\n",
    "        line = row['mouse'].split(',')\n",
    "        line2 = [x.strip() for x in line]\n",
    "        R2 = R2 + line2[tmp: ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "R1=[x for x in mouse_wgd if x not in R2]\n",
    "R1=[x.upper() for x in R1]\n",
    "R2=[x.upper() for x in R2]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2058"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(R1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Orthogroup</th>\n",
       "      <th>bichir</th>\n",
       "      <th>dogshark</th>\n",
       "      <th>frog</th>\n",
       "      <th>human</th>\n",
       "      <th>lamprey</th>\n",
       "      <th>lancelet</th>\n",
       "      <th>lungfish</th>\n",
       "      <th>mouse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>OG0000052</td>\n",
       "      <td>BICHIR120524912, BICHIR120524913, BICHIR120524...</td>\n",
       "      <td>DOGSHARK010424, DOGSHARK021135, DOGSHARK021136...</td>\n",
       "      <td>FROG001044, FROG001841, FROG033843, FROG033849...</td>\n",
       "      <td>KRT1, KRT2, KRT3, KRT5, KRT6A, KRT6B, KRT6C, K...</td>\n",
       "      <td>QSM008425, QSM020967, QSM020971, QSM020973, QS...</td>\n",
       "      <td>LOC118403903, LOC118403905</td>\n",
       "      <td>GENE15525, GENE18968, GENE19705, GENE19799, GE...</td>\n",
       "      <td>GM5414, GM5478, KRT1, KRT2, KRT4, KRT5, KRT6A,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>OG0000056</td>\n",
       "      <td>BICHIR120519198, BICHIR120519220, BICHIR120519...</td>\n",
       "      <td>DOGSHARK000635, DOGSHARK015355, DOGSHARK015356...</td>\n",
       "      <td>FROG009849, FROG009850, FROG014931, FROG014932...</td>\n",
       "      <td>CRYGA, CRYGB, CRYGC, CRYGD, CRYGS</td>\n",
       "      <td>QSM115387, QSM115465, QSM160329, QSM162033</td>\n",
       "      <td>LOC118412053</td>\n",
       "      <td>GENE10072, GENE10370, GENE10404, GENE15671, GE...</td>\n",
       "      <td>CRYGA, CRYGB, CRYGC, CRYGD, CRYGE, CRYGF, CRYGS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>OG0000086</td>\n",
       "      <td>BICHIR120529716, BICHIR120529726, BICHIR120539...</td>\n",
       "      <td>DOGSHARK008810, DOGSHARK008811, DOGSHARK008812...</td>\n",
       "      <td>FROG004166, FROG033798, FROG033799, FROG033805...</td>\n",
       "      <td>AZGP1, HFE, HLA-A, HLA-B, HLA-C, HLA-E, HLA-F,...</td>\n",
       "      <td>no value</td>\n",
       "      <td>no value</td>\n",
       "      <td>GENE1434, GENE19069, GENE19255, GENE19284, GEN...</td>\n",
       "      <td>AZGP1, GM11127, GM7030, GM8909, H2-D1, H2-K1, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>OG0000088</td>\n",
       "      <td>BICHIR120538155, BICHIR120542357, BICHIR120542...</td>\n",
       "      <td>DOGSHARK006640, DOGSHARK006641, DOGSHARK007269...</td>\n",
       "      <td>F0021, FROG006392, FROG006393, FROG006394, FRO...</td>\n",
       "      <td>CYGB, HBA1, HBA2, HBB, HBD, HBE1, HBG1, HBG2, ...</td>\n",
       "      <td>QSM114803, QSM126551</td>\n",
       "      <td>no value</td>\n",
       "      <td>GENE1132, GENE14234, GENE15680, GENE260, GENE4...</td>\n",
       "      <td>CYGB, HBA-A1, HBA-A2, HBA-X, HBB-BH1, HBB-BH2,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>OG0000099</td>\n",
       "      <td>BICHIR120522245, BICHIR120522246, BICHIR120536...</td>\n",
       "      <td>DOGSHARK005585, DOGSHARK005586, DOGSHARK009579...</td>\n",
       "      <td>FROG004929, FROG004969, FROG033112</td>\n",
       "      <td>IRGC</td>\n",
       "      <td>no value</td>\n",
       "      <td>LOC118424024</td>\n",
       "      <td>GENE17706, GENE17867, GENE18205, GENE18390, GE...</td>\n",
       "      <td>9930111J21RIK1, 9930111J21RIK2, F830016B08RIK,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13945</th>\n",
       "      <td>OG0013945</td>\n",
       "      <td>BICHIR120514971</td>\n",
       "      <td>no value</td>\n",
       "      <td>no value</td>\n",
       "      <td>no value</td>\n",
       "      <td>no value</td>\n",
       "      <td>no value</td>\n",
       "      <td>GENE3060</td>\n",
       "      <td>CRYZL2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13981</th>\n",
       "      <td>OG0013981</td>\n",
       "      <td>no value</td>\n",
       "      <td>DOGSHARK007728</td>\n",
       "      <td>no value</td>\n",
       "      <td>no value</td>\n",
       "      <td>no value</td>\n",
       "      <td>no value</td>\n",
       "      <td>GENE7228</td>\n",
       "      <td>2010106E10RIK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14068</th>\n",
       "      <td>OG0014068</td>\n",
       "      <td>no value</td>\n",
       "      <td>DOGSHARK012426</td>\n",
       "      <td>FROG048930</td>\n",
       "      <td>no value</td>\n",
       "      <td>no value</td>\n",
       "      <td>no value</td>\n",
       "      <td>no value</td>\n",
       "      <td>NDOR1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14082</th>\n",
       "      <td>OG0014082</td>\n",
       "      <td>no value</td>\n",
       "      <td>DOGSHARK001146</td>\n",
       "      <td>no value</td>\n",
       "      <td>no value</td>\n",
       "      <td>no value</td>\n",
       "      <td>no value</td>\n",
       "      <td>GENE14317</td>\n",
       "      <td>ARHGAP26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14098</th>\n",
       "      <td>OG0014098</td>\n",
       "      <td>no value</td>\n",
       "      <td>DOGSHARK006731</td>\n",
       "      <td>FROG021782</td>\n",
       "      <td>no value</td>\n",
       "      <td>no value</td>\n",
       "      <td>no value</td>\n",
       "      <td>no value</td>\n",
       "      <td>GM1113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3049 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Orthogroup                                             bichir  \\\n",
       "52     OG0000052  BICHIR120524912, BICHIR120524913, BICHIR120524...   \n",
       "56     OG0000056  BICHIR120519198, BICHIR120519220, BICHIR120519...   \n",
       "86     OG0000086  BICHIR120529716, BICHIR120529726, BICHIR120539...   \n",
       "88     OG0000088  BICHIR120538155, BICHIR120542357, BICHIR120542...   \n",
       "99     OG0000099  BICHIR120522245, BICHIR120522246, BICHIR120536...   \n",
       "...          ...                                                ...   \n",
       "13945  OG0013945                                    BICHIR120514971   \n",
       "13981  OG0013981                                           no value   \n",
       "14068  OG0014068                                           no value   \n",
       "14082  OG0014082                                           no value   \n",
       "14098  OG0014098                                           no value   \n",
       "\n",
       "                                                dogshark  \\\n",
       "52     DOGSHARK010424, DOGSHARK021135, DOGSHARK021136...   \n",
       "56     DOGSHARK000635, DOGSHARK015355, DOGSHARK015356...   \n",
       "86     DOGSHARK008810, DOGSHARK008811, DOGSHARK008812...   \n",
       "88     DOGSHARK006640, DOGSHARK006641, DOGSHARK007269...   \n",
       "99     DOGSHARK005585, DOGSHARK005586, DOGSHARK009579...   \n",
       "...                                                  ...   \n",
       "13945                                           no value   \n",
       "13981                                     DOGSHARK007728   \n",
       "14068                                     DOGSHARK012426   \n",
       "14082                                     DOGSHARK001146   \n",
       "14098                                     DOGSHARK006731   \n",
       "\n",
       "                                                    frog  \\\n",
       "52     FROG001044, FROG001841, FROG033843, FROG033849...   \n",
       "56     FROG009849, FROG009850, FROG014931, FROG014932...   \n",
       "86     FROG004166, FROG033798, FROG033799, FROG033805...   \n",
       "88     F0021, FROG006392, FROG006393, FROG006394, FRO...   \n",
       "99                    FROG004929, FROG004969, FROG033112   \n",
       "...                                                  ...   \n",
       "13945                                           no value   \n",
       "13981                                           no value   \n",
       "14068                                         FROG048930   \n",
       "14082                                           no value   \n",
       "14098                                         FROG021782   \n",
       "\n",
       "                                                   human  \\\n",
       "52     KRT1, KRT2, KRT3, KRT5, KRT6A, KRT6B, KRT6C, K...   \n",
       "56                     CRYGA, CRYGB, CRYGC, CRYGD, CRYGS   \n",
       "86     AZGP1, HFE, HLA-A, HLA-B, HLA-C, HLA-E, HLA-F,...   \n",
       "88     CYGB, HBA1, HBA2, HBB, HBD, HBE1, HBG1, HBG2, ...   \n",
       "99                                                  IRGC   \n",
       "...                                                  ...   \n",
       "13945                                           no value   \n",
       "13981                                           no value   \n",
       "14068                                           no value   \n",
       "14082                                           no value   \n",
       "14098                                           no value   \n",
       "\n",
       "                                                 lamprey  \\\n",
       "52     QSM008425, QSM020967, QSM020971, QSM020973, QS...   \n",
       "56            QSM115387, QSM115465, QSM160329, QSM162033   \n",
       "86                                              no value   \n",
       "88                                  QSM114803, QSM126551   \n",
       "99                                              no value   \n",
       "...                                                  ...   \n",
       "13945                                           no value   \n",
       "13981                                           no value   \n",
       "14068                                           no value   \n",
       "14082                                           no value   \n",
       "14098                                           no value   \n",
       "\n",
       "                         lancelet  \\\n",
       "52     LOC118403903, LOC118403905   \n",
       "56                   LOC118412053   \n",
       "86                       no value   \n",
       "88                       no value   \n",
       "99                   LOC118424024   \n",
       "...                           ...   \n",
       "13945                    no value   \n",
       "13981                    no value   \n",
       "14068                    no value   \n",
       "14082                    no value   \n",
       "14098                    no value   \n",
       "\n",
       "                                                lungfish  \\\n",
       "52     GENE15525, GENE18968, GENE19705, GENE19799, GE...   \n",
       "56     GENE10072, GENE10370, GENE10404, GENE15671, GE...   \n",
       "86     GENE1434, GENE19069, GENE19255, GENE19284, GEN...   \n",
       "88     GENE1132, GENE14234, GENE15680, GENE260, GENE4...   \n",
       "99     GENE17706, GENE17867, GENE18205, GENE18390, GE...   \n",
       "...                                                  ...   \n",
       "13945                                           GENE3060   \n",
       "13981                                           GENE7228   \n",
       "14068                                           no value   \n",
       "14082                                          GENE14317   \n",
       "14098                                           no value   \n",
       "\n",
       "                                                   mouse  \n",
       "52     GM5414, GM5478, KRT1, KRT2, KRT4, KRT5, KRT6A,...  \n",
       "56       CRYGA, CRYGB, CRYGC, CRYGD, CRYGE, CRYGF, CRYGS  \n",
       "86     AZGP1, GM11127, GM7030, GM8909, H2-D1, H2-K1, ...  \n",
       "88     CYGB, HBA-A1, HBA-A2, HBA-X, HBB-BH1, HBB-BH2,...  \n",
       "99     9930111J21RIK1, 9930111J21RIK2, F830016B08RIK,...  \n",
       "...                                                  ...  \n",
       "13945                                             CRYZL2  \n",
       "13981                                      2010106E10RIK  \n",
       "14068                                              NDOR1  \n",
       "14082                                           ARHGAP26  \n",
       "14098                                             GM1113  \n",
       "\n",
       "[3049 rows x 9 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_R1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(set(mouse_wgd).intersection(set(adata.var_names[adata.var['highly_variable']])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['E8.5', 'Filtered', 'E9.0', 'E9.5Posterior', 'E9.5Anterior']\n",
       "Categories (5, object): ['E8.5', 'Filtered', 'E9.0', 'E9.5Posterior', 'E9.5Anterior']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs['stage'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "e8=adata[adata.obs['stage']=='E8.5']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: adata.X seems to be already log-transformed.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\wubaosheng\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\anndata\\compat\\_overloaded_dict.py:106: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.\n",
      "  self.data[key] = value\n",
      "c:\\Users\\wubaosheng\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\anndata\\_core\\anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "  utils.warn_names_duplicates(\"var\")\n"
     ]
    }
   ],
   "source": [
    "adata.X=adata.layers['counts']\n",
    "sc.pp.normalize_total(adata,target_sum=1e4)\n",
    "sc.pp.log1p(adata)\n",
    "e8=adata[adata.obs['stage']=='E8.5']\n",
    "sc.pp.highly_variable_genes(e8, flavor='seurat', n_top_genes=2000, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "e8 = e8[:, e8.var['highly_variable']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./e8_high_variable_genes.txt','w') as f:\n",
    "    f.write('\\n'.join(e8.var_names))\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "430"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(e8.var_names).intersection(R2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "human_rerto = pd.read_excel('H:/002.singcell/01.liver/10.mutileSpecies/08.WGD/05.newGenes/Retrogenes.xlsx', sheet_name='human', index_col=None, header=0,engine='openpyxl')\n",
    "mouse_denovo = pd.read_csv('H:/002.singcell/01.liver/10.mutileSpecies/08.WGD/05.newGenes/mouse_denovo.txt',header=None,sep='\\t',index_col=None,names=['gene','qsm'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "8\n"
     ]
    }
   ],
   "source": [
    "print(len(set(e8.var_names).intersection(human_rerto['genes'])))\n",
    "print(len(set(e8.var_names).intersection(mouse_denovo['gene'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: adata.X seems to be already log-transformed.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\wubaosheng\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\anndata\\compat\\_overloaded_dict.py:106: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.\n",
      "  self.data[key] = value\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R1+R2: 602\n",
      "R1: 200\n",
