019.high_variableGenes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!/usr/bin/env python\n",
    "# -*- encoding: utf-8 -*-\n",
    "'''\n",
    "@文件        :019.high_variableGenes.ipynb\n",
    "@说明        :\n",
    "@时间        :2023/08/17 18:52:06\n",
    "@作者        :Wu Baosheng\n",
    "@版本        :1.0\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scanpy as sc\n",
    "import scanpy.external as sce\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re,sys,os\n",
    "os.chdir('H:/002.singcell/01.liver/10.mutileSpecies/05.SAMap/01.h5ad_counts/count_new')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gene2Name():\n",
    "    dic1 = {}\n",
    "    path='H:/002.singcell/01.liver/10.mutileSpecies/05.SAMap/04.gene2names'\n",
    "    for i in os.listdir(path):\n",
    "        if i.endswith('.tsv'):\n",
    "            # dic1 = {}\n",
    "            sp = i.split('.')[0]\n",
    "            print(sp)\n",
    "            file =open(path+'/'+i,'r')\n",
    "            for line in file:\n",
    "                line = line.strip().split('\\t')\n",
    "                dic1[line[0]] = line[1]\n",
    "                # dic2[sp] = dic1\n",
    "            file.close()\n",
    "        else:\n",
    "            continue\n",
    "    return dic1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fasta2dict(fasta_file):\n",
    "    fasta = {}\n",
    "    with open(fasta_file) as f:\n",
    "        for line in f:\n",
    "            line = line.strip()\n",
    "            if line.startswith(\">\"):\n",
    "                name = line[1:]\n",
    "                fasta[name] = \"\"\n",
    "            else:\n",
    "                fasta[name] += line\n",
    "    return list(fasta.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "def readH5ad(adata_path):\n",
    "    adata = sc.read_h5ad(adata_path)\n",
    "    adata.X=adata.layers['counts']\n",
    "    sc.pp.normalize_total(adata,target_sum=1e6,exclude_highly_expressed=True)\n",
    "    sc.pp.log1p(adata)\n",
    "    sc.pp.highly_variable_genes(adata, flavor='seurat', n_top_genes=2000, inplace=True)\n",
    "    adata = adata[:, adata.var['highly_variable']]\n",
    "    high_variable_genes = adata.var.index.tolist()\n",
    "    return high_variable_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bichir\n",
      "chicken\n",
      "dogshark\n",
      "elephantshark\n",
      "frog\n",
      "Greenlizard\n",
      "hagfish\n",
      "human\n",
      "lamprey\n",
      "lancelet\n",
      "lungfish\n",
      "mouse\n"
     ]
    }
   ],
   "source": [
    "gene_name =gene2Name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "lancelet_high=readH5ad('H:/002.singcell/01.liver/01.lancelet/01.withoutSoupX/lancelet_counts.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lancelet_rbh =pd.read_csv('H:/002.singcell/01.liver/10.mutileSpecies/05.SAMap/08.CommonTrans/06.lancelet/bflancelet_lancelet.RBH.txt',sep='\\t',header=None,names=['bf','lancelet'],index_col=None)\n",
    "df_lancelet_rbh['bf']=df_lancelet_rbh['bf'].apply(lambda x:x.split('_')[0])\n",
    "df_lancelet_rbh['lancelet']=df_lancelet_rbh['lancelet'].astype('str')\n",
    "bf_lancelet_dic = df_lancelet_rbh.set_index('lancelet').to_dict()['bf']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in lancelet_high:\n",
    "    if i in bf_lancelet_dic.keys():\n",
    "        print(bf_lancelet_dic[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "lamprey_high=readH5ad('./lamprey_counts.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "lamprey_genes = pd.read_csv('H:/002.singcell/01.liver/10.mutileSpecies/08.WGD/05.newGenes/lamprey_dogshark.txt',header=None,names=['dogshark','lamprey'], index_col=None, sep='\\t')['lamprey'].to_list()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lamprey_genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(lamprey_high).intersection(set(lamprey_genes)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: adata.X seems to be already log-transformed.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogshark_high=readH5ad('H:/002.singcell/01.liver/03.dogshark/04.intergrate/dogshark_counts.h5ad')\n",
    "dogshark_genes = pd.read_csv('H:/002.singcell/01.liver/10.mutileSpecies/08.WGD/05.newGenes/lamprey_dogshark.txt',header=None,names=['dogshark','lamprey'], index_col=None, sep='\\t')['dogshark'].to_list()\n",
    "len(set(dogshark_high).intersection(set(dogshark_genes)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: adata.X seems to be already log-transformed.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bichir_high=readH5ad('H:/002.singcell/01.liver/08.bichir/bichir_counts.h5ad')\n",
    "bichir_genes = pd.read_csv('H:/002.singcell/01.liver/10.mutileSpecies/08.WGD/05.newGenes/lamprey_bichir.txt',header=None,names=['bichir','lamprey'], index_col=None, sep='\\t')['bichir'].to_list()\n",
    "len(set(bichir_high).intersection(set(bichir_genes)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: adata.X seems to be already log-transformed.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lungfish_high=readH5ad('H:/002.singcell/01.liver/04.lungfish/lungfish_counts.h5ad')\n",
    "lungfish_genes = pd.read_csv('H:/002.singcell/01.liver/10.mutileSpecies/08.WGD/05.newGenes/lamprey_lungfish.txt',header=None,names=['lungfish','lamprey'], index_col=None, sep='\\t')['lungfish'].to_list()\n",
    "len(set(lungfish_high).intersection(set(lungfish_genes)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: adata.X seems to be already log-transformed.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frog_high=readH5ad('H:/002.singcell/01.liver/05.frog/frog_counts.h5ad')\n",
    "frog_genes = pd.read_csv('H:/002.singcell/01.liver/10.mutileSpecies/08.WGD/05.newGenes/lamprey_frog.txt',header=None,names=['frog','lamprey'], index_col=None, sep='\\t')['frog'].to_list()\n",
    "len(set(frog_high).intersection(set(frog_genes)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: adata.X seems to be already log-transformed.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "human_high=readH5ad('H:/002.singcell/01.liver/07.Human/human_counts.h5ad')\n",
    "human_genes = pd.read_csv('H:/002.singcell/01.liver/10.mutileSpecies/08.WGD/05.newGenes/lamprey_human.txt',header=None,names=['human','lamprey'], index_col=None, sep='\\t')['human'].to_list()\n",
    "len(set(human_high).intersection(set(human_genes)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "human = pd.read_excel('H:/002.singcell/01.liver/10.mutileSpecies/08.WGD/05.newGenes/Retrogenes.xlsx',header=0,index_col=None,sheet_name='human',engine='openpyxl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CH25H'}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(human_high).intersection(set(human['genes'].to_list()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "high_valiable_genes = {'R1':[619,302,356,265,283,302],\n",
    "                       'R2':[0,396,586,486,413,497],\n",
    "                       'denovo':[6,8,1,10,9,4],\n",
    "                       'retrogenes':[0,0,0,0,0,1]}\n",
    "df_genes = pd.DataFrame(high_valiable_genes, index=['Lamprey','bamboo shark','Senegal bichir','African lungﬁsh','African bullfrog','Human'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_genes['other']=2000-(df_genes['R1'] + df_genes['R2']+df_genes['denovo']+df_genes['retrogenes'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### df_genes每一个值除以该行的和\n",
    "df_normalized = df_genes.div(df_genes.sum(axis=1), axis=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_normalized=df_normalized.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Lamprey</th>\n",
       "      <th>bamboo shark</th>\n",
       "      <th>Senegal bichir</th>\n",
       "      <th>African lungﬁsh</th>\n",
       "      <th>African bullfrog</th>\n",
       "      <th>Human</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>R1</th>\n",
       "      <td>0.3095</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.1780</td>\n",
       "      <td>0.1325</td>\n",
       "      <td>0.1415</td>\n",
       "      <td>0.1510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0.2930</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.2065</td>\n",
       "      <td>0.2485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>denovo</th>\n",
       "      <td>0.0030</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>0.0045</td>\n",
       "      <td>0.0020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retrogenes</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>0.6875</td>\n",
       "      <td>0.647</td>\n",
       "      <td>0.5285</td>\n",
       "      <td>0.6195</td>\n",
       "      <td>0.6475</td>\n",
       "      <td>0.5980</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Lamprey  bamboo shark  Senegal bichir  African lungﬁsh  \\\n",
       "R1           0.3095         0.151          0.1780           0.1325   \n",
       "R2           0.0000         0.198          0.2930           0.2430   \n",
       "denovo       0.0030         0.004          0.0005           0.0050   \n",
       "retrogenes   0.0000         0.000          0.0000           0.0000   \n",
       "other        0.6875         0.647          0.5285           0.6195   \n",
       "\n",
       "            African bullfrog   Human  \n",
       "R1                    0.1415  0.1510  \n",
       "R2                    0.2065  0.2485  \n",
       "denovo                0.0045  0.0020  \n",
       "retrogenes            0.0000  0.0005  \n",
       "other                 0.6475  0.5980  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_normalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import plotly.graph_objs as go\n",
    "import plotly.express as px\n",
    "mpl.rcParams['pdf.fonttype'] = 42\n",
    "mpl.rcParams['ps.fonttype'] = 42\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
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         "labels": [
          "R1",
          "R2",
          "denovo",
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         ],
         "name": "GHG Emissions",
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         "values": [
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         "labels": [
          "R1",
          "R2",
          "denovo",
          "retrogenes",
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         "name": "bamboo shark",
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         "values": [
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          0.198,
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        },
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         "hole": 0,
         "labels": [
          "R1",
          "R2",
          "denovo",
          "retrogenes",
          "other"
         ],
         "name": "Senegal bichir",
         "type": "pie",
         "values": [
          0.178,
          0.293,
          0.0005,
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        },
        {
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         "hole": 0,
         "labels": [
          "R1",
          "R2",
          "denovo",
          "retrogenes",
          "other"
         ],
         "name": "African lungﬁsh",
         "type": "pie",
         "values": [
          0.1325,
          0.243,
          0.005,
          0,
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        },
        {
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         "hole": 0,
         "labels": [
          "R1",
          "R2",
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         ],
         "name": "African bullfrog",
         "type": "pie",
         "values": [
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        },
        {
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         "hole": 0,
         "labels": [
          "R1",
          "R2",
          "denovo",
          "retrogenes",
          "other"
         ],
         "name": "Human",
         "type": "pie",
         "values": [
          0.151,
          0.2485,
          0.002,
          0.0005,
          0.598
         ]
        }
       ],
       "layout": {
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           "ticks": "",