      "R2: 402\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\wubaosheng\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\anndata\\_core\\anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "  utils.warn_names_duplicates(\"var\")\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'human_rerto' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mh:\\002.singcell\\01.liver\\10.mutileSpecies\\05.SAMap\\020.mouse_liver_development.ipynb Cell 34\u001b[0m in \u001b[0;36m<cell line: 13>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     <a href='vscode-notebook-cell:/h%3A/002.singcell/01.liver/10.mutileSpecies/05.SAMap/020.mouse_liver_development.ipynb#X45sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mR1: \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mlen\u001b[39m(\u001b[39mset\u001b[39m(e9\u001b[39m.\u001b[39mvar_names)\u001b[39m.\u001b[39mintersection(R1))\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m)\n\u001b[0;32m     <a href='vscode-notebook-cell:/h%3A/002.singcell/01.liver/10.mutileSpecies/05.SAMap/020.mouse_liver_development.ipynb#X45sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mR2: \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mlen\u001b[39m(\u001b[39mset\u001b[39m(e9\u001b[39m.\u001b[39mvar_names)\u001b[39m.\u001b[39mintersection(R2))\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m)\n\u001b[1;32m---> <a href='vscode-notebook-cell:/h%3A/002.singcell/01.liver/10.mutileSpecies/05.SAMap/020.mouse_liver_development.ipynb#X45sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mlen\u001b[39m(\u001b[39mset\u001b[39m(e9\u001b[39m.\u001b[39mvar_names)\u001b[39m.\u001b[39mintersection(human_rerto[\u001b[39m'\u001b[39m\u001b[39mgenes\u001b[39m\u001b[39m'\u001b[39m])))\n\u001b[0;32m     <a href='vscode-notebook-cell:/h%3A/002.singcell/01.liver/10.mutileSpecies/05.SAMap/020.mouse_liver_development.ipynb#X45sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mlen\u001b[39m(\u001b[39mset\u001b[39m(e9\u001b[39m.\u001b[39mvar_names)\u001b[39m.\u001b[39mintersection(mouse_denovo[\u001b[39m'\u001b[39m\u001b[39mgene\u001b[39m\u001b[39m'\u001b[39m])))\n",
      "\u001b[1;31mNameError\u001b[0m: name 'human_rerto' is not defined"
     ]
    }
   ],
   "source": [
    "adata.X=adata.layers['counts']\n",
    "sc.pp.normalize_total(adata,target_sum=1e4)\n",
    "sc.pp.log1p(adata)\n",
    "e9=adata[adata.obs['stage']=='E9.0']\n",
    "sc.pp.highly_variable_genes(e9, flavor='seurat', n_top_genes=2000, inplace=True)\n",
    "e9 = e9[:, e9.var['highly_variable']]\n",
    "with open('./e9_high_variable_genes.txt','w') as f:\n",
    "    f.write('\\n'.join(e9.var_names))\n",
    "f.close()\n",
    "print(f'R1+R2: {len(set(e9.var_names).intersection(R2+R1))}')\n",
    "print(f'R1: {len(set(e9.var_names).intersection(R1))}')\n",
    "print(f'R2: {len(set(e9.var_names).intersection(R2))}')\n",
    "print(len(set(e9.var_names).intersection(human_rerto['genes'])))\n",
    "print(len(set(e9.var_names).intersection(mouse_denovo['gene'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: adata.X seems to be already log-transformed.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\wubaosheng\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\anndata\\compat\\_overloaded_dict.py:106: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.\n",
      "  self.data[key] = value\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R1+R2: 620\n",
      "R1: 211\n",
      "R2: 409\n",
      "3\n",
      "3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\wubaosheng\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\anndata\\_core\\anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "  utils.warn_names_duplicates(\"var\")\n"
     ]
    }
   ],
   "source": [
    "adata.X=adata.layers['counts']\n",
    "sc.pp.normalize_total(adata,target_sum=1e4)\n",
    "sc.pp.log1p(adata)\n",
    "e95_p=adata[adata.obs['stage']=='E9.5Posterior']\n",
    "sc.pp.highly_variable_genes(e95_p, flavor='seurat', n_top_genes=2000, inplace=True)\n",
    "e95_p = e95_p[:, e95_p.var['highly_variable']]\n",
    "with open('./e95_p_high_variable_genes.txt','w') as f:\n",
    "    f.write('\\n'.join(e95_p.var_names))\n",
    "f.close()\n",
    "print(f'R1+R2: {len(set(e95_p.var_names).intersection(R2+R1))}')\n",
    "print(f'R1: {len(set(e95_p.var_names).intersection(R1))}')\n",
    "print(f'R2: {len(set(e95_p.var_names).intersection(R2))}')\n",
    "print(len(set(e95_p.var_names).intersection(human_rerto['genes'])))\n",
    "print(len(set(e95_p.var_names).intersection(mouse_denovo['gene'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: adata.X seems to be already log-transformed.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\wubaosheng\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\anndata\\compat\\_overloaded_dict.py:106: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.\n",
      "  self.data[key] = value\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R1+R2: 553\n",
      "R1: 189\n",
      "R2: 364\n",
      "2\n",
      "4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\wubaosheng\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\anndata\\_core\\anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "  utils.warn_names_duplicates(\"var\")\n"
     ]
    }
   ],
   "source": [
    "adata.X=adata.layers['counts']\n",
    "sc.pp.normalize_total(adata,target_sum=1e4)\n",
    "sc.pp.log1p(adata)\n",
    "e95_a=adata[adata.obs['stage']=='E9.5Anterior']\n",
    "sc.pp.highly_variable_genes(e95_a, flavor='seurat', n_top_genes=2000, inplace=True)\n",
    "e95_a = e95_a[:, e95_a.var['highly_variable']]\n",
    "with open('./e95_a_high_variable_genes.txt','w') as f:\n",
    "    f.write('\\n'.join(e95_a.var_names))\n",
    "f.close()\n",
    "print(f'R1+R2: {len(set(e95_a.var_names).intersection(R2+R1))}')\n",
    "print(f'R1: {len(set(e95_a.var_names).intersection(R1))}')\n",
    "print(f'R2: {len(set(e95_a.var_names).intersection(R2))}')\n",
    "print(len(set(e95_a.var_names).intersection(human_rerto['genes'])))\n",
    "print(len(set(e95_a.var_names).intersection(mouse_denovo['gene'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['FLT4']"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[x for x in e8.var_names if 'FLT4' in x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.var_names_make_unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata=adata[~(adata.obs['stage']=='Filtered')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 161.28x298.8 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.dotplot(adata,['KDR','FLT4'],groupby='cell_type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\wubaosheng\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\anndata\\compat\\_overloaded_dict.py:106: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.\n",
      "  self.data[key] = value\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1076.26x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.violin(e8,['KDR','FLT4'],groupby='cell_type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\wubaosheng\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\anndata\\compat\\_overloaded_dict.py:106: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.\n",
      "  self.data[key] = value\n"
     ]
    },
    {
     "data": {
      "image/png": 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++27atm3Lo48+Stu2bcnLy8dqtbJ27VqvDy00WaNz8cUXc9tttwHGG+w7bnSk679mfE6c8awwDpMnTSbAL9iT1LMmvp3/EQUluQzseSVZ+YcpKS8ip+AoK7bNY1/Gdgb3vqbWOtrH98HPJ4DJk6c0sHrF2ZKfn8/777/PsWPHGDFihGe7lJLKykoOHDigozrw9fVl5MiRvPXWW/j7+xMZGUlqaiqjRo2qlpPNGzTZjAQmkwl/f3+9ZdSI27i4jU24y/gYIQWO4mR2797N+g3r6d58ED5mv1OWu7TPNSAFE2e/zIDuV5J2ZCdtmnVm5KDRLFo/m+iw2FN+FsDPJ4C2cb1ZsWI5hw4dIjk5uaG/iuIMiY2N5Y033qixCzwkJISbb75ZB1XVycnJ4bvvvqOiooLS0lKaNWuGlJLffvuNXbt28eijj3rlmdhkPZ2qGM3Tyc3NRTqdhLlCpsP9ladzplgsFk/6j8ZixowZ+Jr9aB3bs9ZysZFJWJ1WzGYfisvyaN2sCw7pYMnGX9mXvo1V2+fXWVfbuF6YTT7MnDmzgdQrGoqYmBjPujQOh4MdO3ZQUVHB9u3befvtt3XVZrPZGDlyJKNHj+aDDz6gR4/uJCYmMmvWLIqLizl69KjXGrXK6GC8QILVq1eTm5WJ2fXrhPpKzALy8vL0FXYO4ufnxyWXXNJoxy8pKWHx4sUkR3fBz6f2VuLPS6cwZ+U39Gk/gJzCYxw4sovDWXspLM4FYSIqPL7O+gJ8g2gR1ZF58+ZTUVHRUF9D0cCYzWZCQ0Ox2Wx0796dl19+WVc9QgheeuklfvjhB/z8/Ni//4BnrZ/Q0FDuu+8+EhISvKKlSRsdi8VCbm6u4Tyd6OhozuvQzPPeJCA8QBjW6Bjt/FXFZDLRo0ePRjv+woULsdvttIqpO9y0S+u+xIY3IygolBEX3U5Wfib+PgEcPLaXts27sGTjL/Wqs1VMNyyWCsOkzVdUZ+nSpTidTlq2bElYWJjecgDw8fHh1ltvJSMjgwceeABfX1+sViubN29m0KBBdOzY0WtamrTRcTgcOBwOw3k6OdnZRPhVT8QX6WcnJydHJ0W1Y7Tz502WLV1GWGAUkcF1eynpWfs5lneQ5Vv+5OOfXqTcUozdYeei7peRnNCOAd2H16vOmJBmBPmHKaNjMEpLSykpKWHWrFk4nU6OHTtmmN8oPz+f8PBwXnnlFd5++20KCwsxmUwsXryY/fv3e1VLkzY6wcHBxMfX/bDwNrm52US6Fm/LLLTy/G9HiPRzkJN1TGdliqpUVFSwafMmEsJa16t8fFQLwkJjqLCU0afjIAJ8g7igyyVk5hxk9rKpniXK60IIQWJYK9atXYfNZjubr6BoQObOncsvv/xCUVERFouFoKAgfHyMEasVGRnJzTffzMMPP0xKSgq+vr74+vpy/fXX07t3b69qadJGp2q3UFlZGePGjdNRjUZlZSVFxaVEuYIHmkX4MW5IHFEBTnJyc3VWd27icDgaZSB3z5492O124sJa1qt8RGg0Qf7BDOt3EzsPbiAxpiW707eSV5yDv48/2w6sqXfdcWEtsVRadA/FVRynVatWDBs2jEmTJhEcHExgYCA5OTns3r1bb2kIIYiNjcVmsxEZGYmUkj179hAZGQnAyJEjvdaTYgwzrDNSSoKDg7nrrrv0lkKuy7BEBxxv9UYF+xDl76S8wkJZWZkh0mmcSzRWmiP3wyQquH4DsB2Te1FZWcGiDbMJDYqgf9fLmbPqGxwOO+WVpbRv2b3edUe66ty9ezcdOnQ4ffGKBmfXrl0EBwcTHR2NlBJfX1+Sk5NZsmSJIX6j6dOnExAQgNVqpXPnzggh+O233zCZTLz99tvExtYest9QNGlP50S8lXuoNrKzswGIchmdSruT26cd9Lx371fUHyEE119/fYMf9+jRo/j6+BPgW79GQKWtgs37VpIU3ZLyilKO5B7kwu7DqLRXcmmf64mNSKx33UF+YZiEiWPHVJerUbj11ls93Z3ucc42bdpw0UUX6SnLQ2pqKnfeeScZGRmAFkjldDoJDAz0akJhZXTQTv6hQ4f0lgHgWXQs2tW9tjmjgg//0ZwYl9HRY1GyutBjIajTZf/+/Q2+AmdeXh6BfiH1Ln8k9xBOp5PC0nyc0gkC9hzaTJB/CJv3rSQz52C9j2USJgL9QzyesUJ//vzzTyZOnEhZWRlFRUXs2bOHwMBAunbtqrc0ioqK+Pbbb4mKimLo0KE4HA7S09OxWCy0a9dORa95m4KCAlasWFF3QS/gMTouI7PzmAWnPP7eCJ7Oxo0befjhh/WWcVrY7fYGH3S3Wq2YRf17qOev/YFySylhIZG0bd4ZS2U5vToMoGVCW8otpfTrOvS06jcLH12WG1bUzEcffUT//v0JDg7Gx8eH5ORkZs6ciZSSyZMn69pAmD9/Pjt37uTZZ5/l008/BSAgIIDLL7+M+++/37PonDdoskbnvffe83g3SUlJ3HLLLTor0sjKyiLcX+Bn1t7f1S+ayCAfIv0lJoEhulN69+7Nhx9+6Hlf36grPenQoQNDhgxphCPXP1z8/93xIf5+gZSUFSKdTg4e3U1JWQGHj+3jwm5XsD71r9OsWTbpcHWjMWvWLIYP18Lef//9d/bs2cP69espKCigWbNmBATUnAjWGxQXF7NhwwZWrFjB/fffj5+fHwkJCWRnZzNt2jSWLVvmNS1NNpDgsssu86SVMdLkxmPHjhHtf3Lr1SQgKtAYRudEzgWj0xiEh4djdVjqXX7Oim+wOqwkRDbj4LE93HrFYyzaMBshBJ1TerN4U/0mh7qx2i2GmXzY1JFSMnbsWBITE3nuuecYMWIEpaWl9OnTh6ioKC6//HJd9d1xxx3ExsbSq1cvnnjiCU/uyU2bNhEdHUNlZSXFxcVeuZ6arKfTuXNnT8vDSK3FrKNHiAmoeYwkxs9OlgHn6jRG19W5QExMDBWVpTic9RvT2pG2gdKyQkrKCqm0Wfh67ru0TuqI3WEjvySbmy59oN512xxWKm0VREdHn6l8RQMihODSSy/lueeeA7SGmNlsZsGCBTor0zCbzXzzzTcsWLCAV155hUOHDlFWVkbLlsn8+9//xmazsXHjRq9oabJGx4hIKcnKzvEEDVRlwh9HsVUUk3X0qA7KamfOnDlMnDhRbxleJyUlBad0UmKpXyLWsJBIbHYrCIHNbuWSXteyYN0szMKHBet+4kDmrnrXXVyhjQ+4E0wq9Oe6667zvF64cCEWi4V77rlHR0XVmTJlCkeOHOGGG27A4XCwf/8+SkqK+eKLL5gxYwaDBw/2io5GMzpCiElCiGwhxPZT7B8shCgSQmx2/Y1vLC21YbVasVjq30XSmBQWFmK12Wo0OhszyumaGEBuXr7hBo+vvPJKHnroIb1lnJKNGzcyYcKEBj9u+/btAcgrO1Kv8kP7Xkfb5l1Iz9rPRd2HsfPQBoICgunRvj+DelzJ0bz6L0WdV3qkmgaFsajak7Jnzx6eeOIJnRXBxx9/zIcffkhSUhIOh4Po6Eg6duxAWFgYgwcP5q+/Tm9M8UxpzDGdKcCHwLRayiyTUl7diBrqpKioyDBhyO7xmujAk43O7HvbsCRT4pSSnJwcz9rmRsBI3ZMncuzYMaxWa6MYxZYtWxIVGUV20SHaxNadVPSLX16nvLIUEBQW5+KUknYtutG5VW+O5Bw8rZDprOLDNEtqZsg0Tk2ZL7/8kpycHCIjIzGZTCxYsIDMzEx69uyptzTy8vL497//zSWXXELfvn0JDPRn/foN+PkF0L17d69pbDRPR0q5FDD8AjCxsbGGWQzLbfxq8nRyS+3M23qkWjmjYORAgvDwcDp16tQoYx9CCPr170dWyUEczrq9z9uHP85F3a6gVWJHcgqPcDTvEOnH9vPNn++zZe8qWiXVb9a6zWElu+QQ/fr3O9uvoGggysrKmD59Ol26dKF58+bs3LmT9evXY7PZePbZZ7nzzjt11fe///2PjRs34uPjg4+PD1FRUcTGReHn58u3335LREQEVqvVK1r0HtPpL4TYIoT4QwjRxduVu6PWjNJSX7RoERkZGTUanYhAM1d0DAX0n6vz5ZdfegyNlNKrs5lPl8DAQMLDwxvt+EOGDMFqr+RYUVqdZX9f+Q1pR3fz8A0TaJXUkUt6X4vdqaXAOZafSXpW/fKoHSnch8Npb6QQcMWZYjKZSEpKYt68eURHR9O1a1eCg4OJiorSWxpPPPEEt9xyC06nk5kzZ5KcnEx0VLinC/CTTz5pEmlwNgLJUsoewAfA7FMVFELcJ4RYL4RY3xhJ6YwSMh0dHU1y80SCfE42gj5mQb+W2iJhens6ZrPZ87qkpISpU6fqqEZf+vTpQ2RkJAdyttZZ9t+3vsO1g+7Ez9efA0dTWbltPt3a9CM8OIqE6OaYTfW7HdNythIfn2CIme4KDSklcXFxtGzZki+++MLT0Fm6dCkvvPCCzuo0rrnmGq688kr69OlDZmYmlspyCguLePbZZ9mzZ4/XdOhmdKSUxVLKUtfrOYCvECLmFGUnSin7Sin7NoY1Noqnk5WVRYS/pCYbaLU7Scu1EOwndF9X584778TkekCGhYUZOoigsfHx8eHqq6/maFEaZZW1e3xb963GaqsE4JI+19A+uTsrt/1BYkxL8otz8TH51llfcUUe2SXpjBw5wvMbKPSnvLycgwcPAtpqtQ6Hg4CAAB544AFefPFF/vOf/+iub+LEiUybNo0HH3wQk8lEWGggeXn59OrVy6tRkLpdtUKIBOFyMYQQ57u0eHVpTLexMYqns3XrVnKzag6JzitzsHhPCVH+Tt2NzokYbUxn586dzJ8/32v1jRw5ErPZxN6sDbWWyy/JYe3OxQBkZqdhsZQxuNdIBva8mkv7Xkdhed1DoHuy1uPr68vVV+saf6M4gbi4OO6++27sdjt2u52PP/4Y0PKxPf/888ybN0/XKNkNGzYwffp05syZg8lkwmazERMTTo8e3UhOTsbXt+4GT0PRmCHT3wKrgA5CiAwhxD1CiAeEEO4ZcDcA24UQW4D3gZulUVwOnQgM8GNgx7ga9yWG+/LgwFgi/Ozk5hrL6BiNwMBApJReGxiNj49nyJAhpOVuo9JeccpyrRI7UF5ZyqINP5NdcJQhfUZy1UW3UmEp5eDRVC47b1St9VTYSjmUt5Nhw4Z51kFRGIshQ4ZgMpno378/AMnJyYSFhbF48WJd0+AIIZgwYQJLly7lu+++o127dphMUFpahtlspqSkxGtaGjN67Z9SykQppa+UsrmU8ksp5adSyk9d+z+UUnaRUvaQUvaTUq5sLC2nwkiBBFJK8vMLCfc/tZaHZhwm0s9JnjI6teLuKvBmgMOtt96KzWFlb9apZ3XHRiaRX5TNsi1zuPmyB2mV1JHlW+eybtdiMrMPsHXf6lrr2HNsPU7p4J///GdDy1c0APv372fGjBmYTCbsdjvbt2+nQ4cOPPfccwQFBemqLTs7m1WrVvHiiy+yefNmnE4nR4/msn//frKzs3nooYc46qWJ56pTGGN0r5WWlmKz2wn3q95Vdfc3x5dcePbyBML9nRQUFhnCULpxa/nqq6/Iy/NqD+kpufzyy70WjQPudVMGsC97I1Z7zd0ox3LTqbRbGN7vZl6Z8hB/rv6eLXtXklechTCZCQ449TIJFls5+3O2cOmll9K8efPG+hqKM8RqtTJ//nzPJNCNGzcSGBhIVlYW3bt3170L+tprr6W0tJSxY8fyyy+/sH37drKyCgkLCyc8PJxp06Z5be6fMjoYw9MpLCwEINzvuBar3cnrI5M875tH+BHmK3E4nJSWlnpb4kkUFBRUS0AaExPj1b5ho3HXXXditVtOObbz45LPaZPYER+zL306XozT6eS+a56jQ8ueNItNISLs1EZy97G1OJw27rjjjsaSrzgLdu3axd69ez1Gp3fv3pjNZubOnUvr1q11v183b97MxIkTWb58OSEhIURERJCfX0J+fj6hoaFe1dekjY6RAgncRie0iqfzzfoCNmZUHyMIdRkld3k9cV+8FouFoqIihg8f3qSzHrdv355BgwaxN3tDjWM7d494msiwOCoqyxncewQVlWUUluZy3cV306VVX9o1rzkEusJWyv6czQwdOtQwE5kV1YmKisLHx4d//OMfrF27lg4dOtCiRQsuvvhirrzySk9Ge72QUvLSSy9x3333UVBQQFRUFH7+vnTt2pUVK1ZwySWXsGtX/XP/nQ1N2uiAtmqoO9RRT4qLiwEI8dWMisXm5IKUIIZ10h7iNofkr30lhPhqRsmbA3+nwtfXl4CAANasWcOMGTP0lmMI7rrrLmwOK7uPrjtpX0RINGazD8Vlecz6axKJMS2Ji2zG298+xertC5i35ocaj5l6ZA1O6eCuu+5qbPmKM6RFixa88cYbbNy4kd69e7Njxw7MZjMVFRV8++23unc7L1++nObNmzN16lSSk5M5cOAApSXlbN68mSeeeIKrrrqKli1bekVLkzc6Qohqkx31wu3eBvlIii0O3vsrm5UHSqmwakbG5pDsy6kk2DVx1AhGx83gwYO599579ZbB8uXL+fHHH3XV0KZNG4YMGcK+nE1YbGXV9vmYfemU0gspJeHBkfTtOJhj+Rl0bNmTMaNe5KahD550vHJrCftztzB8+HA1lmNwHnroISoqKnj33XdZunQpoHlAGRkZ+Pv766rtscceY+7cuUgpCQ0NxWazkZQURXh4GI8//jgvvPACwcHBXtHSJI3O/PnzcTgcCCHw9/c3xM1cVqY9oAJ9JIG+JlpG+rEirZz7vz9MXpmdID8T9/SPIdBldPTsIy4pKeHLL7/Urf5T0b59e3r16kVeXh5Hjmh56qxWKw5H/da7aSjuuusuHE4bqUfXnrSvdVJn4qKaExOZREl5AbP+msTSLb/zzbwPWLLx5EXcdh1ZjRCC0aNHe0O64ix4/vnn8fX15fvvv2fw4MHYbDbmz5/P6NGjdV/eXUpJdnY2hw4doqSkBCEEBQXl9OzZk1deecWrWpqk0dm0aZPX5nDUl4oKbQxg+d5CvliZwz/7RDFuSBxFFQ6ig48nA/d3OWV6TjTz8fGpFhlmhDExgODgYFq1asWmTZtYvnw5AEeOHGH//v1e1ZGcnMzQoUM5kLvlJG/Hz9efPYe34HDYmPXXl/RuP4D+3a7gvI4XM7j3yGplyyuLScvbxlVXXWmorOKKmklMTCQ6Oprp06cTExPDtm3buOqqqxg1ahRt27bVVVtZWRlZWVkkJCRw5MgR/Pz8KCkpZ/fuPV5faK5JGp1///vfBAYGet4bYb5OZaWWHmVfTiV2p6ajfZw/sSE+LNt/3KvxN7vGfHQ0OoGBgYwcefwBaRSj89JLL7Fp0yaGDh3KjTfeCGgLremx5swdd9yBw2ln97H1nm02u43Ckjyax7Xh4l4jGNx7JCGB4ZSU5tMxuedJx0g9thYhBLfffrsXlSvOhNTUVO68805efvll4uPjiYiIICwsjN27d3P77bcTHR2t6zpYISEhJCUlMX/+fPLy8mjdujVXDOtJQEAAe/fu9aqWJmd0pJRs2LCBLVu2VNsGMHr0aPbt26eLLqvViq8J9mRbiArSPJs3F2bh7yMY2Eabv1FYbsfHpGnVeyG3P/74w+NBGCUH2P/93//Ru3dvvWUA2lo7Q4YM4UDOZs+8nT9Wf8uG3UuJCoslMyeN2Usn8+uKabRp3hWrvZIfF3/h+bzFVkZa7jaGDbtCrZlzDtCxY0emTJnC3r17OXLkCDt27MDhcFBSUsLixYsJDAzUPZigVatWjBs3jg8++ICsrCykU9C6dSt8fHx47LHHvJa93hhPCy+yYsUKPv74YywWy0kh01999ZVubrDdbsdkEnRODGBohzCe/SWT9IJK7rggig3p5aw+WMr4OUeRLi/IZrPpotPNuRa+q8fkvNtuuw2bw8q+7M0AlJYVciBzFx1a9qRDyx60TuqMn28APdv1x8fkQ1ZBhueze7I24JQObrnlFq/rVpw5SUlJFBcXExkZSUBAAO3atSM8PJwXX3xR98bD66+/zpQpU5gyZQp2u53CwjLsdjs2m40bbrjBa6mVmpzRGTBgAF9++SUXXHCBISaFunE6nZgEZBXbCfA18