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          }
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "fig = make_subplots(rows=1, cols=6, specs=[[{'type':'domain'}, {'type':'domain'},{'type':'domain'},{'type':'domain'},{'type':'domain'},{'type':'domain'}]])\n",
    "fig.add_trace(go.Pie(labels=df_normalized.index, values=df_normalized['Lamprey'],name=\"GHG Emissions\"),row=1, col=1)\n",
    "fig.add_trace(go.Pie(labels=df_normalized.index, values=df_normalized['bamboo shark'],name='bamboo shark'),row=1, col=2)\n",
    "fig.add_trace(go.Pie(labels=df_normalized.index, values=df_normalized['Senegal bichir'], name='Senegal bichir'), row=1, col=3)\n",
    "fig.add_trace(go.Pie(labels=df_normalized.index, values=df_normalized['African lungﬁsh'], name='African lungﬁsh'), row=1, col=4)\n",
    "fig.add_trace(go.Pie(labels=df_normalized.index, values=df_normalized['African bullfrog'], name='African bullfrog'), row=1, col=5)\n",
    "fig.add_trace(go.Pie(labels=df_normalized.index, values=df_normalized['Human'], name='Human'), row=1, col=6)\n",
    "# fig.update_traces(hole=0, hoverinfo=\"label+percent+name\")\n",
    "fig.update_traces(hole=0, hoverinfo=None)\n",
    "fig.show()\n",
    "fig.write_image('H:/002.singcell/01.liver/10.mutileSpecies/08.WGD/05.newGenes/new_genes.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_development = {'e8':[184,447,1,8],\n",
    "                  'e9':[200,402,2,10],\n",
    "                  'e95_p':[211,409,3,3],\n",
    "                  'e95a':[189,364,2,4]}\n",
    "df_development = pd.DataFrame(df_development, index=['R1','R2','retro','denovo']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_development['other'] = 2000-df_development.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_development=df_development.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>e8</th>\n",
       "      <th>e9</th>\n",
       "      <th>e95_p</th>\n",
       "      <th>e95a</th>\n",
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       "      <th>R1</th>\n",
       "      <td>184.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>189.0</td>\n",
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       "    <tr>\n",
       "      <th>R2</th>\n",
       "      <td>447.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>364.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retro</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>denovo</th>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>1360.0</td>\n",
       "      <td>1386.0</td>\n",
       "      <td>1374.0</td>\n",
       "      <td>1441.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "            e8      e9   e95_p    e95a\n",
       "R1       184.0   200.0   211.0   189.0\n",
       "R2       447.0   402.0   409.0   364.0\n",
       "retro      1.0     2.0     3.0     2.0\n",
       "denovo     8.0    10.0     3.0     4.0\n",
       "other   1360.0  1386.0  1374.0  1441.0"
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     "metadata": {},
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           "showlakes": true,
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          "hoverlabel": {
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          "hovermode": "closest",
          "mapbox": {
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          "polar": {
           "angularaxis": {
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           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
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          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
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            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
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            "zerolinecolor": "white"
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           "zaxis": {
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            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
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            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "fig = make_subplots(rows=1, cols=4, specs=[[{'type':'domain'}, {'type':'domain'},{'type':'domain'},{'type':'domain'}]])\n",
    "fig.add_trace(go.Pie(labels=df_development.index, values=df_development['e8'],name=\"e8\"),row=1, col=1)\n",
    "fig.add_trace(go.Pie(labels=df_development.index, values=df_development['e9'],name='e9'),row=1, col=2)\n",
    "fig.add_trace(go.Pie(labels=df_development.index, values=df_development['e95_p'], name='e95_p'), row=1, col=3)\n",
    "fig.add_trace(go.Pie(labels=df_development.index, values=df_development['e95a'], name='e95a'), row=1, col=4)\n",
    "# fig.update_traces(hole=0, hoverinfo=\"label+percent+name\")\n",
    "fig.update_traces(hole=0, hoverinfo=None)\n",
    "fig.show()\n",
    "fig.write_image('H:/002.singcell/01.liver/10.mutileSpecies/05.SAMap/014.mouse_liver_development/liver_development_new_genes.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_cne = {'before':[6],\n",
    "                  'vetebrate':[9],\n",
    "                  'Jawed':[155],\n",
    "                  'fish':[63],'Amniote':[146],'Mammalia':[577]}\n",
    "df_new_cne = pd.DataFrame(new_cne, index=['counts']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "from plotly.subplots import make_subplots\n",
    "fig = make_subplots(rows=1, cols=1, specs=[[{'type':'domain'}]])\n",
    "fig.add_trace(go.Pie(labels=df_new_cne.index, values=df_new_cne['counts'],name=\"counts\",pull=[0, 0.2, 0,0,0,0]),row=1, col=1)\n",
    "fig.write_image('H:/002.singcell/01.liver/10.mutileSpecies/05.SAMap/014.mouse_liver_development/liver_CNE.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "lamprey=sc.read('H:/002.singcell/01.liver/02.lampery/lamprey.integrate_cell_type.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAACCCAAGAAATTCG-1-liver1             Lymphoid cell\n",
       "AAACCCAAGATGACAT-1-liver1           Endothelia cell\n",
       "AAACCCAAGCCATCCG-1-liver1                Hepatocyte\n",
       "AAACCCAAGGGTCAAC-1-liver1           Endothelia cell\n",