djgOJ64JIHezYMpq3QwfX0BYQFm8spsnvJ64uvri4+P5pEZpXvtVKxevZqnnnrK6/W2bduW8847j/05m3A4HSQnduDWKx7F18cXm91K+5bdufnSBwkJCsfHx48x178IgN1hJS13CwMGDKBFixZe1604M3Jycnj22WeJiooiODjY8xCfPXs2O3bs0FWb3W7H4XDw7bffsnXrVgICAoiKCiM/P5+ff/6Z8PBwr93HTc7oVOVET+fQoUO1FfcK9/SPJjzQTEKYL+3jAth2tIJtRyy8f0MLXr46iaRwP8D4D3o92bdvH1JKbDYb+/fvp1+/fvzvf//TRctNN91EhbWUjPxULup+BUGuVDcf/PA8nVv1IT66BQ5H9a7SQ3k7qbRZuOmmm/SQfE5gs9m47rrrdF9bqipff/01O3bsYMaMGWzduhWHw4GUknXr1um+kNvq1at5+umn+fzzzxk9ejQtWrRg48b9lJWV0qpVK6ZNm0Zubq5XtDQ5ozNmzBgGDRpEQUGBZ5vb+Dz//PO6dVuZTCakhOQof/7aV8LMTZq+romBRAWZKbFoYb+u3jVldGrhww8/JD8/H19fX5KSkur+QCNy3nnn0bxZc/bnbiHtSCo5BUdwOB1EhcZhEiY271nB0i1zmP3XZEC7Fg/kbqFNm7Z069ZNV+1GZsuWLaSlpfHOO+/oLcXD448/TnR0NDt27KBDhw7s2bMHk8nE9ddfr/vqugMGDGD9+vVMnz6dTZs24XQ6ade+OXl5BZ5pBQkJCV7R0uSMzkcffcSSJUtq7L/86quvdMsd5uPj44la69siiKEdtKWpD+VbCfY3MW5WBkeLbNhdRsdIOc6M1E0J8O677xIdHU1+fn61KEU9EEJwzbXXkFuSSUFZDnanHbPJzJ1XPcnBo7tp27wLg3uNICk2BYD8sqMUlGVz7bXXqIZFLVRWVhIWFlYt958R6NChA3369OHAgQP4+vqyd+9efvrpJ5YsWaK3NOLi4li/fj25ubmsWbOG4KAgkpNbkpGRwVtvveU1HU3O6AghToq2MsJD08/PD7tT82SC/c1EBvlwILeSx39Kp2ezIG7qFclf+0qwO7UHkXs8RU/0HleqDafzeFLUw4cP6xaVCHDFFVdgNpsxBzhIjD6eamTaH2+zP3MnEsnRXK1r92DuDvz8/Bg6dKhecs8JvD3hty6WLVvmWbjt0Ucf5fzzz6d79+5ERETQo0cPXnrppWq9K97GbrcTFhZGbm4uSUlJhIWFYbcLDh48xAcffODV89nkjM6J2O12z8RMPXEv8GR1Qn6ZnXumH6J1jD+z721Dyyg/9udZ8TULKh2a0dG7Bb9s2TJ2794NGO8BAFp3pTuX1KFDh7w+QbQqERER9O/Xn8P5u3DK44Y6LDiK1s06YxImwkOiOHBkFxmFqQwcONBrKUnOVZYsWeK1BJX1oU+fPtxwww0UFRVx//33s27dOoQQBAUFsX//frZu3arrwnsFBQXs3buX4OBgysvL6dKlC4WFpYSEhDBixAgmTJjgNS36N5d1pri4mPT0dL1leIzI2kMV7M0q4/ObtQem2aQZmfsvigEgvbR6eb0YOHAgnTp1Ij8/35AeT2VlJbt376ZDhw4MHDhQbzlcOvRSlq9YTl5pJrGhWkTav0Y8w/x1P5FdkElQQAitW7an0mbhsssu01mt8enXrx/Tp083TBdkUFAQEyZMICAgwLOswYoVKwgICOD+++/XPZBACMGoUaPIz88nJCSEPXv2UFRUTlxcDHl5BRw+fJjy8nKvLDbXpD0dKSVRUVF06NBBbymelm2LqED2Zley/ejxjANFFQ4e+1EzjBV2Ua28ETCi0fH39zfUPKL+/fvj6+vHrvT1TPvjbbbsW42/XyAbU5fyrxHPMHLA7WSVHCIoMIi+ffvqLdeQrF+/3pNTz9/fn7i4OEN0jbu55557GD9+vGutmnyio6ORUjJ37lyWL1+uaxaRtWvXsmjRIsLCwujRowcAxcWl2Gw2PvzwQyZNmuS11U2bpNHJz8/ngQce8Lw3QmspNFQLHHAIM6+NTGL94eM5u44UWQn01X6qUpupWnkjYMTuNUDXNelPJCgoiD59+lBizyG38BhJMcmYhAmroxKLtYJFG37mWPF+Luh3AX5+fnrLNSSZmZmedaSMkgWjKnfeeSfLli1j165dJCUlYbPZCA4Opm/fvqxZs0bXzPBCCJo3b86FF17Ik08+idPppKiohISEBM477zyGDRvmScXV2DS57rXVq1djsVh47rnnyMzMBIwRSBAeHg5AiU0ghCAuVItOK7c6+WpdPnf3iwag1CaqldeLgoICj4djVKNjNC68sD+rV6/ituGPEBuhJfB8Y8x0ADqkdGN79hL69++vp0RDc80113heGyGQ5kReffVVevfuzdSpU4mMjCQjI4P4+Hh+++03rrrqKq8un34iw4cPZ/jw4TgcDrKysrDZbCQkJCCEmcTERK8m/TRec6GRiY6OJjY2lhYtWrBv3z7y8vIM4elERERQVlbGb1u1FQav7hpOTqmdsT+m8+qIZuzLreSzFbkUW4WnvJ5MmTLFsxqiMjr1w91tVmzVJuHtTNtAcVkBlTYLmfn7q5VR1I4Rjc7FF19MSEgId999NyaTiYiICGJiYoiMjCQ8PFz3++Shhx5i1KhRTJgwgcjISIQQbN++nUsvvZRPPvnEazqanNFp164dXbp0ASAsLAx/f39DeDqRkZH4+/sTHxXi2RYb4sOnN2kBBVd2Duf+i2Iosprw9/PVPZDgjjvu8LjjRjh/5wLNmjUjJjqGnGJtfM7htCOlE7vdypG8gzRv3oKYmBidVZ4bGNHogNaNlZWVRVZWFpGRkTidTn799VfGjh2rawQlaJ7YU0895Ymiy87OZujQSwkNDfVaNgJogkanKrGxsYSEHH/Il5eX66YlMDCQsNAQ/AKCWLi72LPdZKruhRVWmoiKitLFO3M4HJ4lDUwmk6df3UiBBGvWrOGRRx7xWv/06SCEoEfPHuSVa4Ph3dpcQHhINEEBoUTEhNCjR3edFZ47GNXogDbJfPHixWRlZXH48GFeffVVOnTooMsSG24cDgd2u53Zs2ezefNmAGJiYkhPzyAwMNCrGROalNGRUjJjxoyTtrsf4I899hgHDhzwtiwPMTHRpOVZ2XpEi3L5dkM+v26vfjEUVJqIjYvTQx5ms5nJk7V0LREREZ6FxYqKihgxYoQumk7kggsuoEuXLvz55596S6mRLl26UF5ZQoW1lG/mvs/R3MOUVhZSaavweOCKujGq0RkyZAilpaVce+215OTk4HQ6GTNmDGFhYbrq2rZtG++//z5vvvkm//3vf/H19cVms5Genu5ZfsFbGPOXaySklDWmzXB3D33++efellSNiMho5i07Rp9YG7mldq7vEYHpBI8m3+ZDt1h9jA5oY2Kgrb6akZFBUFAQy5cv56qrrmLChAmMHz9eN21u3JGJeiX5rA13eH5+2TFuveJRAA7naZMcO3bsqJuucw2z2ay3hBpZvHgx06dP54knnqBLly6ewJAHH3xQV109e/akZ8+epKenM2vWLA4cOICPjw8pKSn89NNPrFmzxmtampTRMZlMPPqodqOvWbPmpC6YadOmUVpaypgxY/SQR/PmzUlMbMYjF5URE3LyT+OUkF+h5VDSmxYtWhAeHo7NZmPAgAH07NnTa4tA1Ydly5Zx9OhRQy3zXFRU5JmrUVSRQ7NIbe2mwvIczGYfQ80rMjpGDJm22WysXLmSH3/8kZCQEE/KraefftoQ8+pmzJhBRUUFPXv2JCUlhezsbC655BL8/Py8OoH6jH45IUR7IYS+bsFZUjVu3u3ppKSk6PpAj4+PJ7/MSpGl5jGSIqvA7kT3xaBA6w92zxWKiopiz549/PrrrzqrOk7v3r09XpnT6cTpdDJ37lxdNWVmZrJ9+3YS4hMoqjg+cFtUkUuLFs0NlcTV6Jy4LIneTJ8+nTFjxnD++eczdOhQCgsLyc/Pp7i4mNGjRxsi68n555/PzJkzefvtt6moKKdZs2Z8//33PPDAAwwZMsRr4zq1Gh0hRHchxDwhxHYhxMtCiEQhxI/AImCnVxQ2EsnJyZ6WufvC7dSpE+edd55umhISEoiMiiI6rOaZwbkV2s9lpNY7aF0dF110Ec8884zeUjwMHz6ciooK0tLSAK0VWnWJcj3o3Lkzd911FymtUiitzPdsL7Xm06pVKx2VnXu471mjRE6mpKSwcuVKbDYbcXFxxMfHc+zYMcrLy7n//vsNsRhfcnIyl19+OW+++SY+Pj7s3buXHj160LFjR2677Tav9VTU1b32OfAJsAoYBmwGpgK3Sin1y+nQAHTo0OGkVpKek7cAz3oWORUmEoKOezubMsppGelHjsW3WjmjYKSuDqfTyc6dO3nhhRd44YUXCAsLQ0qJv78///73v/WWB2jdqOvXbUBKiZROSi1FNG/eXG9Z5xRG8XDcXHjhhdx9991UVlYyatQodu7cydy5c0lOTuaKK67QWx4AeXl5fPPNN6xatQqLxUJYWBgpKSmsWLGCmTNnek1HXUbHX0o5xfV6txDiMSmlMe7cs6Rjx46eVONGuYDdC47lVFR/iGcU2ogMNJNToQ2eGs3oGCWSaP369Xz11VdYLBZatWqF0+k0jLaqJCUlYXfY2HVoAy0T2iKlU/fF5hRnT5cuXYiNjaW0tNQznmM2m8nMzGTPnj0MGTJEV33jx49n+fLl+Pr6csUVl3HgwEH27t3L+++/z5IlSxg8eLBXdNTVRA0QQvQSQvQWQvQGKk94f87y73//25PHySguekxMDD5mMzmW6j/LiK7hpET7k11hIioyXPeJoSdilEiivn378t577/HZZ5/xzDPPeAyOURoVbtzdo9sPrONYwWEKCgrw8fHxhKMr6sYo96wbp9PJokWLsNvtjB8/nsmTJ2O1WjGZTOzevZtXX31Vb4k8+OCD+Pr6kpOTQ2BgIImJiVx22WWsWrWK5557DrvdXvdBGoC6moFHgbervD9W5b0ELmkMUd7gnnvuIS8vDzDOBWw2m0mIjyOrvOb1fbIrTCQmNvOyqroxojdhVPLz8z3duBk5B2ib0hGHw0FAQACtW7fWWd25QUZGBm+//XbdBb2IyWQiNDSU1NRUj7bly5ezfv16HnzwQebPn6+zQjzLn7/88stYrVZAe+asW7eOJUuWeO0+rtXTkVIOqeXvnDU4oI3puFvo7pbw999/r6ckAJKatyDHUvOPn2PxpZkB+/6N4um89NJLrF+//qTtRvJ0JkyY4FnRdPigfxAYGERMTAzfffcdbdq00VnduUFiYiKjRo3SW8ZJ/Oc//6Fr164AlJaWYrfbkVLy9ddfc8stt+isTqOsrIwLLriAiIgIbDaba1xRejVysk7TJoSIBm4B3DPXdgHTpZT5p/6UccnNzSUnJ4dOnTqd9DA6evSoTqqOk5SUxM7NJz/E7U7Iq9A/cq2muS9GMTqPPfYYX3/9NeXl5QwaNEhvOTXy7rvv4nQ6MZvN2B2V2J02ggKDvJpw8VzHbDYbck7TK6+8wvXXX48QguDgYEJCQnA6neTn5/Pf//5Xb3k4nU7Wrl3L3r178fHxISsri4yMDO644w6v6qgrZLoTsB3oA+wB9gLnAduFEOfk9On09HQ2bdoEHG8B5+Xlce+99zJ27FgdlWkkJiZSapWU2QRvzD/G9qNaV1uuxYQE3QecJ0yYQFZWVrVtRjE6ERER3HjjjSdlajaSpwNaV0x4eARrti3BYisjQsdljM91jPTbjh49mrZt2+JwODy6LrnkEp5//nl++eUXVq1apau+7OxsnnzySW2Csmt9nc6dO9O7t3eH5+vydP4LPCalrJawTAgxCngFMJ6PWwePPPIIy5cvB46H+kZFRfHuu+9is9l0n6DnNirZFSYeGhRLsJ/J877qfr2oqUVuFKMDxsjWUB8iIiIIDy2j0l5OuAFmqyvOHvdcnC5dutCuXTvWr1+P2Wzm66+/JiAggF69eumm7c8//2TNmjU899xzlJeXs3PnDrKyshg3bhwXX3wxKSkpXtNSV/RatxMNDoCU8kega20fFEJMEkJkCyG2n2K/EEK8L4TYJ4TY6q1ouL/++qsmLWzcuJGnnnrKGxJqxd11lVNhIjXLwj3TDwHHjY7e3Ws1YaR5Om7y8vI86/0YkYiIcKKio7HaLSxevEhvOecsRgkCApg/f75nGsauXbtIT0/Hx8eHG264gbvuukvXlWyHDRvG+PHjKSsrIz09nUOHDiOE4L///a9XDQ7UbXTKznAfwBS0CaWnYjjQzvV3H9ok1Eanpla5lJI+ffrw7rvvekNCrXiMjsXE6oNlvHSle+6OGV9fH0Out2KkLg43dru92qJZubm5NTY49CIsLAy704LNWcm9996rtxxFA5Cbm+vJ59i9e3fi4+OxWCwEBAQYYun0/Px8BgwYwLPPPovT6aS8vNwTSFBTIuTGoq7utTghxLgatgug1un7UsqlQoiUWopcA0yTWlNltRAiQgiRKKX02mi+0fI3AYSGhhISHERuhYWHBx3vKsqpMBEfG2tIr8KIxMfHe0KTpZSUl5efNBalJ6GhoVgdldgdtmprOinOXf75z396Xm/atImCggJSU1N1VFSd3bt3c/jwYVq1aoWPjw+hoaF8/PHHXHXVVbz//vteC0Ov6wn2ORBaw18I8MVZ1t0MqJoFL8O1zatYrVZsNpu3q62VhISEkyaI5lrMJCQZb44OGMto14SUktzcXG688Ua9pXgICQmh0lqBzV5piAzEioYjJyeHn376iejoaPr06aO3HA+//fYbYWFhPPbYY3Tr1o3o6GjGjRtHSkqKV+c91WV0JkkpX6rpD9jgDYEAQoj7hBDrhRDrc3JyGvK4FBQUGCJUuioJiUnkVh4PaNh2pIJVuzINOZ4DxupXr8qRI0fIzc1FSsn06dM9ywoYgaCgIJxS6/5Tns7pY8ReCtDm51x55ZXcdtttWK1WQ+m79957GTZsWLV1pg4cOMD06dO9qqMuozO/pi4yIcRdwHtnWXcmUDX1anPXtpOQUk6UUvaVUvZtyKScUkri4+MNF/MfHx9PXpX8a21iA4iKSzxnIrOMQkJCAiaTie3bt/PWW28Zol8d4OabbyYo6Hgm8aqvFec2O3fuJCQkxJNSxkgNslatWmEymaplHujTpw9XXXWVV3XUZXTGAfOEEO3cG4QQz7q2X3yWdf8CjHZFsfUDirw5nlMVI10YoIX9Vtgl5a5USMU2M76+vsro1MGWLVvYsWOH573JZMLX19dw3Vevv/66MjpnidGWNnBz/vnn891337FmzRqCgoIM5em42bp1K4cOaVGxISEhhIeHe7X+utLgzAEeBP4QQnQVQrwLjAAGSSkzavusEOJbtCUROgghMoQQ9wghHhBCPOAqMgc4AOxDGzvSZ7lOjOeiu41Lvmtcx/1fGZ3aKS8vp6Kiet660NBQw3myKSkp1ZK2+vn56ajm3MZI9+67777L1q1biY+P51//+he5ubksWmSscPgPP/yQzZs3ExYWBugzx67ONDhSyoWu7rQlwErgkvqspSOl/Gcd+yXwUD11NipGay25w6ILKk00D3GSX6kZHb3X+zE67vXoARYuXEhlZSX+/v6UlZVx22238dtvv+morjpuo1NZWcmECROYN2+ezorOTYx071577bXVVvWNiory6jLQ9eGee+7Bx8eHhQsXUlhYqEs0bF1pcEqEEMVoXkkYcCmQXWX7OY2RWklVqWp0AAorRbXtirqxWCyeB9LevXtp27atzoqq4zY6/v7+vPfe2Q6PKoxASkoKu3btYtasWQCe8ZOMjAxuv/12ndVpBAYGVsu6YjijI6UMlVKGuf77SSmDq7wP85bIxsZIrSWA6OhooIrRsZoI8PdXff+nwVVXXeUJHOjWrZshEi5Wxd/f3/PaaOsjKc6cxMRE2rdvz/Llyz1Rsc2bN+fzzz/XWdlx0tLSOHjwIABLly5l8eLFXq2/Sc80NGrYZUBAAEGBARRZNV1FlSaio1RSyLp48803+eWXX07aLoQwnMGuanT8/PzIz89n48aNOipSNASJiYl06dKFAQMGEBISwpIlSwBYtmxZtQwZejB+/HjS09MJCQnx3A+dO3fG4XAwZoz3htSbtNFxYzSjAxAVGUmRVft5iqyCyKhonRUZn3HjxjFixIiTtq9YsYLnn39eB0WnpqrR8ff3Jzc3l507d+qo6NzEiPeum6CgIM/6Ohs3btR9EvrAgQP5+OOPiY2N9QQlWa1WrFYrH3/8sdd0qCUfMV73GkBkVDQlmYcBKLb70CoqSmdFxscdiWOxWPjss8+QUmK327nooou45BJjrTl4oqfTvn172rdvr6Oi6tjtdt555x3S09N5//339ZZzSox477oxm82e4J+nn35aZzXaejruicg7d+6ktLSUwMBAIr28tEaT9nSM3EqKiIykxK61CUptJq/H0p/L+Pr60rp1ayoqKkhP1zIt/fzzz54leo1A1cHcqgbIKOzdu5eff/6Z1atX6y3lnGbevHlkZ2frLQOAK664gueeew7Qpl+UlJSQkJBQLerTGzRpowN4sqwajfDwcEpsJqSEkkqpjM5pYDabGT58OGlpabRu3RohBEePHvXMEjcCVefm6L2GU01IKTGZTIbJ4nAu4nQ6ad26tWdOjN6Ul5ezd+9eQIuETU5O9kwS9SZN2uiUlpayY8cOQ3o8oaGhlFklFQ5wSJTROU3Kyso8D0whBA888IChggmqGhojXn9VU6UozowdO3bQsmVLwxjuQ4cOMWfOHM97q9XK6tWreeuttzzRbN6gSV9ZISEhnoE+oxEaGorNeTxsOjQ0VGdF5xbh4eG0adMGMOYic0ZabbUmVJaEs6dbt26G8mI7depEp06dAFi3bh1hYWHcdtttBAQEkJeXR3JyslcaQE3a6LgxYvea28jkViijcya8++67ZGdnExcXZ0ijY0TvpipGNzpGne5gZNLS0lixYgVHjhyhc+fOZGVlIYQgPT2dP/74g3bt2nmlK9B4d6MCOJ7u3r2ujtGSVhqdsWPHesJC1YPp9DFKl1BVjhw54gkMMTKTJk3yzMkx0rXnzkPYvXt3goODEUJgMpnIzMzkpZde8trYU5M2OkZuLbmNTJ4yOqeNzWbj2muv9bzftm0bS5cu1U/QOYgRPZ0dO3awdetWwJhZpt1aqnapGUnfsmXL2LJlC506dfKsLSWE4J577uGnn36ipKTEKzqatNFxY0Sj4x70LqxURud08fX1ZdKkSZ73fn5+KtXMaWLEMO7LLrvM62u/1JcZM2Z4cujdfvvtZGVlkZaWprOq6owYMYJ7772XNWvWUFRUBBwf74yJifFa8Iga08FYrRE3bqPjDiRQD83TI6rKZNoOHTroqOTcREWvnR7updD37t3Lp59+SqdOnUhKSjJUg9bHxwcfHx9uvPFGfv/9d9LS0jz6Ro4c6TUdytPBmJ6Ou0+92JV/zchGx4jnD6CoqMhQS1SfSxj1NzU6bdu2pWvXrnz77bccPnyY7Oxs/vjjDzIyal1+rNGxWq188MEHLFu2rNp2w2WZbioY0dNxG50iq4lDhw4ZOi+XEc8faJPzjKpN8ffkySefZPHixVx++eW0a9eOiooKNm7cyO7du3XVZbFYOHLkyEnLo+jRuGjSPrSRW3Nuo1NqEyQmJvDll1+SlJREs2bNdFZ27uDtnFIKxbhx40hKSmLy5MkAtGzZkrvvvltnVRAWFsZrr7120nbl6eiEEVvD7oFchxRERkTw6aefGtbgGNl4KxTepFmzZtXuByEEQ4YMMeQzRi+atKfjxogPTR8fH0xC4JQSPz/jzGquCXVDKRQaEydOxGq1egKBpJT88ccfhnzGgD6ZMZSnY1CEEPj6am0CI86ZUCgUJ5OTk0O/fv0874UQhpxo60YPY6iMjsHIycnhgw8+AI5PMvP1Mbano1AoNJ577jn69u0LaEln9V64zYgoo2MwQkJC6NmzJ3B8roRZzZlQKM458vLyKC0t1VuG4VBGB2ONSQQGBjJw4EDgeH+rkTLVAhQUFBhqbRpF08WoYyW///47LVq0ICIiQm8p9cKbId3K6BgYsyuc0WSwNPgfffQRGzdu1FuGQmFYWrZsCRjHKNrtdjIzM1m7dm2N+998802vdQUqo2Ng3DH0ZoOl5n/++ec5//zzDXND1Yd9+/Zx77336i1D0cAYqZeiKt26dUMIYRh9M2fOZNKkSadcKfSLL77wWo+KGiwwMMJlbITBjI4bo9xQ9aFt27a8//77estQNDDnUsNHT/75z3/qLcGDMZ9mCgDU/dSwGDl/neLMMHrDx+hGMTU1tcZMBY2J8nTOBQx+YykU3sboD3M3RjeKrVu3plevXl6tU3k6BkY6tQvW6BeuQqGoGaMaR7cuX19fkpKSvFq3MjoGxr3krcPp1FmJQqE4E4zaYNRz1WRldAyMx+ioWc0KxTmJUT0dPVFGx8BYXcbGZldGR6E4FzGqp6MnyugYGJtNm/VvrbTqrEShUJwJytM5GWV0DIqUkkqrZmwslZU6q1EoFGeC8nRORhkdg2KxWDwXrMVi0VmNQqE4E5SnczLK6BiU8vJyAML9nFgqrSrBpkJxDqI8nZNpVKMjhBgmhNgthNgnhHimhv13CiFyhBCbXX//akw95xIlJSUAxAVq4dIqRbpCofg70GhGRwhhBj4ChgOdgX8KITrXUPR7KWVP198XjaXnXKO4uBiAhCBHtfcKhUJxLtOYns75wD4p5QEppRX4DrimEev7W1FQUABAixDN6OTn5+spR6FQKBqExjQ6zYD0Ku8zXNtOZJQQYqsQ4gchRItG1HNOkZubC0ByqGZ08vLy9JSjUCjOAKMGEuipS+9Agl+BFClld2A+MLWmQkKI+4QQ64UQ63NycrwqUC+ys7MxmyDFZXSayvdWKP5OGDWQQE9djWl0MoGqnktz1zYPUso8KaV7EsoXQJ+aDiSlnCil7Cul7BsbG9soYo3GsWPHiAmEIB9JkK/g2LFjeks6CaO24hQKhXFpTKOzDmgnhGglhPADbgZ+qVpACJFY5e1IYFcj6jmnyMzMIM7fhhAQF+ggIyNDb0kKhUJx1jTaejpSSrsQ4mFgLmAGJkkpdwghJgDrpZS/AI8KIUYCdiAfuLOx9JxLSCk5fOgwg+K0rrXEQDtph9J0VqVQKP4u6NlL0aiLuEkp5wBzTtg2vsrrZ4FnG1PDucjRo0exVFbSLFgzOs2CHaw6kEN5eTlBQUE6q1MoFIozR+9AAkUN7N+/HzgeudbS9X/fvn26aVIoFIqGQBkdA7Jr1y7MAlq65ui0CtNS4KSmpuopS6FQKM4aZXQMyPbt22gZ6sTPrL2P9JfEBML27dv1FaZQKBRniTI6BsNqtbJzx046RFRfQ6dDeCWbN200bNy/QqE4d/i7ztNRnAHbtm3DarPRKbJ6VunOkXYKi4pJS1NRbAqF4txFGR2DsXbtWswm6BxZfYnqrtHa+zVr1ughS6FQKBoEZXQMxvJlS+kUYSPwhGD26ABJcpiT5cuX6SNMoVAoGgBldAxEWloa6RmZ9Im11bi/T0wl27fv8CQDVSgUxkalijoZZXQMxIIFCzAJOD/OWuP+C+KtSClZsmSJd4UpFIozQgX+nIwyOgbB6XQyb+6fdI60E+5f84XaLNhJcqiTuX/+4WV1CoXiTFCezskoo2MQNm3aRFZ2DoOSLLWWG5hoYfeevZ6sBQqFwrgY3dPRQ58yOgZh9uzZhPgJ+p5iPMfNRQlWfE3w888/e0mZQqE4U4zu6djt9roLNTDK6BiAnJwcli9fxqCECk8WglMR6ie5IL6SuX/+QWlpqXcEKhSKM8Lonk5FRYXX61RGxwDMmjUL6XQytEVl3YWBy1tUUmGpZM6cOXUXbkSMfkMpFHpjdE9Hj4arMjo6U15ezs+zZ9EnxkpcoLNen2kd5qBDpIOZ33+ni3usUCjqh1EbZm5dhYWFXq9bGR30bY389ttvlJSWcVVK7QEEJ3J1ywqycnJZuHBhIylTKBR/V2w2bVpGVlaW1+tu0kZH71ZIZWUl307/hk6RdtqGO07rsz1ibDQPcfL1V9NwOuvnISkUCu+g97OlNhwOBzk5OQBkZmZ6vf4mbXTc6OXp/PHHH+TlF3BNq9MfzDMJuCalnEOH01m6dGkjqKs/Ru+3NiIOx+k1MhTVMfJD3egcPnwYq1WLkt29x/trdCmjgz4XsNVq5euvptIuwkGXyDMbl7kg3kZisGTK5Em6ejvqAXD6WCzHu1OVAfr7UVRUVO2/kdixYwcA7Ts4yEjP9Pq4jjI66NNS/+OPP8jOyeP6VuWcafUmAde1KuNA2kFdvR3VvXf6lJWVeV6Xl5frqETRGBw5cqTafyOxfPlywsIEPXpojd1Vq1Z5tX5ldPB+S91qtTJt6hTaRTjoGnV20Wf9XN7O5Elf6vbwr9pqV9SPqkan6mvFuY+U0rO0fGpqqqF6AgoLC1m3bi2tWtmIi5OEhgoWLJjvVQ3K6OjAb7/9Rk5uHqPOwstx4/Z20g4e4q+//moYgfXEfTOVlJR4td6/A1XnRxQXF+uo5NzGiOOJhw4dori4mNDQUIqKijh06JDekjzMnj0bm81Ol64OhIAuXaysW7eeAwcOeE1DkzY6bs/Amy2RyspKvv5qGu0jHHQ5Sy/HTb94G0kh3vd23OfNHQmjqD9V+/qV0T593NeekbwIN4sWLUIIQceOHT3vjUBhYSEzZnxPSoqTqCjtvHXp6sDXV/DFF194TUeTNjruATRvXrhz5swhNy+f61ufvZfjxiTgupQyDh467DVvR0pJZaWWQcFILblzhapGR48Jeuc67i5doxkdi8XC7NmziYyMJDQ0lKioKH7++WdDdEF/+umnlJeXceFFx/M7BgZC375Wli9f7rWxnSZtdNwPS4ulfulnzhabzcY3X391VhFrp+ICl7czdcpkr9yIR48e9eRt2rJlS6PXdzYYMWtDQUFBja+NgtVa85pORsHtXRvN6Pzwww8UFhaSkpICQEpKCgUFBfzwww+66vrrr7+YM2cOPXvaPV6Om569HERFwRtvvE5eXl6ja2nSRmfrtm0ApB1M87TaG5P58+eTnZPLtSkN5+W4MQkY0bKcA2kHvdJimTdvHgDO5k42btxo6C627OxsvSWcRH5+Pr5mP4QwGdLoGFFTVdxLe1R6qcFYH/bv38/kyZOJiYkhIiICgIiICGJiYpg8ebJuy5EcPnyYV199hfh4yQX9Tm6Amc1w+RWVFBcX8sIL4xu9kdZkjc7Ro0fZumULjtBEKi0Wli1b1qj1SSn5dvo3tAx10j26cX7U/glWYgLh2+nTG+X4bkpKSvjxpx8hHmQ3idPpZHoj13m6VM2ea8S1h/Lz8wn0CyHQL9grrcvTJT093fPaaN4EwJo1awA4euyoLqlcTqS4uJjx48djMpk8YzluOnbsiMlkYvz48V4PGsnJyeGJJ8YBlQwbXon5FFnsY2IkQy6pZOvWbbz66quNOjbcZI3O559/gURQ2WYQBIbz5aRJjertrFu3jkOH07myZUWDezlufExwefNytmzdyu7duxulDiklH3zwAUVFRTi6OSAEnG2c/PTTT2xzeY5GYMWKFZ7Xjd2gOBNyc3Px9wkmwCeY3NxcveWcxLp16zyv09LSdFRyMtu2bWPPnj0MTe6PEEL3rquysjKefPJJMjMz6dy5M35+ftX2+/n50blzZzIzM3nyySe9Ni+rsLCQceMep6Agh6tHWAgNrb18hw5O+vW3sWDBAt55551Ga2w0SaOzcOFCFiyYjzWpJ9I/lIrkC8nMyOCjjz5qtBP9008/Ee4P/eIbt698cLNK/H0Es2bNapTjT506lT///BNnRydEattkNwnB8PQzTxviAWWz2fh62jQiTCb6AAsXLODw4cN6y6pGTk4OAb7BBPgGk5tjLKNTVlbG77/9RrifLyYhmDlzpt6SPFgsFt568y0iAsIY0XYw/RJ78MMPPzRaI6suCgsLeeKJJ9i9ezddu3YlMjKyxnKRkZF06dKF3bt3M27cuEbPVJCbm8ujjz5CZmY6V15VSXx8/Z5rffo46N3bzs8//8z//ve/RvF4mpzRWbhwIf99+WVkaDy2pJ4AOMObYUvoyuzZs/nwww8b3PBkZ2ezetUqBidW4NPIZzzIBy6Mt7BwwfwGXSvD6XTyxRdfMGnSJJzJTmSXKufIF+wD7ZTZy3j0sUfZuXNng9V7ukgp+eyzzzhw8CBXOZ1cCvg4nbzy8suGmfkvpSQvL49A3xACfEMM1b0mpeTtt9+muKSETpEhtAgJ4Pfff/f6rPWasFqtvDB+PAcPHuS2zlcT4OPPDR0uJ8wvhH8/9e9qXYLeICMjgwcffJDU1FS6dOlCTExMreVjY2Pp0qULqampPPDAA42WbDMrK4tHHnmYzMzDXD2ikubN6284hID+F9rp3cfOL7/8wmuvvdbgYzxNxug4HA6+/vprXpowAXtwHOXtrwDT8a9vbXkBtoQuzJw5k5deeqlBH9jz58/HKSWDkrwTEXRxUiWVVluDpcYpKSnh6WeeZtq0aThTnMi+Ek7sIgwB+yA7xfZiHnr4IX755RevjwU4HA7eeecdZsyYwflARwShCK5xOtmdmsrYxx4zRHhyWVkZVqtVG9PxDaGouAibrfZlyr2B3W7nrbfeYv78+bQNDybc35d24SGE+fvyn//8R1fDk5eXx9jHHmPV6tXc0vkqusW2ByDEL4hHe9+KrbySBx94kI0bN3pFz/r167n//vvJycmhV69exMXF1etzcXFx9OzZk5ycHO677z42bNjQoLoOHz7MmDEPkpt7lJHXWE7L4LgRAvr3t3PBBTbmzp3LCy+80KBDD03C6GRmZvLwI48wceJE7JEpVLS/Anyq97siBNaW/bA278uixYsZPfqOBrsgFi1cQNtwB/FB3pm42SbMQXyQZOHCBWd9rFWrVnH76NtZu3Ytzt4ug3OqqyYM7JfaccQ4eOutt3juuee81opPT09n7GOPMXv2bAYAV1fZ1wXBP4H9e/Zwz913s3LlSq9oOhXuMZwA32AC/IIBLbBATzIyMnj0kUf49ddfaRUWROuwIADMJkGfmHAChOSZZ57hs88+83o49eLFi7nrzjvZk7qH+3r8g0Et+lbbnxQSx9Pn30Ow9Gfc4+P4+OOPG218VkrJt99+yxNPPIGUkt69exMeHn5ax4iIiKB3795IKRk3bhzffvttgzTQ9u3bx0MPjaGsLI9rr7OQmHjmxxQCzjvfwcBBNpYtW8bTTz/dYEtbCyNGptRG37595fr16+tVtrKyku+//55pX32FzQEVyf1xRLehrpF8U2k2gQeWQkUhw4YN44EHHiAqKuqM9GZlZfGPf/yDm9uWc3WK98I7p+8NZF5GML/8+ishISGn/fmioiI+/PBD5s6diwgX2Pvaob6nQILYIzDvMBMUGMRjjz7GFVdc0SgpSywWi/YbT52Kj8PJMOmk90lumEYGklnCRLZ0MmTIEMaMGUN8fHyDa6qLTZs28dhjj3FxhxtxOG0s3zuLzz77jE6dOnldS3l5Od999x3ffPMNOJ10iggmMTjgpHIOp2RXQQmZZRaaJSXxyKOP0r9//0ZNQ3P48GE++vBDVq1eTXJ4End2vZakkFN7FBZ7JT/unsfSjA0kJiTy8CMPM2DAgAbTaLFYeP3111m0aBGxsbF06tQJHx+fMz6e3W5n165d5OTkcOmll/L0008TEHDyua8Pe/fuZezYx4AyRl5jITKy4Z7rqbtMLFzoR48ePXjjjf8jMDCwxnJCiA1Syr417qxa7u9odKSULF++nPc/+ICsY8ewR6Vgbdkf6R9c/4ocdnwzN+KXtZ1A/wDuvvsuRo0addoX2e+//84bb7zB6/2KaB7ivRQ1O/N9eHVjKK+99hoXXXRRvT8npWTevHm8/8H7lJSW4OzgRHaScIpQy1opAfN6M+RC7969eeKJJ2jRosUZHOhkbDYbv/32G1MnTya/sJCuwJVA6CkMjhs7kuXAX0KA2cx1113HbbfddsoB4MZg0aJFvPjii1zR5U7sThsLd33DG2+8Qf/+/b2mobKykl9//ZWpU6dSVFREQpA/HSJCCPCp/YfOrahkd1E5pVYb3bp25d777qNnz54Nqq2wsJBp06Yxa9Ys/Ey+XNV6EJe0vACzqX4XYWreAb7b/QdHS3Lo3as3D455kA4dOpyVpqysLJ599ln27dtH69atSU5ObhBjJqXk0KFDHDhwgLZt2/Laa6+ddkMoLS2Nhx9+CCjj2usshIc3/DN9924TC+Zrhuett/53UoQeNGGjk5GRwbvvvcfaNWsgKJKKlv1whjc74/pERRH+h1djLkynZctkxo17nN69e9f786+++iorF8/lowH5jRYqXRNWB9z/VySjbryZMWPG1OszOTk5vPbaa6xfvx6iwdHHAafXc3AyEsQBgXm7GbM0c9+993HjjTdiMp1Zz67D4WD+/PlM+vJLjmVlkSwEl0lJch3G5kQKkSwBNgL+/v7ceNNN3HTTTYTWFVfaAMyaNYt33nmHET0fxOGwMWfbFzz77LMMHz680et2G5uvv/qK/IICogL8aBceTIS/b72P4ZSSjNIK0kosWOx2evTowV133UWvXr3O6kFstVqZOXMmX331FRUVFVzUrBfXtL2EMP/T99QdTgdL09fz24G/KLWWM3ToUO6///4z8mzT09MZO3YshYWFdOrUqc6AgTMhNzeXXbt2ERERwXvvvUfz5s3r9bnCwkLuvfdflJbmcP2oxjE4bnbvNjF/nh/Dhg3j2WefPem3bnJGx263M23aNL7+5hscUmBJ6o09vnO1YIGzwVxwiIDDa8BSzKWXXsrYsWPr1Zd75x2jCS3ey1O9Gi4wob6MXxtGZNvzeOfdd+ssu2TJEt74vzcot5Tj6OZAtqkhWOBsqADTRhPiiKBnr5489/+eO60HgJSSpUuX8vnEiRxOTydJCIZKSVtAnIXQHCSLgO1ASHAwt9x6KzfccMMZd3PUh2nTpvHFF18wqs/j2J02ft70IQ8//DA33nhjo9Vps9k0z2bKFAoKC4kK8KNNWBBRASe3WOuLw6kZn4Olx43PfffdR7du3U77WCtXruS9d9/j6LGjdIttz/Xth9balVZfKmwW/kxbzsLDqzGZzdxy6y3cdttt+PrWz8imp6fz8MMPU1ZWRo8ePRq1UVJSUsKWLVsIDg7mww8/rLNXQErJ2LFj2bp1M9dfbyE+ofGf5WvW+LBurQ9jx47l+uuvr7avvkanUQMJhBDDhBC7hRD7hBDP1LDfXwjxvWv/GiFEypnUU1xczJNPPcWUKVOwhLWkrNsN2BO7NpjBAXBEJlPW7XqszXqxaPES/vWve+tMB+5wODh8OJ0WIfqsDNk82E5aWt2z8WfMmMH48eMp8yvDPtSObNvABgcgEJwXOnH2dbJl2xbuf+B+MjIy6vXRvLw8nnn6af7zn/9QkZHJzcADUtIOcVYGByAWwU0IxgBJZWVMnDiRu+64o1HDvsvKyjCZzJhNPvia/YHGXchtw4YNjB49mnfffRcs5ZwXF8F5cRFnZXBACzJIDgtiQEIkHSNDSN2xnYceeogXX3yx3hNey8vLee2113jmmWegzM5jfW7n4d63NIjBAQj0DeC69kN56aKH6RrVlsmTJ3PfvffVaz6Z1WrlP//5D2VlZfTq1avRveDQ0FB69epFWVkZ48ePrzNg46+//mLTpk0MGGj1isEBOP98Oy1bOvn884lnnF2h0YyOEMIMfAQMBzoD/xRCdD6h2D1AgZSyLfAO8Mbp1pOfn8+/7r2PTZs2U9l6EJVthyD9gs5Wfs2YfLA170N5p6vILizh/gceqDXZZUFBAXaHg+gAfRZXiwl0kl9QeziuZ25SM4ljiAMa874SIFtJ7JfYKSgr4OFHHq4zhcmGDRu4c/Ro1q9Zw5XAQ9JJlwYwNieSiOB2BHcBZVlZjBkzhq+//rpB63BTWVmJr1l74JuECZPJ3GCRQVWRUjJp0iQef/xxCnOy6R0bznmx4WdtbE7EbBIkhwYxICGKNmFB/LVkMXfddVedGSrKy8t58okn+PPPPxneeiDP97ufzjFtGlSbm+jACO7r8Q/G9LqZnMwsxjw4ps4JpVOnTuXAgQN07NiR4ODTGA8+C4KDg+nYsSP79+9n6tSppywnpeTzzz8jKgq6dPFeo1YIuPAiG+Xl5Xz//fdndIwzD72om/OBfVLKAwBCiO+Aa4CqTchrgBddr38APhRCCHkafX7ff/89x44dpaLTVThDE2otW16UR+rn42o/YGRLet/z31qLOEPiKOs8ErnrNz755BM+/fTTGsu5Zx2H+9XP6OSUVDLsw734+0KQH5RVQrA/2B0ggc6Jwbw6ojmRwfXrGnDXW1RUVGM/tNVq5dPPPkXGSZz9nHU2QbZM3ULZhtpXuez/Xj0imsK1yaQFiwqYOXMmDz/8cI3FLBYL/50wAb/SUkZLSVwdhmbwsrrnJS0ZOKjW/a0RPOR0MhuYOHEivXv3pnPnE9tKZ8a2bdsoKSlh5cqV5OTk8Nem30iKasmxY8c4cuQIhw4dIjk5uUHqAi2IZcqUKSQFB9A5MhSzqfbzt2zHbgL9fYgIDubQsWxaxsdxLL+A8KBgNh5I48q+PYmLOHWXso9J0DYihISgADbnF/P0008zdepUYmNjayz/8ccfs2PnTu7tfgN9ErrUqs3usDP0jX+SV16IEycB+OJE61q1YMUfP7685w16t+pe63F6xHWkRWgCb66bwjNPP8PMH2aeMjho7ty5xMTE1GsM5+OPP64z6W1MTAwPPfRQnceKiYkhOjqauXPncu+999ZYJicnh/T0TAYOstWrQ+emG9fVWeb5/yTRrVvd498xMZKkJCfr1q09pb7aaEyj0wyoOkU4A7jgVGWklHYhRBEQDVTzzYUQ9wH3AbRs2bLaAX755VeQELRvYZ2CNi6cV7fqgsOEbPqm7nKAtFvZuXMnaWlptGrV6qT97rkCn6eGMGVP3S3zxcvXUG4Bqi69UWUo6GBhGWuzjnFB7x710md1aLb7VJ7O5s2bKS8rx1Rpwvx73ZFBdRkcgLQ30ujQpX6RQk6nkyV/LTml0Zk/fz75BQX4CcHkBuoqfTQ7i2YJtTdOQODQBPLt9On89+WXG6Tuyy+/nOLiYk9X2hY2efZ98cUXrFy5koUL676O68uvv/4KQF6ljWXHas8abbPZ+Gvz9uobd+6t9vbjuUsYPHBgvequtNvBWsqKFSu49tprayzzx5w/tHkvqXP4NnVOrcfbtmsH2eXH5zJVUP2arsTKbV8+zpVDh9VLX0llGVTAjh076NHj5PspNzeX7OxszGZztTx+p6I+WdZzc3PrdSzQxqgdDge5ubk1Gr2DBw8CsHaNHxvWN4zX//J/j3DFFe3qVbay0kle3kGklKcdPNKYRqfBkFJOBCaCFkhQdd/o0bdz9OjReh3nmiuvaHBtgYGBp2zJtWnThltuuaXe/fWXDL+mIaUBEB4efsoB+7Zt23LjjTfWe7LfyGEjG1IaoJ2jU9GjRw+uv/76eud/Gjqy4fWdf/75DXYs93W6a9cu/vjjj2r7RowYQbt29bvh68uNN97I5s2b6/1QGHnddQ1av8lkok+fPqfc/8CDD3D48OF66bt0+GUNKQ0pJQEBAZ51b04kIiKC0aNH13vcojEiD8PCwjxLJJxI+/btT+vevfzyhr83mjdvfkbRio0WvSaE6A+8KKW8wvX+WQAp5WtVysx1lVklhPABjgGxtXWvnc7kUIVCoVB4ByNEr60D2gkhWgkh/ICbgV9OKPMLcIfr9Q3AotMZz1EoFArFuUWjda+5xmgeBuaizWefJKXcIYSYAKyXUv4CfAl8JYTYB+SjGSaFQqFQ/E1p1DEdKeUcYM4J28ZXeW0B/tGYGhQKhUJhHJpElmmFQqFQGANldBQKhULhNZTRUSgUCoXXUEZHoVAoFF7jnMsyLYTIAQ414CFjOCEDgsFQ+s4OI+szsjZQ+s6WpqYvWUpZ80z5KpxzRqehEUKsr8+EJr1Q+s4OI+szsjZQ+s4Wpa9mVPeaQqFQKLyGMjoKhUKh8BrK6LgSiRoYpe/sMLI+I2sDpe9sUfpqoMmP6SgUCoXCeyhPR6FQKBRe429ldIQQDiHEZiHEFiHERiHEha7tKUKI7XV9vp51LBFC1CviQwiRIIT4TgixXwixQQgxRwjR/gzrfVEI8aTr9QQhxNDT+Kz7vLj/nqmlbE8hxJVnoO+gEKLuJRbPUFdD1Fn1HOqlSQgRIYQYU+X9YCHEb6dZxxQhxA2u11/UsAz8ieXrfc0qFI3N38roABVSyp5Syh7As8BrdX2gsRDa6kazgCVSyjZSyj4uTTWvqHbCZ4UQp/xtpJTjpZQLTkOO+7y4/16vpWxP4LSNzukihDCfpi5v4dEE9G0ETRHAGLdxA74ABtZl3IQQ/6+m7VLKf0kpd9a0r5ZjLRFC7K5iWH84zc+XVnkthRD/q/L+SSHEi6dzvHrU11cI8f4J22o1pEKIECHEZ1UafEuEEFcLIX4WQux1bX9PCOF3YkOrtsaJEGKsECLoDL/HEiHEYVFl5TMhxOyq5/MUn3tOCLFDCLHV9XuduAJz1bK1NqxcDZZyIURolW3vun7H02o0nil/N6NTlTDgpDV6hRABQojJQohtQohNQoghdWwPdHkru4QQs4DAetY/BLBJKT91b5BSbgE2CSEWCs0T2yaEuMZVT4rrQTAN2A60cF1se4QQywHPGtAntHTHCyHWCSG2CyEmui9oIURbIcQCIcQWIFAIcdISnUKI84QQK4XmGa4VQoQDE4CbXBf3TUKIKNeNsVUIsVoI0d312WghxDzXzfAFUPVGus11vM2uG9/s2l4qhPifS1N/IFgI8abrGAuEEOe7bkybEOLbKueoYz3qHOc6B9uFEGOrbD/VOWwjhPjT9UBa5q4D8BdCfCqEWAP8n+tcfyKEqBRCFLiOle96aE05A02vA+7fYj7wL2AT0FcIkSqE+KbKb9hHCPGXEGIDMEEIkVjDb7hECHGe6/UnQoj1Li0vnVj2BG6tYuxvqKNsbVQC15/4wBLaooxnjNAwAUgp10spHz3NQ3yBtlxKO1eD7y7gf8BsKWU7oD0QArzC6TW0xgK1Gh339X4KCoGLXOUigJN+0xOO1R+4GugtpewODAXS66n1VOwD3M8dE3AJkHmWx6w/Usq/zR/gADYDqUAR0Me1PQXY7nr9BNraPgAdgcNAQC3bx1XZ3h2wo7WA69LyKPBODdt9gDDX6xi0C0C4NDqBfq59fYBtaBd4mKvck659U4AbXK+jqhz7K2CE6/Ua4Loq52WL69xsBm4C/IADwHmuMmEubXcCH1Y55gfAC67XlwCbXa/fB8a7Xl8FSNf36QT8Cvi69n0MjHa9lsCNVY4tgf0uTYUujb5oN0CGq8wY4Is66nSfq2C0B8kOoFcd53Ah2gMJ4AK0BQRx/QZFVc7VEuA74CBatE8x8DLaQ20DMP00NaWgNSpKXZ8Z7KqvOZoXVAH803UecoDH0QyVE8gDvgF+cJ2jaUAZ2kPpEzTjtQOt4bAE6O6qYwlVrtkT31fZPsV1nNVo18ZgYBKwC5hSpVwp8I6rLrvrfLziOu5S4AjwAlrmkJ/QFnTc4NruC3zq2rcVrTdgp+u8rHZ9x1w0Y3aDS89uoAR4yXUuJ7s07AVG1fA92gBpgLnKtktd2lJcx5vm+l5FrmPZXOf+B+BF1/48l44jaPfMo67vW+7S85m7Dtcx/od2DQ84xTNhCfD/cN1fwN3A07iuBde2p1zna6vr+16PtjzM765jb3dpOYh2XVa49MwE/F3an6zluTTFpeHXKvf0J67jxbi23QasRbv+P0NbD83s+ux2tOv6cVfZnq7fzf1bRtb1bPy7eTrurpGOwDBgmrvVWIUBwNcAUspUtIu/fS3bB1XZvhXt5J4NAnhVCLEVWAA043iX2yEp5WrX64HALClluZSymJNXXXUzRAixRgixDe0C6iI017mZlHKWq0yFlLKHPN6y/R6t1X9USrnO9d2KpZT2Go4/AM2YIaVcBEQLIcKofl5+57hXeSnaA3ed0LqPLgVau/Y5gB9POH5bqXVlvQ/MkFLa0B4Abvd/A9qDglrqHOA6V2VSylK0B93AU51DIUQIcCEw06XxM463OB3Ao/J4F9tBNCMK2sMuy/U+Be2h2/80NbkJFMe710zARVLKQrRrYgLwGBCJtrLuMDRjtkFKeavr84loBn0d2jLvz7m+RyVaQ6k7UNtYzzfiePfam1W2R7q+0+Ou8/UO0AXoJoTo6SoTjLYQYxfX+YoFbkV7MPkAE6WUL6E9oNdJKc/D9VB0/b7XAb9LreW+DYirUn8UmhG9DnjA9b3uB/4CLka7ToqA9WjGeVEN360LWuPIccK2Da7X7YCPpZSd0Az7Fte5C0ZraCYD5wOz0e5Pf7TfZT5gBVpLKUNd3939ewQDa1z32fIaNLlZCAxyeUM3A9+7dwghLndpOx/tYd4HrVHRCe16WgE8BPyJ9hwZBvRwnaf+wIO11FuVPUCsECIS7Rx+V0VDJzSjdpHr+nd/x55oz5SuUspuaPcCaMb56Sq/5Qt1Vd6oi7jpiZRylcvlrzMXUCOxA62ldiK3omnqI6W0CSEOol3ooF1g9UYIEYD24OkrpUwXWl96QO2fanQEMFVK+WwN+ywnPAiQruYSWku+ssoudxeFg4a/Tk1AoeumqokTfwe3LovrtVuT8yw0VEgpewohBqO1TN0Pn0NoBvd5YIvUuoYQQpRKKS+v8vkcKeXqKm2qB4H/oHmOFWjeQ23Xwq1SyvU1bP9VSildjZgsKeU2V/070AztZrTv7dZrR/MUpwG3A39UOVYU8IwQ4ia0h2mpECLJpeuAq8xUtNa9m6Ou7xXvqu9GNCOagHbuWwJXAJ8DSClP6kKvB1Ubd6Fohtbd8AxB+33noHkZT6F5NYFoDSg/YL4QwuHalu06Tk0NqppwAMvRDE6glPJgld/wctffJtf7EDQPeDguzxXNIDyBdv3tkVLuEUKkofVIDKL+jeKfXBouQDPqbqo2GqnyHX8FWgshPkDzuua5uuMjpJR/uT47Fa1xUSt/N0/Hg6uP3ozmIldlGa7WidAiyVqiudun2r4UuMW1vSvaD18fFqGND9xXRVN3tFZUtsvgDHG9r4mlwLVCG1MKBUbUUMb9UMl1td5vAJBSlgAZQohrq9R9Yj/0biCxynhAqKsfvoTjXgZUPy+DgVyX11D1vAxHu3FBa8ndIISIc+2LEkKc6jueLqeqcxnauQoSQgSjtZKXcYpz6NKfJoT4h+tYQgjR4ww17TpNTSee35pIdJWLdfXp4zp+lyplqhroJLQHxz6gG9pv0PMMv4/7uCc2Apyc2vhL4F2X7qrjGQ40IzoW2CGljEcz6JLqz56qvRHlVT4bADyJZnSWoj3s6vPM2gH0OGFsZSfawxSXBlweexgwF/je5d22RXvoHwF6o7XeY10aBFo32hBX2Q5SyhddxzypQVUL3+Hy7E/YLoDXqvRItJVSfunqeemM1i15DHAHnZzNJMvvgf8C86WUVRtP7kZjz6rf0WXce6AZvwfQPPQz4u9mdALdXQZoJ/WOGi6EjwGTqyX3PXCnlLKylu2fACFCiF1o3R4bqAeuFvx1wFDXoPMOtGi6OWiDxtuA0WjjTzV9fqNLxxa01uO6GsoUorX4tqPdOFXL3A486urGCwbWV+lOeV1KaUVrNX0gtIH9+Wg3+WKgs6vcTWh9xH1cx3kdrbsHtP7mQa7vdT3aGBhSi6R6Hq0ltNV13FMOllb5vR6g7sHcU9W5Ea2/eS3aWNYXUspNdZzDW4F7XN99B66BVbSH5ptVdPWhdmacpqY8tG6SwBO6tdz0BI6iGbIStGCGLWgtzoE1lAft9y1A63Lai3bd7a1D99lg4rgX7wMsl1Lmo7WIr6lSbh6a0ZkOTBZC9JRSFqF1j7m9tn+jeQ81IdAMRJmrzHC03+ohTwGti6gaUsr9aOfipSrd6/vQrsPrXZ8zo43B/A70xdVYEEI0QzufYUC5lPJrtDGmrmjGPBBXd+9ZNKiWoT0Lvj1h+1zgblcDEiFEMyFEfyHERVW0bHOdCzvQQQjRFu0+LkbrgqwXUspDaF2XH5+wq8ZGo6vXyCSl/BHt/u7t+i0LhBDu6/L2emmoaaBH/ak/9de4fxwPenH/vY42zrYLCHWVeRt4yfX6Dde+b6gSGFPleFPQ+uoXonWd3FlL3UvQPF133QuqHMMdoFKtjhP2lbq0bUd7+MW6tq9A64J80fU+BvgZzUtKBT51bb8ALXDEgmaUdnM8kGBflc8edNWbjvbg/wnNo5uKZoj2ANef4juGoTXI9rt0LkEL9FiA5sHtR+uS8kebylCO1i25B3gPLWhkq+v8lHM8QGeSS3cpWgPUHfhTeqrzfcJ5rymAo2ogwWNohmUbsArNiO901VmB1ri4lLMLJLihhu0HOR5IcJPre291f0c0L2cjx6+Z4a6yPTkeSDCbegQSqDQ4CoWi0RBaaP81Usrb9dbyd8I1FtxXSmnk9Xpq5G8bSKBQKPTFNeg8HC9MNlacOyhPR6H4myK0ycytTtj8tJRyrh56GgOhTeL1P2Hz7dIVdacHRjjvQoiPcE1CrcJ7UsrJNZX3JsroKBQKhcJr/N2i1xQKhUJhYJTRUSgUCoXXUEZHoWgEhBB3CiE+dL2uMfOvEOJaUceyBArF3w1ldBQK/biW2vOjKRR/O5TRUSjqiRBitNCWeNgihPjKtS1WCPGj0JaXWOeaPV6fY10IjOR49oM2QoiNVfa3c78X2sJw/ye0ZR7Wumahn3HdCoWeqHk6CkU9cOU9ex64UEqZK4SIcu16D20Ji+VCiJZoqUw61XU8KeVKIcQvwG9Syh9cdRS5UsVsRlv/pWp4a5GUspsQYjRanrOrz7RuhUJPlNFRKOrHJcBM9wxwqeUaA21Rrc7HU3wR5s6ddQZ8AdwlhBiHlork/Cr7vq3y/53a6pbaUgoKhSFRRkehODtMaPm3LFU3ipOWcaoXP6KtR7IIbe2cqhnSZQ2va6xboTAyakxHoagfi4B/CCGiQcu+69o+D3jEXUgcX+isPlRb5sBlPOaiZTY/ceb4TVX+r2qAuhUKXVBGR6GoB1LKHWhLMv/lWmrgbdeuR9GWqtgqhNiJtkRDffkOeEoIsUkI0ca17Ru0rMzzTigb6Voq4jG0VT3Ptm6FQhdUGhyFwkC45vOESyn/U2XbQc7RjMIKxYmoMR2FwiC4EkW2QQtaUCj+lihPR6FQKBReQ43pKBQKhcJrKKOjUCgUCq+hjI5CoVAovIYyOgqFQqHwGsroKBQKhcJrKKOjUCgUCq/x/wHnhgGkNbjA4gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 538.128x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.violin(e9,['KDR'],groupby='cell_type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 538.128x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.violin(e95_p,['KDR'],groupby='cell_type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\wubaosheng\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\anndata\\compat\\_overloaded_dict.py:106: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.\n",
      "  self.data[key] = value\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 538.128x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.violin(e95_a,['KDR'],groupby='cell_type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "endo_marker = ['SEMA6A','CALCRL','NRP2','TUBA1A','TGFBR3','APLNR','OIT3','TMEM204','PLPP3','VWF','SGK1','CRIM1','FLT4','ADGRL4','MYO1C','RARB','PLXND1','SHE','PLK2','FGD5','FLI1','FLT1','ASAP1','TEK','RGS3','TANC2','SPTBN1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(endo_marker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FGD5', 'SHE', 'TEK'}"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(R2).intersection(set(endo_marker))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mouse_atac=pd.read_csv('H:/002.singcell/01.liver/10.mutileSpecies/05.SAMap/014.mouse_liver_development/scACAT.txt',index_col=None,sep='\\t',header=None,names=['chr','UMI'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "mouse_atac['id']=mouse_atac['chr'].str.split(':',expand=True)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "mouse_atac['start']=mouse_atac['chr'].str.split(':',expand=True)[1].str.split('-',expand=True)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "mouse_atac['end']=mouse_atac['chr'].str.split(':',expand=True)[1].str.split('-',expand=True)[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "mouse_atac['length'] = mouse_atac['end'].astype(int) - mouse_atac['start'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "mouse_atac['TPM']=mouse_atac['UMI'].astype(float)/mouse_atac['length'].sum()*1000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "mouse_atac=mouse_atac[mouse_atac['TPM']<50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>chr</th>\n",
       "      <th>UMI</th>\n",
       "      <th>id</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>length</th>\n",
       "      <th>TPM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr10:100001532-100001667</td>\n",
       "      <td>47</td>\n",
       "      <td>chr10</td>\n",
       "      <td>100001532</td>\n",
       "      <td>100001667</td>\n",
       "      <td>135</td>\n",
       "      <td>0.332376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr10:100010109-100010715</td>\n",
       "      <td>30</td>\n",
       "      <td>chr10</td>\n",
       "      <td>100010109</td>\n",
       "      <td>100010715</td>\n",
       "      <td>606</td>\n",
       "      <td>0.212155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr10:100013826-100020189</td>\n",
       "      <td>1642</td>\n",
       "      <td>chr10</td>\n",
       "      <td>100013826</td>\n",
       "      <td>100020189</td>\n",
       "      <td>6363</td>\n",
       "      <td>11.611938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr10:100022150-100022719</td>\n",
       "      <td>15</td>\n",
       "      <td>chr10</td>\n",
       "      <td>100022150</td>\n",
       "      <td>100022719</td>\n",
       "      <td>569</td>\n",
       "      <td>0.106077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr10:100025384-100026163</td>\n",
       "      <td>185</td>\n",
       "      <td>chr10</td>\n",
       "      <td>100025384</td>\n",
       "      <td>100026163</td>\n",
       "      <td>779</td>\n",
       "      <td>1.308288</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162723</th>\n",
       "      <td>chrY:90803545-90803811</td>\n",
       "      <td>38</td>\n",
       "      <td>chrY</td>\n",
       "      <td>90803545</td>\n",
       "      <td>90803811</td>\n",
       "      <td>266</td>\n",
       "      <td>0.268729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162724</th>\n",
       "      <td>chrY:90804478-90805450</td>\n",
       "      <td>391</td>\n",
       "      <td>chrY</td>\n",
       "      <td>90804478</td>\n",
       "      <td>90805450</td>\n",
       "      <td>972</td>\n",
       "      <td>2.765084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162725</th>\n",
       "      <td>chrY:90807112-90809625</td>\n",
       "      <td>498</td>\n",
       "      <td>chrY</td>\n",
       "      <td>90807112</td>\n",
       "      <td>90809625</td>\n",
       "      <td>2513</td>\n",
       "      <td>3.521769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162726</th>\n",
       "      <td>chrY:90810389-90813288</td>\n",
       "      <td>424</td>\n",
       "      <td>chrY</td>\n",
       "      <td>90810389</td>\n",
       "      <td>90813288</td>\n",
       "      <td>2899</td>\n",
       "      <td>2.998454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162727</th>\n",
       "      <td>chrY:90828557-90829216</td>\n",
       "      <td>46</td>\n",
       "      <td>chrY</td>\n",
       "      <td>90828557</td>\n",
       "      <td>90829216</td>\n",
       "      <td>659</td>\n",
       "      <td>0.325304</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>162711 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              chr   UMI     id      start        end  length  \\\n",