       "AAACCCAAGTGCTCAT-1-liver1           Endothelia cell\n",
       "                                      ...          \n",
       "TTTGTTGCACTGATTG-1-liver2           Endothelia cell\n",
       "TTTGTTGGTATTAAGG-1-liver2    Hepatic stellate cells\n",
       "TTTGTTGGTCGACTTA-1-liver2                   unknocw\n",
       "TTTGTTGGTTCCTTGC-1-liver2           Endothelia cell\n",
       "TTTGTTGTCTTCTAAC-1-liver2           Endothelia cell\n",
       "Name: cell type, Length: 19073, dtype: category\n",
       "Categories (7, object): ['Cholangiocyte', 'Endothelia cell', 'Hepatic stellate cells', 'Hepatocyte', 'Lymphoid cell', 'Macrophage', 'unknocw']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lamprey.obs['cell type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 134.64x248.4 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.dotplot(lamprey,['MYC'],groupby='cell type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "high_all_genes = []\n",
    "for x in lamprey_high+dogshark_high+lungfish_high+bichir_high+frog_high+human_high:\n",
    "    if x in gene_name.keys():\n",
    "        high_all_genes.append(gene_name[x])\n",
    "vertebrate_high = []\n",
    "for x in set(high_all_genes):\n",
    "    if high_all_genes.count(x) >= 3:\n",
    "        vertebrate_high.append(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "739"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(vertebrate_high)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "high_all_genes = []\n",
    "for x in lamprey_high+dogshark_high+lungfish_high+bichir_high+frog_high+human_high:\n",
    "    if x in gene_name.keys():\n",
    "        high_all_genes.append(gene_name[x])\n",
    "vertebrate_high2 = []\n",
    "for x in set(high_all_genes):\n",
    "    if high_all_genes.count(x) >= 6:\n",
    "        vertebrate_high2.append(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(vertebrate_high2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in vertebrate_high:\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "human_2R = pd.read_csv('H:/002.singcell/01.liver/10.mutileSpecies/05.SAMap/017. HVGs/human.Pairs.Intermediate.2R.txt',sep='\\t',header=0,index_col=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "R1=set(human_2R['Symbol1'].to_list()).intersection(set(vertebrate_high))\n",
    "R2=set(human_2R['Symbol2'].to_list()).intersection(set(vertebrate_high))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the high R1:226\n",
      "the high R2:220\n"
     ]
    }
   ],
   "source": [
    "print(f'the high R1:{len(R1)}\\nthe high R2:{len(R2)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "103"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(R1).intersection(set(R2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3708\n",
      "3672\n"
     ]
    }
   ],
   "source": [
    "print(len(set(human_2R['Symbol1'].to_list())))\n",
    "print(len(set(human_2R['Symbol2'].to_list())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1420"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(human_2R['Symbol1'].to_list()).intersection(set(human_2R['Symbol2'].to_list())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "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>Ohno1</th>\n",
       "      <th>Ohno2</th>\n",
       "      <th>Symbol1</th>\n",
       "      <th>Symbol2</th>\n",
       "      <th>Og weighted global mean</th>\n",
       "      <th>Self weighted global mean</th>\n",
       "      <th>Outgroup Support</th>\n",
       "      <th>Duplication time</th>\n",
       "      <th>Gene type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENSG00000164236</td>\n",
       "      <td>ENSG00000167612</td>\n",
       "      <td>ANKRD33B</td>\n",
       "      <td>ANKRD33</td>\n",
       "      <td>1.592424e-04</td>\n",
       "      <td>1.329170e-08</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Vertebrata</td>\n",
       "      <td>protein_coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENSG00000147465</td>\n",
       "      <td>ENSG00000010270</td>\n",
       "      <td>STAR</td>\n",
       "      <td>STARD3NL</td>\n",
       "      <td>6.313951e-05</td>\n",
       "      <td>2.230453e-04</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Vertebrata</td>\n",
       "      <td>protein_coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENSG00000095464</td>\n",
       "      <td>ENSG00000132915</td>\n",
       "      <td>PDE6C</td>\n",
       "      <td>PDE6A</td>\n",
       "      <td>1.290518e-09</td>\n",
       "      <td>1.249648e-13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Vertebrata</td>\n",
       "      <td>protein_coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENSG00000078804</td>\n",
       "      <td>ENSG00000164938</td>\n",
       "      <td>TP53INP2</td>\n",
       "      <td>TP53INP1</td>\n",
       "      <td>2.431218e-06</td>\n",
       "      <td>3.374232e-24</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Vertebrata</td>\n",
       "      <td>protein_coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENSG00000155744</td>\n",
       "      <td>ENSG00000122591</td>\n",
       "      <td>FAM126B</td>\n",
       "      <td>FAM126A</td>\n",
       "      <td>1.899786e-05</td>\n",
       "      <td>9.903332e-34</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Vertebrata</td>\n",
       "      <td>protein_coding</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>5639</th>\n",
       "      <td>ENSG00000172352</td>\n",
       "      <td>ENSG00000166446</td>\n",
       "      <td>CDY1B</td>\n",
       "      <td>CDYL2</td>\n",
       "      <td>6.726989e-04</td>\n",
       "      <td>8.291722e-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vertebrata</td>\n",
       "      <td>protein_coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5640</th>\n",
       "      <td>ENSG00000273554</td>\n",
       "      <td>ENSG00000132164</td>\n",
       "      <td>AC136616.1</td>\n",
       "      <td>SLC6A11</td>\n",
       "      <td>5.881828e-04</td>\n",
       "      <td>2.370274e-23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vertebrata</td>\n",