       "0       chr10:100001532-100001667    47  chr10  100001532  100001667     135   \n",
       "1       chr10:100010109-100010715    30  chr10  100010109  100010715     606   \n",
       "2       chr10:100013826-100020189  1642  chr10  100013826  100020189    6363   \n",
       "3       chr10:100022150-100022719    15  chr10  100022150  100022719     569   \n",
       "4       chr10:100025384-100026163   185  chr10  100025384  100026163     779   \n",
       "...                           ...   ...    ...        ...        ...     ...   \n",
       "162723     chrY:90803545-90803811    38   chrY   90803545   90803811     266   \n",
       "162724     chrY:90804478-90805450   391   chrY   90804478   90805450     972   \n",
       "162725     chrY:90807112-90809625   498   chrY   90807112   90809625    2513   \n",
       "162726     chrY:90810389-90813288   424   chrY   90810389   90813288    2899   \n",
       "162727     chrY:90828557-90829216    46   chrY   90828557   90829216     659   \n",
       "\n",
       "              TPM  \n",
       "0        0.332376  \n",
       "1        0.212155  \n",
       "2       11.611938  \n",
       "3        0.106077  \n",
       "4        1.308288  \n",
       "...           ...  \n",
       "162723   0.268729  \n",
       "162724   2.765084  \n",
       "162725   3.521769  \n",
       "162726   2.998454  \n",
       "162727   0.325304  \n",
       "\n",
       "[162711 rows x 7 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mouse_atac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "blast=pd.read_csv('H:/002.singcell/01.liver/10.mutileSpecies/05.SAMap/014.mouse_liver_development/02.blastnParse.py.out',index_col=None,sep='\\t',header=None,names=['human','mouse'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "blast_dir = blast.set_index('human')['mouse'].to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "blast_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "mouse_atac_high = mouse_atac[mouse_atac['chr'].isin(blast['mouse'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chr</th>\n",
       "      <th>UMI</th>\n",
       "      <th>id</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>length</th>\n",
       "      <th>TPM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>chr10:102510952-102512958</td>\n",
       "      <td>448</td>\n",
       "      <td>chr10</td>\n",
       "      <td>102510952</td>\n",
       "      <td>102512958</td>\n",
       "      <td>2006</td>\n",
       "      <td>3.168178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>chr10:10392716-10392881</td>\n",
       "      <td>6</td>\n",
       "      <td>chr10</td>\n",
       "      <td>10392716</td>\n",
       "      <td>10392881</td>\n",
       "      <td>165</td>\n",
       "      <td>0.042431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>chr10:111502216-111512465</td>\n",
       "      <td>2850</td>\n",
       "      <td>chr10</td>\n",
       "      <td>111502216</td>\n",
       "      <td>111512465</td>\n",
       "      <td>10249</td>\n",
       "      <td>20.154704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>chr10:111574135-111577149</td>\n",
       "      <td>868</td>\n",
       "      <td>chr10</td>\n",
       "      <td>111574135</td>\n",
       "      <td>111577149</td>\n",
       "      <td>3014</td>\n",
       "      <td>6.138345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>chr10:116947851-116951390</td>\n",
       "      <td>1027</td>\n",
       "      <td>chr10</td>\n",
       "      <td>116947851</td>\n",
       "      <td>116951390</td>\n",
       "      <td>3539</td>\n",
       "      <td>7.262765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162369</th>\n",
       "      <td>chrX:7877432-7880492</td>\n",
       "      <td>996</td>\n",
       "      <td>chrX</td>\n",
       "      <td>7877432</td>\n",
       "      <td>7880492</td>\n",
       "      <td>3060</td>\n",
       "      <td>7.043539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162397</th>\n",
       "      <td>chrX:81070033-81073465</td>\n",
       "      <td>535</td>\n",
       "      <td>chrX</td>\n",
       "      <td>81070033</td>\n",
       "      <td>81073465</td>\n",
       "      <td>3432</td>\n",
       "      <td>3.783427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162631</th>\n",
       "      <td>chrX:99127111-99127794</td>\n",
       "      <td>19</td>\n",
       "      <td>chrX</td>\n",
       "      <td>99127111</td>\n",
       "      <td>99127794</td>\n",
       "      <td>683</td>\n",
       "      <td>0.134365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162633</th>\n",
       "      <td>chrX:99134748-99139765</td>\n",
       "      <td>1127</td>\n",
       "      <td>chrX</td>\n",
       "      <td>99134748</td>\n",
       "      <td>99139765</td>\n",
       "      <td>5017</td>\n",
       "      <td>7.969948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162636</th>\n",
       "      <td>chrX:99148076-99148694</td>\n",
       "      <td>42</td>\n",
       "      <td>chrX</td>\n",
       "      <td>99148076</td>\n",
       "      <td>99148694</td>\n",
       "      <td>618</td>\n",
       "      <td>0.297017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>956 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              chr   UMI     id      start        end  length  \\\n",
       "47      chr10:102510952-102512958   448  chr10  102510952  102512958    2006   \n",
       "85        chr10:10392716-10392881     6  chr10   10392716   10392881     165   \n",
       "409     chr10:111502216-111512465  2850  chr10  111502216  111512465   10249   \n",
       "425     chr10:111574135-111577149   868  chr10  111574135  111577149    3014   \n",
       "711     chr10:116947851-116951390  1027  chr10  116947851  116951390    3539   \n",
       "...                           ...   ...    ...        ...        ...     ...   \n",
       "162369       chrX:7877432-7880492   996   chrX    7877432    7880492    3060   \n",
       "162397     chrX:81070033-81073465   535   chrX   81070033   81073465    3432   \n",
       "162631     chrX:99127111-99127794    19   chrX   99127111   99127794     683   \n",
       "162633     chrX:99134748-99139765  1127   chrX   99134748   99139765    5017   \n",
       "162636     chrX:99148076-99148694    42   chrX   99148076   99148694     618   \n",
       "\n",
       "              TPM  \n",
       "47       3.168178  \n",
       "85       0.042431  \n",
       "409     20.154704  \n",
       "425      6.138345  \n",
       "711      7.262765  \n",
       "...           ...  \n",
       "162369   7.043539  \n",
       "162397   3.783427  \n",
       "162631   0.134365  \n",
       "162633   7.969948  \n",
       "162636   0.297017  \n",
       "\n",
       "[956 rows x 7 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mouse_atac_high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "mouse_atac_low = mouse_atac[~mouse_atac['chr'].isin(blast['mouse'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0994553605940556"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mouse_atac_low['TPM'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.532828634869218"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mouse_atac_high['TPM'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "mouse_atac['high_low'] = ['high' if x in mouse_atac_high.index else 'low' for x in mouse_atac.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chr</th>\n",
       "      <th>UMI</th>\n",
       "      <th>id</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr10:100001532-100001667</td>\n",
       "      <td>47</td>\n",
       "      <td>chr10</td>\n",
       "      <td>100001532</td>\n",
       "      <td>100001667</td>\n",
       "      <td>135</td>\n",
       "      <td>0.332376</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr10:100010109-100010715</td>\n",
       "      <td>30</td>\n",
       "      <td>chr10</td>\n",
       "      <td>100010109</td>\n",
       "      <td>100010715</td>\n",
       "      <td>606</td>\n",
       "      <td>0.212155</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr10:100013826-100020189</td>\n",
       "      <td>1642</td>\n",
       "      <td>chr10</td>\n",
       "      <td>100013826</td>\n",
       "      <td>100020189</td>\n",
       "      <td>6363</td>\n",
       "      <td>11.611938</td>\n",
       "      <td>low</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr10:100022150-100022719</td>\n",
       "      <td>15</td>\n",
       "      <td>chr10</td>\n",
       "      <td>100022150</td>\n",
       "      <td>100022719</td>\n",
       "      <td>569</td>\n",
       "      <td>0.106077</td>\n",
       "      <td>low</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr10:100025384-100026163</td>\n",
       "      <td>185</td>\n",
       "      <td>chr10</td>\n",
       "      <td>100025384</td>\n",
       "      <td>100026163</td>\n",
       "      <td>779</td>\n",
       "      <td>1.308288</td>\n",
       "      <td>low</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>162723</th>\n",
       "      <td>chrY:90803545-90803811</td>\n",
       "      <td>38</td>\n",
       "      <td>chrY</td>\n",
       "      <td>90803545</td>\n",
       "      <td>90803811</td>\n",
       "      <td>266</td>\n",
       "      <td>0.268729</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162724</th>\n",
       "      <td>chrY:90804478-90805450</td>\n",
       "      <td>391</td>\n",
       "      <td>chrY</td>\n",
       "      <td>90804478</td>\n",
       "      <td>90805450</td>\n",
       "      <td>972</td>\n",
       "      <td>2.765084</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162725</th>\n",
       "      <td>chrY:90807112-90809625</td>\n",
       "      <td>498</td>\n",
       "      <td>chrY</td>\n",
       "      <td>90807112</td>\n",
       "      <td>90809625</td>\n",
       "      <td>2513</td>\n",
       "      <td>3.521769</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162726</th>\n",
       "      <td>chrY:90810389-90813288</td>\n",
       "      <td>424</td>\n",
       "      <td>chrY</td>\n",
       "      <td>90810389</td>\n",
       "      <td>90813288</td>\n",
       "      <td>2899</td>\n",
       "      <td>2.998454</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162727</th>\n",
       "      <td>chrY:90828557-90829216</td>\n",
       "      <td>46</td>\n",
       "      <td>chrY</td>\n",
       "      <td>90828557</td>\n",
       "      <td>90829216</td>\n",
       "      <td>659</td>\n",
       "      <td>0.325304</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>161755 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              chr   UMI     id      start        end  length  \\\n",
       "0       chr10:100001532-100001667    47  chr10  100001532  100001667     135   \n",
       "1       chr10:100010109-100010715    30  chr10  100010109  100010715     606   \n",
       "2       chr10:100013826-100020189  1642  chr10  100013826  100020189    6363   \n",
       "3       chr10:100022150-100022719    15  chr10  100022150  100022719     569   \n",
       "4       chr10:100025384-100026163   185  chr10  100025384  100026163     779   \n",
       "...                           ...   ...    ...        ...        ...     ...   \n",
       "162723     chrY:90803545-90803811    38   chrY   90803545   90803811     266   \n",
       "162724     chrY:90804478-90805450   391   chrY   90804478   90805450     972   \n",
       "162725     chrY:90807112-90809625   498   chrY   90807112   90809625    2513   \n",
       "162726     chrY:90810389-90813288   424   chrY   90810389   90813288    2899   \n",
       "162727     chrY:90828557-90829216    46   chrY   90828557   90829216     659   \n",
       "\n",
       "              TPM high_low  \n",
       "0        0.332376      low  \n",
       "1        0.212155      low  \n",
       "2       11.611938      low  \n",
       "3        0.106077      low  \n",
       "4        1.308288      low  \n",
       "...           ...      ...  \n",
       "162723   0.268729      low  \n",
       "162724   2.765084      low  \n",
       "162725   3.521769      low  \n",
       "162726   2.998454      low  \n",
       "162727   0.325304      low  \n",
       "\n",
       "[161755 rows x 8 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mouse_atac[mouse_atac['high_low']=='low']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='high_low', ylabel='TPM'>"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "# ax=sns.boxplot(x=\"high_low\", y=\"TPM\", data=mouse_atac,hue='high_low',dodge=False)\n",
    "# sns.stripplot(x='high_low', y='TPM', data=mouse_atac, color='black', size=1, ax=ax)\n",
    "sns.boxplot(\n",
    "    data=mouse_atac, x=\"high_low\", y=\"TPM\",\n",
    "    # notch=True, showcaps=False,\n",
    "    flierprops={\"marker\": \".\", \"markersize\": 5},\n",
    "    boxprops={\"facecolor\": (.4, .6, .8, .5)},\n",
    "    medianprops={\"color\": \"coral\"},\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(data=mouse_atac, x=\"high_low\", y=\"TPM\",scale=\"width\", inner=\"box\", cut=0, linewidth=0.5, palette=\"Set2\")\n",
    "plt.savefig(\"H:/002.singcell/01.liver/10.mutileSpecies/05.SAMap/014.mouse_liver_development/mouse_development_atac.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=66.65973389123154, pvalue=0.0)"
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### t test\n",
    "from scipy.stats import ttest_ind\n",
    "ttest_ind(mouse_atac[mouse_atac['high_low']=='high']['TPM'],mouse_atac[mouse_atac['high_low']=='low']['TPM'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>chr</th>\n",
       "      <th>UMI</th>\n",
       "      <th>id</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>length</th>\n",
       "      <th>TPM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>chr10:102510952-102512958</td>\n",
       "      <td>448</td>\n",
       "      <td>chr10</td>\n",
       "      <td>102510952</td>\n",
       "      <td>102512958</td>\n",
       "      <td>2006</td>\n",
       "      <td>3.168178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>chr10:10392716-10392881</td>\n",
       "      <td>6</td>\n",
       "      <td>chr10</td>\n",
       "      <td>10392716</td>\n",
       "      <td>10392881</td>\n",
       "      <td>165</td>\n",
       "      <td>0.042431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>chr10:111502216-111512465</td>\n",
       "      <td>2850</td>\n",
       "      <td>chr10</td>\n",
       "      <td>111502216</td>\n",
       "      <td>111512465</td>\n",
       "      <td>10249</td>\n",
       "      <td>20.154704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>chr10:111574135-111577149</td>\n",
       "      <td>868</td>\n",
       "      <td>chr10</td>\n",
       "      <td>111574135</td>\n",
       "      <td>111577149</td>\n",
       "      <td>3014</td>\n",
       "      <td>6.138345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>chr10:116947851-116951390</td>\n",
       "      <td>1027</td>\n",
       "      <td>chr10</td>\n",
       "      <td>116947851</td>\n",
       "      <td>116951390</td>\n",
       "      <td>3539</td>\n",
       "      <td>7.262765</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162369</th>\n",
       "      <td>chrX:7877432-7880492</td>\n",
       "      <td>996</td>\n",
       "      <td>chrX</td>\n",
       "      <td>7877432</td>\n",
       "      <td>7880492</td>\n",