       "      <td>protein_coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5641</th>\n",
       "      <td>ENSG00000010379</td>\n",
       "      <td>ENSG00000273554</td>\n",
       "      <td>SLC6A13</td>\n",
       "      <td>AC136616.1</td>\n",
       "      <td>1.274947e-03</td>\n",
       "      <td>8.271787e-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vertebrata</td>\n",
       "      <td>protein_coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5642</th>\n",
       "      <td>ENSG00000111181</td>\n",
       "      <td>ENSG00000273554</td>\n",
       "      <td>SLC6A12</td>\n",
       "      <td>AC136616.1</td>\n",
       "      <td>4.454568e-03</td>\n",
       "      <td>1.140506e-14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vertebrata</td>\n",
       "      <td>protein_coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5643</th>\n",
       "      <td>ENSG00000273554</td>\n",
       "      <td>ENSG00000131389</td>\n",
       "      <td>AC136616.1</td>\n",
       "      <td>SLC6A6</td>\n",
       "      <td>5.025420e-04</td>\n",
       "      <td>2.149403e-25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vertebrata</td>\n",
       "      <td>protein_coding</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5644 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Ohno1            Ohno2     Symbol1     Symbol2  \\\n",
       "0     ENSG00000164236  ENSG00000167612    ANKRD33B     ANKRD33   \n",
       "1     ENSG00000147465  ENSG00000010270        STAR    STARD3NL   \n",
       "2     ENSG00000095464  ENSG00000132915       PDE6C       PDE6A   \n",
       "3     ENSG00000078804  ENSG00000164938    TP53INP2    TP53INP1   \n",
       "4     ENSG00000155744  ENSG00000122591     FAM126B     FAM126A   \n",
       "...               ...              ...         ...         ...   \n",
       "5639  ENSG00000172352  ENSG00000166446       CDY1B       CDYL2   \n",
       "5640  ENSG00000273554  ENSG00000132164  AC136616.1     SLC6A11   \n",
       "5641  ENSG00000010379  ENSG00000273554     SLC6A13  AC136616.1   \n",
       "5642  ENSG00000111181  ENSG00000273554     SLC6A12  AC136616.1   \n",
       "5643  ENSG00000273554  ENSG00000131389  AC136616.1      SLC6A6   \n",
       "\n",
       "      Og weighted global mean  Self weighted global mean  Outgroup Support  \\\n",
       "0                1.592424e-04               1.329170e-08               2.0   \n",
       "1                6.313951e-05               2.230453e-04               4.0   \n",
       "2                1.290518e-09               1.249648e-13               1.0   \n",
       "3                2.431218e-06               3.374232e-24               2.0   \n",
       "4                1.899786e-05               9.903332e-34               3.0   \n",
       "...                       ...                        ...               ...   \n",
       "5639             6.726989e-04               8.291722e-17               NaN   \n",
       "5640             5.881828e-04               2.370274e-23               NaN   \n",
       "5641             1.274947e-03               8.271787e-19               NaN   \n",
       "5642             4.454568e-03               1.140506e-14               NaN   \n",
       "5643             5.025420e-04               2.149403e-25               NaN   \n",
       "\n",
       "     Duplication time       Gene type  \n",
       "0          Vertebrata  protein_coding  \n",
       "1          Vertebrata  protein_coding  \n",
       "2          Vertebrata  protein_coding  \n",
       "3          Vertebrata  protein_coding  \n",
       "4          Vertebrata  protein_coding  \n",
       "...               ...             ...  \n",
       "5639       Vertebrata  protein_coding  \n",
       "5640       Vertebrata  protein_coding  \n",
       "5641       Vertebrata  protein_coding  \n",
       "5642       Vertebrata  protein_coding  \n",
       "5643       Vertebrata  protein_coding  \n",
       "\n",
       "[5644 rows x 9 columns]"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "human_2R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gene2Name():\n",
    "    dic2 = {}\n",
    "    path='H:/002.singcell/01.liver/10.mutileSpecies/05.SAMap/04.gene2names'\n",
    "    for i in os.listdir(path):\n",
    "        if i.endswith('.tsv'):\n",
    "            dic1 = {}\n",
    "            sp = i.split('.')[0]\n",
    "            file =open(path+'/'+i,'r')\n",
    "            for line in file:\n",
    "                line = line.strip().split('\\t')\n",
    "                dic1[line[0]] = line[1]\n",
    "                dic2[sp] = dic1\n",
    "            file.close()\n",
    "        else:\n",
    "            continue\n",
    "    return dic2\n",
    "gene_name2 = gene2Name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['bichir', 'chicken', 'dogshark', 'elephantshark', 'frog', 'Greenlizard', 'hagfish', 'human', 'lamprey', 'lancelet', 'lungfish', 'mouse'])"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_name2.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_gene = []\n",
    "for x in gene_name2.keys():\n",
    "    if x in ['bichir','dogshark','frog','human','lamprey','lungfish']:\n",
    "        for y in gene_name2[x].keys():\n",
    "            all_gene.append(gene_name2[x][y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "homo_gene = []\n",
    "for x in set(all_gene):\n",
    "    if all_gene.count(x)>=3:\n",
    "        homo_gene.append(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14609"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(homo_gene)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bichir\n",
      "dogshark\n",
      "frog\n",
      "lamprey\n",
      "human\n",
      "lungfish\n"
     ]
    }
   ],
   "source": [
    "human_R1_R2 = human_2R['Symbol1'].to_list() + human_2R['Symbol2'].to_list()\n",
    "human_R1_R2 = list(set(human_R1_R2))\n",
    "wgd_gene = []\n",
    "for x in ['bichir','dogshark','frog','lamprey','human','lungfish']:\n",
    "    tmp = []\n",
    "    print(x)\n",
    "    for y in gene_name2[x]:\n",
    "        if gene_name2[x][y] in human_R1_R2:\n",