       "      <td>3060</td>\n",
       "      <td>7.043539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162397</th>\n",
       "      <td>chrX:81070033-81073465</td>\n",
       "      <td>535</td>\n",
       "      <td>chrX</td>\n",
       "      <td>81070033</td>\n",
       "      <td>81073465</td>\n",
       "      <td>3432</td>\n",
       "      <td>3.783427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162631</th>\n",
       "      <td>chrX:99127111-99127794</td>\n",
       "      <td>19</td>\n",
       "      <td>chrX</td>\n",
       "      <td>99127111</td>\n",
       "      <td>99127794</td>\n",
       "      <td>683</td>\n",
       "      <td>0.134365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162633</th>\n",
       "      <td>chrX:99134748-99139765</td>\n",
       "      <td>1127</td>\n",
       "      <td>chrX</td>\n",
       "      <td>99134748</td>\n",
       "      <td>99139765</td>\n",
       "      <td>5017</td>\n",
       "      <td>7.969948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162636</th>\n",
       "      <td>chrX:99148076-99148694</td>\n",
       "      <td>42</td>\n",
       "      <td>chrX</td>\n",
       "      <td>99148076</td>\n",
       "      <td>99148694</td>\n",
       "      <td>618</td>\n",
       "      <td>0.297017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>956 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              chr   UMI     id      start        end  length  \\\n",
       "47      chr10:102510952-102512958   448  chr10  102510952  102512958    2006   \n",
       "85        chr10:10392716-10392881     6  chr10   10392716   10392881     165   \n",
       "409     chr10:111502216-111512465  2850  chr10  111502216  111512465   10249   \n",
       "425     chr10:111574135-111577149   868  chr10  111574135  111577149    3014   \n",
       "711     chr10:116947851-116951390  1027  chr10  116947851  116951390    3539   \n",
       "...                           ...   ...    ...        ...        ...     ...   \n",
       "162369       chrX:7877432-7880492   996   chrX    7877432    7880492    3060   \n",
       "162397     chrX:81070033-81073465   535   chrX   81070033   81073465    3432   \n",
       "162631     chrX:99127111-99127794    19   chrX   99127111   99127794     683   \n",
       "162633     chrX:99134748-99139765  1127   chrX   99134748   99139765    5017   \n",
       "162636     chrX:99148076-99148694    42   chrX   99148076   99148694     618   \n",
       "\n",
       "              TPM  \n",
       "47       3.168178  \n",
       "85       0.042431  \n",
       "409     20.154704  \n",
       "425      6.138345  \n",
       "711      7.262765  \n",
       "...           ...  \n",
       "162369   7.043539  \n",
       "162397   3.783427  \n",
       "162631   0.134365  \n",
       "162633   7.969948  \n",
       "162636   0.297017  \n",
       "\n",
       "[956 rows x 7 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mouse_atac_high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fasta2dic(fasta):\n",
    "    import os,re,sys\n",
    "    from Bio import SeqIO\n",
    "    from Bio.Seq import Seq\n",
    "    import pandas as pd\n",
    "    human_dir={}\n",
    "    records = SeqIO.parse(fasta, \"fasta\")\n",
    "    for record in records:\n",
    "        id = record.id\n",
    "        seq = record.seq\n",
    "        human_dir[id] = seq\n",
    "    return human_dir\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "enhancer = fasta2dic('H:/002.singcell/01.liver/10.mutileSpecies/05.SAMap/014.mouse_liver_development/yangmo.origin.fa')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the number of enhancers that are conserved is 146\n"
     ]
    }
   ],
   "source": [
    "counter = 0\n",
    "target =[]\n",
    "for x in enhancer.keys():\n",
    "    if x in blast['human'].tolist():\n",
    "        counter += 1\n",
    "        target.append(x)\n",
    "print(f'the number of enhancers that are conserved is {counter}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_human_liver_enhancer = pd.read_csv('H:/002.singcell/01.liver/10.mutileSpecies/05.SAMap/015.enhancer/AllPredictions.AvgHiC.ABC0.015.minus150.ForABCPaperV3.txt',sep='\\t',index_col=0)\n",
    "df_human_liver_enhancer = df_human_liver_enhancer[df_human_liver_enhancer['CellType']=='liver-ENCODE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>start</th>\n",
       "      <th>end</th>\n",
       "      <th>name</th>\n",
       "      <th>class</th>\n",
       "      <th>activity_base</th>\n",
       "      <th>TargetGene</th>\n",
       "      <th>TargetGeneTSS</th>\n",
       "      <th>TargetGeneExpression</th>\n",
       "      <th>TargetGenePromoterActivityQuantile</th>\n",
       "      <th>TargetGeneIsExpressed</th>\n",
       "      <th>...</th>\n",
       "      <th>powerlaw_contact</th>\n",
       "      <th>powerlaw_contact_reference</th>\n",
       "      <th>hic_contact_pl_scaled</th>\n",
       "      <th>hic_pseudocount</th>\n",
       "      <th>hic_contact_pl_scaled_adj</th>\n",
       "      <th>ABC.Score.Numerator</th>\n",
       "      <th>ABC.Score</th>\n",
       "      <th>powerlaw.Score.Numerator</th>\n",
       "      <th>powerlaw.Score</th>\n",
       "      <th>CellType</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>chr1</th>\n",
       "      <td>1208114</td>\n",
       "      <td>1208523</td>\n",
       "      <td>genic|chr1:1207964-1208673</td>\n",
       "      <td>genic</td>\n",
       "      <td>4.833947</td>\n",
       "      <td>UBE2J2</td>\n",
       "      <td>1209234</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.734602</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>0.125372</td>\n",
       "      <td>0.123436</td>\n",
       "      <td>0.048468</td>\n",
       "      <td>0.001216</td>\n",
       "      <td>0.049684</td>\n",
       "      <td>0.240169</td>\n",
       "      <td>0.064867</td>\n",
       "      <td>0.596683</td>\n",
       "      <td>0.086196</td>\n",
       "      <td>liver-ENCODE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr1</th>\n",
       "      <td>1209080</td>\n",
       "      <td>1209611</td>\n",
       "      <td>promoter|chr1:1208930-1209761</td>\n",
       "      <td>promoter</td>\n",
       "      <td>7.759225</td>\n",
       "      <td>UBE2J2</td>\n",
       "      <td>1209234</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.734602</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>0.125372</td>\n",
       "      <td>0.123436</td>\n",
       "      <td>0.048468</td>\n",
       "      <td>0.001216</td>\n",
       "      <td>0.049684</td>\n",
       "      <td>0.385508</td>\n",
       "      <td>0.104122</td>\n",
       "      <td>0.957768</td>\n",
       "      <td>0.138357</td>\n",
       "      <td>liver-ENCODE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr1</th>\n",
       "      <td>1217079</td>\n",
       "      <td>1217589</td>\n",
       "      <td>genic|chr1:1216929-1217739</td>\n",
       "      <td>genic</td>\n",
       "      <td>11.350953</td>\n",
       "      <td>UBE2J2</td>\n",
       "      <td>1209234</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.734602</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>0.082206</td>\n",
       "      <td>0.081132</td>\n",
       "      <td>0.018883</td>\n",
       "      <td>0.001216</td>\n",
       "      <td>0.020099</td>\n",
       "      <td>0.228139</td>\n",
       "      <td>0.061618</td>\n",
       "      <td>0.920927</td>\n",
       "      <td>0.133035</td>\n",
       "      <td>liver-ENCODE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr1</th>\n",
       "      <td>1684450</td>\n",
       "      <td>1685073</td>\n",
       "      <td>genic|chr1:1684300-1685223</td>\n",
       "      <td>genic</td>\n",
       "      <td>5.394758</td>\n",
       "      <td>NADK</td>\n",
       "      <td>1709909</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.872739</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030509</td>\n",
       "      <td>0.030281</td>\n",
       "      <td>0.012373</td>\n",
       "      <td>0.001216</td>\n",
       "      <td>0.013588</td>\n",
       "      <td>0.073306</td>\n",
       "      <td>0.018105</td>\n",
       "      <td>0.163361</td>\n",
       "      <td>0.018287</td>\n",
       "      <td>liver-ENCODE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr1</th>\n",
       "      <td>1689561</td>\n",
       "      <td>1689761</td>\n",
       "      <td>genic|chr1:1689411-1689911</td>\n",
       "      <td>genic</td>\n",
       "      <td>9.396646</td>\n",
       "      <td>NADK</td>\n",
       "      <td>1709909</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.872739</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>0.036878</td>\n",
       "      <td>0.036564</td>\n",
       "      <td>0.014171</td>\n",
       "      <td>0.001216</td>\n",
       "      <td>0.015387</td>\n",
       "      <td>0.144587</td>\n",
       "      <td>0.035709</td>\n",
       "      <td>0.343576</td>\n",
       "      <td>0.038460</td>\n",
       "      <td>liver-ENCODE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrX</th>\n",
       "      <td>153769512</td>\n",
       "      <td>153769712</td>\n",
       "      <td>genic|chrX:153769362-153769862</td>\n",
       "      <td>genic</td>\n",
       "      <td>7.078828</td>\n",
       "      <td>SLC10A3</td>\n",
       "      <td>153719029</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.749756</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016553</td>\n",
       "      <td>0.016487</td>\n",
       "      <td>0.006585</td>\n",
       "      <td>0.001216</td>\n",
       "      <td>0.007801</td>\n",
       "      <td>0.055220</td>\n",
       "      <td>0.020675</td>\n",
       "      <td>0.116706</td>\n",
       "      <td>0.022449</td>\n",
       "      <td>liver-ENCODE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrX</th>\n",
       "      <td>153770112</td>\n",
       "      <td>153770312</td>\n",
       "      <td>genic|chrX:153769962-153770462</td>\n",
       "      <td>genic</td>\n",
       "      <td>7.777634</td>\n",
       "      <td>SLC10A3</td>\n",
       "      <td>153719029</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.749756</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016383</td>\n",
       "      <td>0.016318</td>\n",
       "      <td>0.005539</td>\n",
       "      <td>0.001216</td>\n",
       "      <td>0.006755</td>\n",
       "      <td>0.052536</td>\n",
       "      <td>0.019670</td>\n",
       "      <td>0.126918</td>\n",
       "      <td>0.024413</td>\n",
       "      <td>liver-ENCODE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrX</th>\n",
       "      <td>153770892</td>\n",
       "      <td>153771374</td>\n",
       "      <td>genic|chrX:153770742-153771524</td>\n",
       "      <td>genic</td>\n",
       "      <td>12.060128</td>\n",
       "      <td>MPP1</td>\n",
       "      <td>154033802</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.587950</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003916</td>\n",
       "      <td>0.003933</td>\n",
       "      <td>0.001505</td>\n",
       "      <td>0.001216</td>\n",
       "      <td>0.002721</td>\n",
       "      <td>0.032816</td>\n",
       "      <td>0.020757</td>\n",
       "      <td>0.047434</td>\n",
       "      <td>0.016525</td>\n",
       "      <td>liver-ENCODE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrX</th>\n",
       "      <td>154027592</td>\n",
       "      <td>154028214</td>\n",
       "      <td>genic|chrX:154027442-154028364</td>\n",
       "      <td>genic</td>\n",
       "      <td>8.020224</td>\n",
       "      <td>MPP1</td>\n",
       "      <td>154033802</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.587950</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>0.108488</td>\n",
       "      <td>0.106901</td>\n",
       "      <td>0.036832</td>\n",
       "      <td>0.001216</td>\n",
       "      <td>0.038048</td>\n",
       "      <td>0.305156</td>\n",
       "      <td>0.193020</td>\n",
       "      <td>0.857367</td>\n",
       "      <td>0.298685</td>\n",
       "      <td>liver-ENCODE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrX</th>\n",
       "      <td>154033355</td>\n",
       "      <td>154034098</td>\n",
       "      <td>promoter|chrX:154033205-154034248</td>\n",
       "      <td>promoter</td>\n",
       "      <td>4.901057</td>\n",
       "      <td>MPP1</td>\n",
       "      <td>154033802</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.587950</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>0.125372</td>\n",
       "      <td>0.123436</td>\n",
       "      <td>0.036802</td>\n",
       "      <td>0.001216</td>\n",
       "      <td>0.038018</td>\n",
       "      <td>0.186328</td>\n",
       "      <td>0.117857</td>\n",
       "      <td>0.604967</td>\n",
       "      <td>0.210756</td>\n",
       "      <td>liver-ENCODE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4713 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          start        end                               name     class  \\\n",
       "chr                                                                       \n",
       "chr1    1208114    1208523         genic|chr1:1207964-1208673     genic   \n",
       "chr1    1209080    1209611      promoter|chr1:1208930-1209761  promoter   \n",
       "chr1    1217079    1217589         genic|chr1:1216929-1217739     genic   \n",
       "chr1    1684450    1685073         genic|chr1:1684300-1685223     genic   \n",
       "chr1    1689561    1689761         genic|chr1:1689411-1689911     genic   \n",
       "...         ...        ...                                ...       ...   \n",
       "chrX  153769512  153769712     genic|chrX:153769362-153769862     genic   \n",
       "chrX  153770112  153770312     genic|chrX:153769962-153770462     genic   \n",
       "chrX  153770892  153771374     genic|chrX:153770742-153771524     genic   \n",
       "chrX  154027592  154028214     genic|chrX:154027442-154028364     genic   \n",
       "chrX  154033355  154034098  promoter|chrX:154033205-154034248  promoter   \n",
       "\n",
       "      activity_base TargetGene  TargetGeneTSS  TargetGeneExpression  \\\n",
       "chr                                                                   \n",
       "chr1       4.833947     UBE2J2        1209234                   NaN   \n",
       "chr1       7.759225     UBE2J2        1209234                   NaN   \n",
       "chr1      11.350953     UBE2J2        1209234                   NaN   \n",
       "chr1       5.394758       NADK        1709909                   NaN   \n",
       "chr1       9.396646       NADK        1709909                   NaN   \n",
       "...             ...        ...            ...                   ...   \n",
       "chrX       7.078828    SLC10A3      153719029                   NaN   \n",
       "chrX       7.777634    SLC10A3      153719029                   NaN   \n",
       "chrX      12.060128       MPP1      154033802                   NaN   \n",
       "chrX       8.020224       MPP1      154033802                   NaN   \n",
       "chrX       4.901057       MPP1      154033802                   NaN   \n",
       "\n",
       "      TargetGenePromoterActivityQuantile  TargetGeneIsExpressed  ...  \\\n",
       "chr                                                              ...   \n",
       "chr1                            0.734602                   True  ...   \n",
       "chr1                            0.734602                   True  ...   \n",
       "chr1                            0.734602                   True  ...   \n",
       "chr1                            0.872739                   True  ...   \n",
       "chr1                            0.872739                   True  ...   \n",
       "...                                  ...                    ...  ...   \n",
       "chrX                            0.749756                   True  ...   \n",
       "chrX                            0.749756                   True  ...   \n",
       "chrX                            0.587950                   True  ...   \n",
       "chrX                            0.587950                   True  ...   \n",
       "chrX                            0.587950                   True  ...   \n",
       "\n",
       "      powerlaw_contact  powerlaw_contact_reference  hic_contact_pl_scaled  \\\n",
       "chr                                                                         \n",
       "chr1          0.125372                    0.123436               0.048468   \n",
       "chr1          0.125372                    0.123436               0.048468   \n",
       "chr1          0.082206                    0.081132               0.018883   \n",
       "chr1          0.030509                    0.030281               0.012373   \n",
       "chr1          0.036878                    0.036564               0.014171   \n",
       "...                ...                         ...                    ...   \n",
       "chrX          0.016553                    0.016487               0.006585   \n",
       "chrX          0.016383                    0.016318               0.005539   \n",
       "chrX          0.003916                    0.003933               0.001505   \n",
       "chrX          0.108488                    0.106901               0.036832   \n",
       "chrX          0.125372                    0.123436               0.036802   \n",
       "\n",
       "      hic_pseudocount  hic_contact_pl_scaled_adj  ABC.Score.Numerator  \\\n",
       "chr                                                                     \n",
       "chr1         0.001216                   0.049684             0.240169   \n",
       "chr1         0.001216                   0.049684             0.385508   \n",
       "chr1         0.001216                   0.020099             0.228139   \n",
       "chr1         0.001216                   0.013588             0.073306   \n",
       "chr1         0.001216                   0.015387             0.144587   \n",
       "...               ...                        ...                  ...   \n",
       "chrX         0.001216                   0.007801             0.055220   \n",
       "chrX         0.001216                   0.006755             0.052536   \n",
       "chrX         0.001216                   0.002721             0.032816   \n",
       "chrX         0.001216                   0.038048             0.305156   \n",
       "chrX         0.001216                   0.038018             0.186328   \n",
       "\n",
       "      ABC.Score  powerlaw.Score.Numerator  powerlaw.Score      CellType  \n",
       "chr                                                                      \n",
       "chr1   0.064867                  0.596683        0.086196  liver-ENCODE  \n",
       "chr1   0.104122                  0.957768        0.138357  liver-ENCODE  \n",
       "chr1   0.061618                  0.920927        0.133035  liver-ENCODE  \n",
       "chr1   0.018105                  0.163361        0.018287  liver-ENCODE  \n",
       "chr1   0.035709                  0.343576        0.038460  liver-ENCODE  \n",
       "...         ...                       ...             ...           ...  \n",
       "chrX   0.020675                  0.116706        0.022449  liver-ENCODE  \n",
       "chrX   0.019670                  0.126918        0.024413  liver-ENCODE  \n",
       "chrX   0.020757                  0.047434        0.016525  liver-ENCODE  \n",
       "chrX   0.193020                  0.857367        0.298685  liver-ENCODE  \n",
       "chrX   0.117857                  0.604967        0.210756  liver-ENCODE  \n",
       "\n",
       "[4713 rows x 23 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_human_liver_enhancer[df_human_liver_enhancer['TargetGene'].isin(R1)].drop_duplicates(subset=['name'],keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mouse_atac_high = df_human_liver_enhancer[df_human_liver_enhancer['name'].isin(blast['human'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mouse_atac_high=df_mouse_atac_high.drop_duplicates(subset=['name'],keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>name</th>\n",
       "      <th>class</th>\n",
       "      <th>activity_base</th>\n",
       "      <th>TargetGene</th>\n",
       "      <th>TargetGeneTSS</th>\n",
       "      <th>TargetGeneExpression</th>\n",
       "      <th>TargetGenePromoterActivityQuantile</th>\n",
       "      <th>TargetGeneIsExpressed</th>\n",
       "      <th>...</th>\n",
       "      <th>powerlaw_contact</th>\n",
       "      <th>powerlaw_contact_reference</th>\n",
       "      <th>hic_contact_pl_scaled</th>\n",
       "      <th>hic_pseudocount</th>\n",
       "      <th>hic_contact_pl_scaled_adj</th>\n",
       "      <th>ABC.Score.Numerator</th>\n",
       "      <th>ABC.Score</th>\n",
       "      <th>powerlaw.Score.Numerator</th>\n",
       "      <th>powerlaw.Score</th>\n",
       "      <th>CellType</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>chr1</th>\n",
       "      <td>21671397</td>\n",
       "      <td>21672364</td>\n",
       "      <td>intergenic|chr1:21671247-21672514</td>\n",
       "      <td>intergenic</td>\n",
       "      <td>26.304502</td>\n",
       "      <td>C1QA</td>\n",
       "      <td>22962998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.411398</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000972</td>\n",
       "      <td>0.000984</td>\n",
       "      <td>0.000205</td>\n",
       "      <td>0.000972</td>\n",
       "      <td>0.001177</td>\n",
       "      <td>0.030965</td>\n",
       "      <td>0.015554</td>\n",
       "      <td>0.025889</td>\n",
       "      <td>0.010547</td>\n",
       "      <td>liver-ENCODE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr1</th>\n",
       "      <td>26606112</td>\n",
       "      <td>26606636</td>\n",
       "      <td>promoter|chr1:26605962-26606786</td>\n",
       "      <td>promoter</td>\n",
       "      <td>13.140121</td>\n",
       "      <td>SH3BGRL3</td>\n",
       "      <td>26606212</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.865977</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>0.125372</td>\n",
       "      <td>0.123436</td>\n",
       "      <td>0.042142</td>\n",
       "      <td>0.001216</td>\n",
       "      <td>0.043358</td>\n",
       "      <td>0.569724</td>\n",
       "      <td>0.158123</td>\n",
       "      <td>1.621965</td>\n",
       "      <td>0.246040</td>\n",
       "      <td>liver-ENCODE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr1</th>\n",
       "      <td>26946849</td>\n",
       "      <td>26947665</td>\n",
       "      <td>intergenic|chr1:26946699-26947815</td>\n",
       "      <td>intergenic</td>\n",
       "      <td>40.799530</td>\n",
       "      <td>PTAFR</td>\n",
       "      <td>28503455</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.436247</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000826</td>\n",
       "      <td>0.000837</td>\n",
       "      <td>0.000325</td>\n",
       "      <td>0.000826</td>\n",
       "      <td>0.001150</td>\n",
       "      <td>0.046930</td>\n",
       "      <td>0.019518</td>\n",
       "      <td>0.034133</td>\n",
       "      <td>0.009794</td>\n",
       "      <td>liver-ENCODE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr1</th>\n",
       "      <td>28498699</td>\n",
       "      <td>28498899</td>\n",
       "      <td>genic|chr1:28498549-28499049</td>\n",
       "      <td>genic</td>\n",
       "      <td>1.838767</td>\n",
       "      <td>PTAFR</td>\n",
       "      <td>28503455</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.436247</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>0.125372</td>\n",
       "      <td>0.123436</td>\n",
       "      <td>0.036465</td>\n",
       "      <td>0.001216</td>\n",
       "      <td>0.037680</td>\n",
       "      <td>0.069286</td>\n",
       "      <td>0.028815</td>\n",
       "      <td>0.226970</td>\n",
       "      <td>0.065126</td>\n",
       "      <td>liver-ENCODE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr1</th>\n",
       "      <td>31296977</td>\n",
       "      <td>31297510</td>\n",
       "      <td>intergenic|chr1:31296827-31297660</td>\n",
       "      <td>intergenic</td>\n",
       "      <td>14.425468</td>\n",
       "      <td>SDC3</td>\n",
       "      <td>31381480</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.509492</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010594</td>\n",
       "      <td>0.010579</td>\n",
       "      <td>0.007571</td>\n",
       "      <td>0.001216</td>\n",
       "      <td>0.008787</td>\n",
       "      <td>0.126761</td>\n",
       "      <td>0.047650</td>\n",
       "      <td>0.152602</td>\n",
       "      <td>0.039463</td>\n",
       "      <td>liver-ENCODE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrX</th>\n",
       "      <td>39968051</td>\n",
       "      <td>39968533</td>\n",
       "      <td>intergenic|chrX:39967901-39968683</td>\n",
       "      <td>intergenic</td>\n",
       "      <td>12.617401</td>\n",
       "      <td>SRPX</td>\n",
       "      <td>38080177</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.447654</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000697</td>\n",
       "      <td>0.000707</td>\n",
       "      <td>0.000113</td>\n",
       "      <td>0.000697</td>\n",
       "      <td>0.000810</td>\n",
       "      <td>0.010226</td>\n",
       "      <td>0.017051</td>\n",
       "      <td>0.008922</td>\n",
       "      <td>0.009132</td>\n",
       "      <td>liver-ENCODE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrX</th>\n",
       "      <td>49023441</td>\n",
       "      <td>49023641</td>\n",
       "      <td>genic|chrX:49023291-49023791</td>\n",
       "      <td>genic</td>\n",
       "      <td>6.256536</td>\n",
       "      <td>PLP2</td>\n",
       "      <td>49028183</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.512058</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>0.125372</td>\n",
       "      <td>0.123436</td>\n",
       "      <td>0.030186</td>\n",
       "      <td>0.001216</td>\n",
       "      <td>0.031401</td>\n",
       "      <td>0.196464</td>\n",
       "      <td>0.081982</td>\n",
       "      <td>0.772282</td>\n",
       "      <td>0.138158</td>\n",
       "      <td>liver-ENCODE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrX</th>\n",
       "      <td>70401856</td>\n",
       "      <td>70402056</td>\n",
       "      <td>intergenic|chrX:70401706-70402206</td>\n",
       "      <td>intergenic</td>\n",
       "      <td>6.368728</td>\n",
       "      <td>CXCR3</td>\n",
       "      <td>70838367</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.423212</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002512</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.000850</td>\n",
       "      <td>0.001216</td>\n",
       "      <td>0.002066</td>\n",
       "      <td>0.013157</td>\n",
       "      <td>0.015212</td>\n",
       "      <td>0.016105</td>\n",
       "      <td>0.008573</td>\n",
       "      <td>liver-ENCODE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrX</th>\n",
       "      <td>129091321</td>\n",
       "      <td>129091735</td>\n",
       "      <td>intergenic|chrX:129091171-129091885</td>\n",
       "      <td>intergenic</td>\n",
       "      <td>6.635338</td>\n",
       "      <td>SASH3</td>\n",
       "      <td>128913891</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.472747</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005515</td>\n",
       "      <td>0.005527</td>\n",
       "      <td>0.001168</td>\n",
       "      <td>0.001216</td>\n",
       "      <td>0.002384</td>\n",
       "      <td>0.015820</td>\n",
       "      <td>0.015115</td>\n",
       "      <td>0.036677</td>\n",
       "      <td>0.013241</td>\n",
       "      <td>liver-ENCODE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrX</th>\n",
       "      <td>135229458</td>\n",
       "      <td>135230013</td>\n",
       "      <td>promoter|chrX:135229308-135230163</td>\n",
       "      <td>promoter</td>\n",
       "      <td>2.571128</td>\n",
       "      <td>FHL1</td>\n",
       "      <td>135229558</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.507210</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>0.125372</td>\n",
       "      <td>0.123436</td>\n",
       "      <td>0.034564</td>\n",
       "      <td>0.001216</td>\n",
       "      <td>0.035780</td>\n",
       "      <td>0.091995</td>\n",
       "      <td>0.130602</td>\n",
       "      <td>0.317370</td>\n",
       "      <td>0.221601</td>\n",
       "      <td>liver-ENCODE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>240 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          start        end                                 name       class  \\\n",
       "chr                                                                           \n",
       "chr1   21671397   21672364    intergenic|chr1:21671247-21672514  intergenic   \n",
       "chr1   26606112   26606636      promoter|chr1:26605962-26606786    promoter   \n",
       "chr1   26946849   26947665    intergenic|chr1:26946699-26947815  intergenic   \n",
       "chr1   28498699   28498899         genic|chr1:28498549-28499049       genic   \n",
       "chr1   31296977   31297510    intergenic|chr1:31296827-31297660  intergenic   \n",
       "...         ...        ...                                  ...         ...   \n",
       "chrX   39968051   39968533    intergenic|chrX:39967901-39968683  intergenic   \n",
       "chrX   49023441   49023641         genic|chrX:49023291-49023791       genic   \n",
       "chrX   70401856   70402056    intergenic|chrX:70401706-70402206  intergenic   \n",
       "chrX  129091321  129091735  intergenic|chrX:129091171-129091885  intergenic   \n",
       "chrX  135229458  135230013    promoter|chrX:135229308-135230163    promoter   \n",
       "\n",
       "      activity_base TargetGene  TargetGeneTSS  TargetGeneExpression  \\\n",
       "chr                                                                   \n",
       "chr1      26.304502       C1QA       22962998                   NaN   \n",
       "chr1      13.140121   SH3BGRL3       26606212                   NaN   \n",
       "chr1      40.799530      PTAFR       28503455                   NaN   \n",
       "chr1       1.838767      PTAFR       28503455                   NaN   \n",
       "chr1      14.425468       SDC3       31381480                   NaN   \n",
       "...             ...        ...            ...                   ...   \n",
       "chrX      12.617401       SRPX       38080177                   NaN   \n",