    "            tmp.append(gene_name2[x][y])\n",
    "        else:\n",
    "            pass\n",
    "    wgd_gene = wgd_gene + list(set(tmp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_wgd_gene=[]\n",
    "for x in set(wgd_gene):\n",
    "    if wgd_gene.count(x) >=3:\n",
    "        selected_wgd_gene.append(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4984"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(selected_wgd_gene)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fisherExactForOverlap(overlap_value,set1,set2,background_value):\n",
    "    from scipy.stats import fisher_exact\n",
    "    table=[[overlap_value,set1-overlap_value],[set2-overlap_value,background_value-(set1+set2)+overlap_value]]\n",
    "    return fisher_exact(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.7097599328729187, 3.3139336904639815e-12)"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fisherExactForOverlap(342,739,4989,14609)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('H:/002.singcell/01.liver/10.mutileSpecies/05.SAMap/13.homoGenes/human_2R.txt',header=0,index_col=None,sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "human = pd.read_csv('H:/002.singcell/01.liver/10.mutileSpecies/05.SAMap/13.homoGenes/human.homoGenes.txt',sep='\\t',header=0,index_col=0)\n",
    "human.fillna('no value', inplace=True)\n",
    "human_wgd = []\n",
    "for index, row in human.iterrows():\n",
    "    if row['lancelet']== 'no value':\n",
    "        line = row['human'].split(',')\n",
    "        line2 = [x.strip() for x in line]\n",
    "        human_wgd = human_wgd + line2\n",
    "    else:\n",
    "        tmp =len(row['human'].split(','))-len(row['lancelet'].split(','))\n",
    "        line = row['human'].split(',')\n",
    "        line2 = [x.strip() for x in line]\n",
    "        human_wgd = human_wgd + line2[tmp: ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_R1=pd.read_csv('H:/002.singcell/01.liver/10.mutileSpecies/05.SAMap/13.homoGenes/human_2R.txt',header=0,index_col=None,sep='\\t')\n",
    "df_R1.fillna('no value', inplace=True)\n",
    "R2_1 = []\n",
    "for index, row in df_R1.iterrows():\n",
    "    if row['lamprey']== 'no value':\n",
    "        line = row['human'].split(',')\n",
    "        line2 = [x.strip() for x in line]\n",
    "        R2_1 = R2_1 + line2\n",
    "    else:\n",
    "        tmp =len(row['human'].split(','))-len(row['lamprey'].split(','))\n",
    "        line = row['human'].split(',')\n",
    "        line2 = [x.strip() for x in line]\n",
    "        R2_1 = R2_1 + line2[tmp: ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "R1_1=[x for x in human_wgd if x not in R2_1]\n",
    "R1_1=[x.upper() for x in R1_1]\n",
    "R2_1=[x.upper() for x in R2_1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2056"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(R1_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bichir\n",
      "dogshark\n",
      "frog\n",
      "lamprey\n",
      "human\n",
      "lungfish\n"
     ]
    }
   ],
   "source": [
    "human_R1_R2_1 = list(set(R1_1 + R2_1))\n",
    "t = []\n",
    "for x in ['bichir','dogshark','frog','lamprey','human','lungfish']:\n",
    "    tmp = []\n",
    "    print(x)\n",
    "    for y in gene_name2[x]:\n",
    "        if gene_name2[x][y] in human_R1_R2_1:\n",
    "            tmp.append(gene_name2[x][y])\n",
    "        else:\n",
    "            pass\n",
    "    t = t + list(set(tmp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_wgd_gene_1=[]\n",
    "for x in set(t):\n",
    "    if t.count(x) >=3:\n",
    "        selected_wgd_gene_1.append(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "152"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(R1_1).intersection(set(vertebrate_high)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "160"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(R2_1).intersection(set(vertebrate_high)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "ancient_genes_high=set(vertebrate_high)-set(R2)-set(R1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "lancelet_high=['LOC'+x for x in lancelet_high]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "lancelet_high_names =[]\n",
    "for x in lancelet_high:\n",
    "    if x in gene_name2['lancelet'].keys():\n",
    "        lancelet_high_names.append(gene_name2['lancelet'][x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "ancient_lancelet_high = set(lancelet_high_names).intersection(ancient_genes_high)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "ancient_vertebrate_high = set(ancient_genes_high)-ancient_lancelet_high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in ancient_vertebrate_high:\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_key_by_value(dictionary, value):\n",
    "    for key, val in dictionary.items():\n",
    "        if val == value:\n",
    "            return key\n",
    "    return None\n",
    "tmp1 = [find_key_by_value(gene_name2['lancelet'],x) for x in ancient_lancelet_high]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp2 = []\n",
    "for x in tmp1:\n",
    "    x = re.search(r'LOC(\\d+)',x).group(1)\n",
    "    if x in bf_lancelet_dic.keys():\n",
    "        print(x)\n",
    "        tmp2.append(bf_lancelet_dic[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AC097634.4\n",
      "LRRIQ1\n",
      "CTSK\n",
      "TUBA1A\n",
      "GCH1\n",
      "S1PR3\n",
      "HSPA8\n",
      "CH25H\n",
      "FREM1\n",
      "DCDC2\n",
      "RGS9\n",
      "ZP3\n",
      "PRG3\n",
      "RBP4\n",
      "IGLL1\n",
      "SLC6A19\n",
      "LTC4S\n",
      "STK17A\n",
      "APOE\n",
      "GPR4\n",
      "PLBD1\n",
      "MCC\n",
      "WDR17\n",
      "ANKFN1\n",
      "ZNF335\n",
      "RNF220\n",
      "HEG1\n",
      "RTL1\n",
      "ZNF366\n",
      "NPR3\n",
      "SHISA9\n",
      "MEGF6\n",
      "KNG1\n",
      "ZNF665\n",
      "CCDC80\n",
      "CRP\n",
      "GPNMB\n",
      "IGHD\n",
      "TNNC1\n",
      "CDKL1\n",
      "CARTPT\n",
      "DNASE1L3\n",
      "BMPR2\n",
      "TUBB3\n",
      "PTPRM\n",
      "KIF21B\n",
      "CXCR1\n",
      "HLA\n",