       "chrX       6.256536       PLP2       49028183                   NaN   \n",
       "chrX       6.368728      CXCR3       70838367                   NaN   \n",
       "chrX       6.635338      SASH3      128913891                   NaN   \n",
       "chrX       2.571128       FHL1      135229558                   NaN   \n",
       "\n",
       "      TargetGenePromoterActivityQuantile  TargetGeneIsExpressed  ...  \\\n",
       "chr                                                              ...   \n",
       "chr1                            0.411398                   True  ...   \n",
       "chr1                            0.865977                   True  ...   \n",
       "chr1                            0.436247                   True  ...   \n",
       "chr1                            0.436247                   True  ...   \n",
       "chr1                            0.509492                   True  ...   \n",
       "...                                  ...                    ...  ...   \n",
       "chrX                            0.447654                   True  ...   \n",
       "chrX                            0.512058                   True  ...   \n",
       "chrX                            0.423212                   True  ...   \n",
       "chrX                            0.472747                   True  ...   \n",
       "chrX                            0.507210                   True  ...   \n",
       "\n",
       "      powerlaw_contact  powerlaw_contact_reference  hic_contact_pl_scaled  \\\n",
       "chr                                                                         \n",
       "chr1          0.000972                    0.000984               0.000205   \n",
       "chr1          0.125372                    0.123436               0.042142   \n",
       "chr1          0.000826                    0.000837               0.000325   \n",
       "chr1          0.125372                    0.123436               0.036465   \n",
       "chr1          0.010594                    0.010579               0.007571   \n",
       "...                ...                         ...                    ...   \n",
       "chrX          0.000697                    0.000707               0.000113   \n",
       "chrX          0.125372                    0.123436               0.030186   \n",
       "chrX          0.002512                    0.002529               0.000850   \n",
       "chrX          0.005515                    0.005527               0.001168   \n",
       "chrX          0.125372                    0.123436               0.034564   \n",
       "\n",
       "      hic_pseudocount  hic_contact_pl_scaled_adj  ABC.Score.Numerator  \\\n",
       "chr                                                                     \n",
       "chr1         0.000972                   0.001177             0.030965   \n",
       "chr1         0.001216                   0.043358             0.569724   \n",
       "chr1         0.000826                   0.001150             0.046930   \n",
       "chr1         0.001216                   0.037680             0.069286   \n",
       "chr1         0.001216                   0.008787             0.126761   \n",
       "...               ...                        ...                  ...   \n",
       "chrX         0.000697                   0.000810             0.010226   \n",
       "chrX         0.001216                   0.031401             0.196464   \n",
       "chrX         0.001216                   0.002066             0.013157   \n",
       "chrX         0.001216                   0.002384             0.015820   \n",
       "chrX         0.001216                   0.035780             0.091995   \n",
       "\n",
       "      ABC.Score  powerlaw.Score.Numerator  powerlaw.Score      CellType  \n",
       "chr                                                                      \n",
       "chr1   0.015554                  0.025889        0.010547  liver-ENCODE  \n",
       "chr1   0.158123                  1.621965        0.246040  liver-ENCODE  \n",
       "chr1   0.019518                  0.034133        0.009794  liver-ENCODE  \n",
       "chr1   0.028815                  0.226970        0.065126  liver-ENCODE  \n",
       "chr1   0.047650                  0.152602        0.039463  liver-ENCODE  \n",
       "...         ...                       ...             ...           ...  \n",
       "chrX   0.017051                  0.008922        0.009132  liver-ENCODE  \n",
       "chrX   0.081982                  0.772282        0.138158  liver-ENCODE  \n",
       "chrX   0.015212                  0.016105        0.008573  liver-ENCODE  \n",
       "chrX   0.015115                  0.036677        0.013241  liver-ENCODE  \n",
       "chrX   0.130602                  0.317370        0.221601  liver-ENCODE  \n",
       "\n",
       "[240 rows x 23 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mouse_atac_high[df_mouse_atac_high['TargetGene'].isin(R2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "blast_dir = blast.set_index('mouse')['human'].to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\wubaosheng\\AppData\\Local\\Temp\\ipykernel_6636\\4061197566.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mouse_atac_high['human_chr']=mouse_atac_high['chr'].map(blast_dir)\n"
     ]
    }
   ],
   "source": [
    "mouse_atac_high['human_chr']=mouse_atac_high['chr'].map(blast_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_human_liver_enhancer[df_human_liver_enhancer['name'].isin(target)]['TargetGene'].to_csv('H:/002.singcell/01.liver/10.mutileSpecies/05.SAMap/015.enhancer/Fish_liver_enhancer_target_gene.txt',sep='\\t',index=False,header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_human_liver_enhancer=df_human_liver_enhancer.drop_duplicates(subset=['name'],keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_human_liver_enhancer_dir = df_human_liver_enhancer.set_index('name')['TargetGene'].to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\wubaosheng\\AppData\\Local\\Temp\\ipykernel_6636\\1899922543.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mouse_atac_high['targetGene']=mouse_atac_high['human_chr'].map(df_human_liver_enhancer_dir)\n"
     ]
    }
   ],
   "source": [
    "mouse_atac_high['targetGene']=mouse_atac_high['human_chr'].map(df_human_liver_enhancer_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "gene_info =[]\n",
    "for x in mouse_atac_high['targetGene']:\n",
    "    if x in R1:\n",
    "        gene_info.append('R1')\n",
    "    elif x in R2:\n",
    "        gene_info.append('R2')\n",
    "    else:\n",
    "        gene_info.append('ancient')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\wubaosheng\\AppData\\Local\\Temp\\ipykernel_6636\\2568889653.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mouse_atac_high['R1_R2']=gene_info\n"
     ]
    }
   ],
   "source": [
    "mouse_atac_high['R1_R2']=gene_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>chr</th>\n",
       "      <th>UMI</th>\n",
       "      <th>id</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>length</th>\n",
       "      <th>TPM</th>\n",
       "      <th>human_chr</th>\n",
       "      <th>targetGene</th>\n",
       "      <th>R1_R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>chr10:102510952-102512958</td>\n",
       "      <td>448</td>\n",
       "      <td>chr10</td>\n",
       "      <td>102510952</td>\n",
       "      <td>102512958</td>\n",
       "      <td>2006</td>\n",
       "      <td>3.168178</td>\n",
       "      <td>promoter|chr12:86229863-86230568</td>\n",
       "      <td>RASSF9</td>\n",
       "      <td>R1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>chr10:10392716-10392881</td>\n",
       "      <td>6</td>\n",
       "      <td>chr10</td>\n",
       "      <td>10392716</td>\n",
       "      <td>10392881</td>\n",
       "      <td>165</td>\n",
       "      <td>0.042431</td>\n",
       "      <td>genic|chr18:24723689-24724189</td>\n",
       "      <td>CHST9</td>\n",
       "      <td>R2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>chr10:111502216-111512465</td>\n",
       "      <td>2850</td>\n",
       "      <td>chr10</td>\n",
       "      <td>111502216</td>\n",
       "      <td>111512465</td>\n",
       "      <td>10249</td>\n",
       "      <td>20.154704</td>\n",
       "      <td>promoter|chr12:76425235-76425806</td>\n",
       "      <td>PHLDA1</td>\n",
       "      <td>R1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>chr10:111574135-111577149</td>\n",
       "      <td>868</td>\n",
       "      <td>chr10</td>\n",
       "      <td>111574135</td>\n",
       "      <td>111577149</td>\n",
       "      <td>3014</td>\n",
       "      <td>6.138345</td>\n",
       "      <td>intergenic|chr12:76337875-76338375</td>\n",
       "      <td>PHLDA1</td>\n",
       "      <td>R1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>chr10:116947851-116951390</td>\n",
       "      <td>1027</td>\n",
       "      <td>chr10</td>\n",
       "      <td>116947851</td>\n",
       "      <td>116951390</td>\n",
       "      <td>3539</td>\n",
       "      <td>7.262765</td>\n",
       "      <td>promoter|chr12:70132098-70132982</td>\n",
       "      <td>RAB3IP</td>\n",
       "      <td>ancient</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162369</th>\n",
       "      <td>chrX:7877432-7880492</td>\n",
       "      <td>996</td>\n",
       "      <td>chrX</td>\n",
       "      <td>7877432</td>\n",
       "      <td>7880492</td>\n",
       "      <td>3060</td>\n",
       "      <td>7.043539</td>\n",
       "      <td>promoter|chrX:48775948-48776924</td>\n",
       "      <td>PIM2</td>\n",
       "      <td>ancient</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162397</th>\n",
       "      <td>chrX:81070033-81073465</td>\n",
       "      <td>535</td>\n",
       "      <td>chrX</td>\n",
       "      <td>81070033</td>\n",
       "      <td>81073465</td>\n",
       "      <td>3432</td>\n",
       "      <td>3.783427</td>\n",
       "      <td>promoter|chrX:34674942-34675734</td>\n",
       "      <td>TMEM47</td>\n",
       "      <td>ancient</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162631</th>\n",
       "      <td>chrX:99127111-99127794</td>\n",
       "      <td>19</td>\n",
       "      <td>chrX</td>\n",
       "      <td>99127111</td>\n",
       "      <td>99127794</td>\n",
       "      <td>683</td>\n",
       "      <td>0.134365</td>\n",
       "      <td>intergenic|chrX:68040348-68040848</td>\n",
       "      <td>EFNB1</td>\n",
       "      <td>ancient</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162633</th>\n",
       "      <td>chrX:99134748-99139765</td>\n",
       "      <td>1127</td>\n",
       "      <td>chrX</td>\n",
       "      <td>99134748</td>\n",
       "      <td>99139765</td>\n",
       "      <td>5017</td>\n",
       "      <td>7.969948</td>\n",
       "      <td>promoter|chrX:68048451-68049202</td>\n",
       "      <td>EFNB1</td>\n",
       "      <td>ancient</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162636</th>\n",
       "      <td>chrX:99148076-99148694</td>\n",
       "      <td>42</td>\n",
       "      <td>chrX</td>\n",
       "      <td>99148076</td>\n",
       "      <td>99148694</td>\n",
       "      <td>618</td>\n",
       "      <td>0.297017</td>\n",
       "      <td>genic|chrX:68061385-68061885</td>\n",
       "      <td>EFNB1</td>\n",
       "      <td>ancient</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>956 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              chr   UMI     id      start        end  length  \\\n",
       "47      chr10:102510952-102512958   448  chr10  102510952  102512958    2006   \n",
       "85        chr10:10392716-10392881     6  chr10   10392716   10392881     165   \n",
       "409     chr10:111502216-111512465  2850  chr10  111502216  111512465   10249   \n",
       "425     chr10:111574135-111577149   868  chr10  111574135  111577149    3014   \n",
       "711     chr10:116947851-116951390  1027  chr10  116947851  116951390    3539   \n",
       "...                           ...   ...    ...        ...        ...     ...   \n",
       "162369       chrX:7877432-7880492   996   chrX    7877432    7880492    3060   \n",
       "162397     chrX:81070033-81073465   535   chrX   81070033   81073465    3432   \n",
       "162631     chrX:99127111-99127794    19   chrX   99127111   99127794     683   \n",
       "162633     chrX:99134748-99139765  1127   chrX   99134748   99139765    5017   \n",
       "162636     chrX:99148076-99148694    42   chrX   99148076   99148694     618   \n",
       "\n",
       "              TPM                           human_chr targetGene    R1_R2  \n",
       "47       3.168178    promoter|chr12:86229863-86230568     RASSF9       R1  \n",
       "85       0.042431       genic|chr18:24723689-24724189      CHST9       R2  \n",
       "409     20.154704    promoter|chr12:76425235-76425806     PHLDA1       R1  \n",
       "425      6.138345  intergenic|chr12:76337875-76338375     PHLDA1       R1  \n",
       "711      7.262765    promoter|chr12:70132098-70132982     RAB3IP  ancient  \n",
       "...           ...                                 ...        ...      ...  \n",
       "162369   7.043539     promoter|chrX:48775948-48776924       PIM2  ancient  \n",
       "162397   3.783427     promoter|chrX:34674942-34675734     TMEM47  ancient  \n",
       "162631   0.134365   intergenic|chrX:68040348-68040848      EFNB1  ancient  \n",
       "162633   7.969948     promoter|chrX:68048451-68049202      EFNB1  ancient  \n",
       "162636   0.297017        genic|chrX:68061385-68061885      EFNB1  ancient  \n",
       "\n",
       "[956 rows x 10 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mouse_atac_high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='R1_R2', ylabel='TPM'>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sns.violinplot(data=mouse_atac_high, x='R1_R2', y=\"TPM\", inner=\"box\", cut=0, linewidth=0.5, palette=\"Set2\")\n",
    "sns.boxplot(data=mouse_atac_high, x='R1_R2', y=\"TPM\", linewidth=0.5, palette=\"Set2\")\n",
    "# plt.savefig(\"H:/002.singcell/01.liver/10.mutileSpecies/05.SAMap/014.mouse_liver_development/mouse_development_atac.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "endothelia_marker=[\"ITIH2\",\"HABP2\",\"PAH\",\"HAO1\",\"F2\",\"ITIH3\",\"HAL\",\"F9\",\"VTN\",\"AMBP,\",\"SORD\",\"FGB\",\"KNG1\",\"HGD\",\"SERPIND1\",\"SERPINF2\",\"FGA\",\"HAAO\",\"CP\",\"PLG\",\"HPD\",\"C3\",\"F7\",\"TDO2\",\"F10\",\"FGG\",\"CFI\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(endothelia_marker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HABP2\n",
      "PAH\n",
      "HAO1\n",
      "F2\n",
      "HAL\n",
      "F9\n",
      "VTN\n",
      "AMBP,\n",
      "SORD\n",
      "FGB\n",
      "HGD\n",
      "SERPIND1\n",
      "FGA\n",
      "HAAO\n",
      "CP\n",
      "PLG\n",
      "HPD\n",
      "C3\n",
      "F7\n",
      "TDO2\n",
      "F10\n",
      "FGG\n",
      "CFI\n"
     ]
    }
   ],
   "source": [
    "for i in endothelia_marker:\n",
    "    if i not in set(endothelia_marker).intersection(set(R2)):\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ITIH2', 'ITIH3', 'KNG1', 'SERPINF2'}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(endothelia_marker).intersection(set(R2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}