      "MAG\n",
      "VPREB1\n",
      "STAB2\n",
      "CLEC4E\n",
      "PARP14\n",
      "EPHX1\n",
      "SULT2B1\n",
      "NIBAN1\n",
      "ADAMTS17\n",
      "CXCL8\n",
      "NECTIN1\n",
      "IGKV1\n",
      "NLRP3\n",
      "PTPN22\n",
      "SPIC\n",
      "LPAR6\n",
      "STAB1\n",
      "COL6A2\n",
      "GNMT\n",
      "TENT5A\n",
      "ZFP2\n",
      "IGKV2D\n",
      "AXL\n",
      "BMP10\n",
      "TRIM14\n",
      "TRIM16\n",
      "CDHR3\n",
      "MMRN2\n",
      "TIMD4\n",
      "NUMA1\n",
      "CYP2A13\n",
      "METTL24\n",
      "CALD1\n",
      "SEMA3D\n",
      "P2RY8\n",
      "LIFR\n",
      "HLX\n",
      "AFAP1\n",
      "CCL19\n",
      "CXCL10\n",
      "PDGFA\n",
      "CYSLTR1\n",
      "ESM1\n",
      "ADGRF5\n",
      "PDIA2\n",
      "PTPRB\n",
      "CENPF\n",
      "TMTC2\n",
      "BICDL1\n",
      "TRIM69\n",
      "CXCR4\n",
      "MCPH1\n",
      "SLC24A3\n",
      "C1QTNF12\n",
      "WT1\n",
      "ANKK1\n",
      "VCAM1\n",
      "NTSR1\n",
      "SNCG\n",
      "NTRK2\n",
      "FRK\n",
      "ADM\n",
      "NLRP12\n",
      "MID1IP1\n",
      "GPR55\n",
      "CENPE\n",
      "NECAB1\n",
      "CHRNA4\n",
      "HBE1\n",
      "HPX\n",
      "FGFRL1\n",
      "KRT17\n",
      "SPEF1\n",
      "NRP2\n",
      "SEMA3G\n",
      "IGHV3\n",
      "CCN1\n",
      "ANGPT1\n",
      "SERPINF2\n",
      "ACSBG1\n",
      "GFRA1\n",
      "COL21A1\n",
      "PTN\n",
      "KRT18\n",
      "CCL20\n",
      "CCN4\n",
      "B3GNT3\n",
      "CCR6\n",
      "ADAMTSL3\n",
      "HMOX1\n",
      "GZMH\n",
      "ZCCHC3\n",
      "BICC1\n",
      "AGR3\n",
      "PTGIR\n",
      "CASR\n",
      "IFI44L\n",
      "PVALB\n",
      "CD276\n",
      "ZP2\n",
      "PLK2\n",
      "ENTPD1\n",
      "RHOB\n",
      "CYP1B1\n",
      "SERPINA1\n",
      "FCRL5\n",
      "KLF1\n",
      "EFCC1\n",
      "NR2F1\n",
      "ZBED4\n",
      "CD163\n",
      "SNCAIP\n",
      "ZBED6\n",
      "WNT4\n",
      "KANK1\n",
      "GRID2\n",
      "TMEM211\n",
      "AKAP12\n",
      "NCKAP5\n",
      "IL1R2\n",
      "HS3ST1\n",
      "APOA4\n",
      "MKI67\n",
      "UCP2\n",
      "HHIP\n",
      "NCR3\n",
      "AHNAK2\n",
      "TRIM25\n",
      "SORT1\n",
      "THEMIS\n",
      "NNMT\n",
      "SEMA4D\n",
      "RASEF\n",
      "PCDH17\n",
      "CPLANE1\n",
      "LAMA5\n",
      "RP1\n",
      "EHD3\n",
      "IL6ST\n",
      "CDC42EP2\n",
      "PITPNC1\n",
      "CILP\n",
      "CEP350\n",
      "IFI30\n",
      "PCDHGC5\n",
      "SLC6A7\n",
      "CLEC14A\n",
      "GP9\n",
      "CMKLR1\n",
      "TIGD1\n",
      "TNNI1\n",
      "TBX20\n",
      "CCL3L3\n",
      "FHDC1\n",
      "SLC40A1\n",
      "FNDC1\n",
      "RIMS2\n",
      "CASZ1\n",
      "PIK3R5\n",
      "CLDN5\n",
      "RERGL\n",
      "TRIM39\n",
      "PDE2A\n",
      "XIRP2\n",
      "HDC\n",
      "SEMA7A\n",
      "HEPACAM\n",
      "CCDC3\n",
      "IGLL5\n",
      "EPOR\n",
      "AFAP1L1\n",
      "CCBE1\n",
      "MYH1\n",
      "EDN1\n",
      "PPP1R32\n",
      "PDZD2\n",
      "IFIT5\n",
      "SEMA3F\n",
      "HSPG2\n",
      "TRPS1\n",
      "LRRC32\n",
      "PDE10A\n",
      "IRGC\n",
      "RDH16\n",
      "URGCP\n",
      "AKR1C3\n",
      "ARHGAP6\n",
      "PDE5A\n",
      "CD22\n",
      "GTF2IRD2\n",
      "RAMP2\n",
      "F2RL2\n",
      "SH3TC1\n",
      "KCNQ1\n",
      "RGS9BP\n",
      "TNS2\n",
      "HBZ\n",
      "OLFM4\n",
      "PACRG\n",
      "TRIM29\n",
      "KLF5\n",
      "DEF6\n",
      "UBB\n",
      "ASMT\n",
      "FETUB\n",
      "GZMA\n",
      "ARTN\n",
      "PDGFC\n",
      "OTOGL\n",
      "GZMB\n",
      "C1QB\n",
      "VWA7\n",
      "LGMN\n",
      "IGSF11\n",
      "AMBP\n",
      "ASPM\n",
      "FAM189A1\n",
      "SACS\n",
      "VIPR2\n",
      "EMILIN1\n",
      "LRRC46\n",
      "PRX\n",
      "MPO\n",
      "GPR139\n",
      "NLRC3\n",
      "CHIA\n",
      "CCR4\n",
      "BLVRB\n",
      "CD109\n",
      "PHLDA2\n",
      "DCN\n",
      "A2M\n",
      "RAMP3\n",
      "GAS1\n",
      "RGCC\n",
      "CD3E\n",
      "C1QC\n",
      "ZSCAN2\n",
      "LGALS2\n",
      "GPR34\n",
      "CEACAM6\n",
      "AC008687.4\n",
      "PTX3\n",
      "SLC51A\n",
      "MYO10\n",
      "B3GNT7\n",
      "JCHAIN\n",
      "PIEZO2\n",
      "ZNF385A\n",
      "NUGGC\n",
      "CLEC17A\n",
      "LTBP1\n",
      "GFRA2\n",
      "TMEM204\n",
      "TCN1\n",
      "DMRT1\n",
      "FZD7\n",
      "PODXL\n",
      "VMO1\n",
      "TGM2\n",
      "SEMA3A\n",
      "MOB2\n",
      "ANO7\n",
      "IQCA1\n",
      "GZMK\n",
      "RYR2\n",
      "PCDH10\n",
      "A2ML1\n",
      "CRIP2\n",
      "HPGDS\n",
      "CTSL\n",
      "AC004151.1\n",
      "COL6A1\n",
      "LUM\n",
      "ZNF484\n",
      "LURAP1\n",
      "SULT6B1\n",
      "PRF1\n",
      "LUZP2\n",
      "GJA4\n",
      "BIRC5\n",
      "EGFLAM\n",
      "CHKA\n"
     ]
    }
   ],
   "source": [
    "for x in ancient_vertebrate_high:\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "331"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ancient_vertebrate_high)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TUBA1A\n",
      "URGCP\n",
      "HLA\n",
      "NRP2\n",
      "VPREB1\n",
      "IGHV3\n",
      "SERPINA1\n",
      "FCRL5\n",
      "CCN1\n",
      "IGLL1\n",
      "OLFM4\n",
      "TIGD1\n",
      "ZBED4\n",
      "COL21A1\n",
      "CD163\n",
      "TRIM69\n",
      "FETUB\n",
      "TRIM39\n",
      "LTBP1\n",
      "NLRP3\n",
      "GZMA\n",
      "IGKV1\n",
      "VMO1\n",
      "RTL1\n",
      "IGLL5\n",
      "CTSL\n",
      "GZMH\n",
      "TRIM25\n",
      "MYH1\n",
      "ZCCHC3\n",
      "TRIM16\n",
      "CASR\n",
      "CRP\n",
      "IGHD\n",
      "PRF1\n",
      "PTPRM\n",
      "PLK2\n"
     ]
    }
   ],
   "source": [
    "for x in set(vertebrate_high2).intersection(set(ancient_vertebrate_high)):\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_enhancer = pd.read_csv('H:/002.singcell/01.liver/10.mutileSpecies/05.SAMap/015.enhancer/enhancer_liver.csv',sep=',',header=0,index_col=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_enhancer=df_enhancer[df_enhancer['TargetGene'].isin(ancient_vertebrate_high)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\wubaosheng\\AppData\\Local\\Temp\\ipykernel_23704\\535442118.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\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",
      "  df_enhancer.drop_duplicates(subset=['name'],inplace=True)\n"
     ]
    }
   ],
   "source": [
    "df_enhancer.drop_duplicates(subset=['name'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Bio import SeqIO\n",
    "from Bio.Seq import Seq\n",
    "human_dir={}\n",
    "records = SeqIO.parse(\"H:/002.singcell/01.liver/10.mutileSpecies/05.SAMap/015.enhancer/hg19.fa\", \"fasta\")\n",
    "for record in records:\n",
    "    id = record.id\n",
    "    seq = record.seq\n",
    "    human_dir[id] = seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = open('H:/002.singcell/01.liver/10.mutileSpecies/05.SAMap/015.enhancer/hg19.fa.fai','r')\n",
    "chr_info = {}\n",
    "for line in file:\n",
    "    line = line.strip().split(',')[0].split(' ')\n",
    "    nc = line[0][1: ]\n",
    "    chr =line[-1]\n",
    "    if chr != 'mitochondrion':\n",
    "        id = 'chr' + chr\n",
    "        chr_info[id] = nc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp =[]\n",
    "table = open('H:/002.singcell/01.liver/10.mutileSpecies/05.SAMap/017. HVGs/liver_enhancer2.fa','w')\n",
    "for index,row in df_enhancer.iterrows():\n",
    "    name = row['name']\n",
    "    start = row['start']\n",
    "    end = row['end']\n",
    "    chr = row['chr']\n",
    "    id = name + '|' + 'chr'+ \"_\" + str(start) + '_' + str(end)\n",
    "    seq = human_dir[chr_info[chr]][start-1:end]\n",
    "    if id not in tmp:\n",
    "        table.write('>'+id+'\\n')\n",
    "        table.write(str(seq).upper()+'\\n')\n",
    "        if len(str(seq)) == 0:\n",
    "            print(name)\n",
    "        tmp.append(id)\n",
    "    else:\n",
    "        pass\n",
    "table.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in ancient_vertebrate_high:\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_gene = pd.DataFrame({'type':['overlap_R1','overlap_R2',]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = open('H:/002.singcell/01.liver/10.mutileSpecies/05.SAMap/017. HVGs/human_2R_genes.txt','w')\n",
    "for x in set(human_2R['Symbol1'].to_list() + human_2R['Symbol2'].to_list()):\n",
    "    table.writelines(str(x)+'\\n')\n",
    "table.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "hagfish=pd.read_csv(\"H:/002.singcell/01.liver/10.mutileSpecies/05.SAMap/017. HVGs/human2R_hagfish.txt\",sep='\\t',header=0,index_col=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "hagfish.fillna('no value',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_R1=[]\n",
    "total_R2=[]\n",
    "for index,row in hagfish.iterrows():\n",
    "    if row['Last common ancestor with Hagfish']=='Vertebrata'or row['Last common ancestor with Lamprey']=='Vertebrata':\n",
    "        total_R1.append(row['Gene name'])\n",
    "    else:\n",
    "        total_R2.append(row['Gene name'])\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "246"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(vertebrate_high).intersection(set(total_R1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "106"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(vertebrate_high).intersection(set(total_R2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "overlap =set(vertebrate_high).intersection(set(total_R1)).intersection(set(total_R2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_R2=[x for x in total_R2 if x not in overlap]\n",
    "final_R1 = set(total_R1)\n",
    "final_R2 = set(final_R2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3389"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(final_R1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2524"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(final_R2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "246"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(vertebrate_high).intersection(set(final_R1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "96"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(vertebrate_high).intersection(set(final_R2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = open('H:/002.singcell/01.liver/10.mutileSpecies/05.SAMap/017. HVGs/human_R1_genes.txt','w')\n",
    "for x in set(final_R1):\n",
    "    table.writelines(str(x)+'\\n')\n",
    "table.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
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         },
         "name": "",
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         "orientation": "v",
         "showlegend": false,
         "textposition": "auto",
         "type": "bar",
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    "from plotly.subplots import make_subplots\n",
    "x=['overlap_R1','overlap_R2','vertebrateHVG','R1','R2','vertebrate_homo']\n",
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    "from plotly.subplots import make_subplots\n",
    "x=['overlap_R1','overlap_R2','vertebrateHVG']\n",
    "y=[246,96,739]\n",
    "fig=px.bar(x=x,y=y)\n",
    "fig.show()\n",
    "fig.write_image('H:/002.singcell/01.liver/10.mutileSpecies/05.SAMap/017. HVGs/R1+R2.pdf')"
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          "font": {
           "color": "#2a3f5f"
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          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
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          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
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           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "x"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "y"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "from plotly.subplots import make_subplots\n",
    "x=['R1','R2','vertebrate_homo']\n",
    "y=[3389,2524,14609]\n",
    "fig=px.bar(x=x,y=y)\n",
    "fig.show()\n",
    "fig.write_image('H:/002.singcell/01.liver/10.mutileSpecies/05.SAMap/017. HVGs/R1+R2+homo.pdf')"
   ]
  }
 ],
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