017.Trans2Dff.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!/usr/bin/env python\n",
    "# -*- encoding: utf-8 -*-\n",
    "'''\n",
    "@文件        :017.Trans2Dff.ipynb\n",
    "@说明        :\n",
    "@时间        :2023/07/27 12:26:21\n",
    "@作者        :Wu Baosheng\n",
    "@版本        :1.0\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os,re,sys\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scanpy as sc\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import tspex\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",
    "os.chdir('H:/002.singcell/01.liver/10.mutileSpecies/05.SAMap/03.endothelial/02.knockout/02.Trans')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nusage: df.index=make_index_unique(df.index)\\n'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def make_index_unique(index: pd.Index, join: str = \"-\"):\n",
    "    ##### exsample df_lungfish.index=make_index_unique(df_lungfish.index)\n",
    "    \"\"\"\n",
    "    Makes the index unique by appending a number string to each duplicate index element:\n",
    "    '1', '2', etc.\n",
    "\n",
    "    If a tentative name created by the algorithm already exists in the index, it tries\n",
    "    the next integer in the sequence.\n",
    "\n",
    "    The first occurrence of a non-unique value is ignored.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    join\n",
    "         The connecting string between name and integer.\n",
    "\n",
    "    Examples\n",
    "    --------\n",
    "    # >>> from anndata import AnnData\n",
    "    # >>> adata = AnnData(np.ones((2, 3)), var=pd.DataFrame(index=[\"a\", \"a\", \"b\"]))\n",
    "    # >>> adata.var_names\n",
    "    # Index(['a', 'a', 'b'], dtype='object')\n",
    "    # >>> adata.var_names_make_unique()\n",
    "    # >>> adata.var_names\n",
    "    Index(['a', 'a-1', 'b'], dtype='object')\n",
    "    \"\"\"\n",
    "    import warnings\n",
    "    if index.is_unique:\n",
    "        return index\n",
    "    from collections import Counter\n",
    "\n",
    "    values = index.values.copy()\n",
    "    indices_dup = index.duplicated(keep=\"first\")\n",
    "    values_dup = values[indices_dup]\n",
    "    values_set = set(values)\n",
    "    counter = Counter()\n",
    "    issue_interpretation_warning = False\n",
    "    example_colliding_values = []\n",
    "    for i, v in enumerate(values_dup):\n",
    "        while True:\n",
    "            counter[v] += 1\n",
    "            tentative_new_name = v + join + str(counter[v])\n",
    "            if tentative_new_name not in values_set:\n",
    "                values_set.add(tentative_new_name)\n",
    "                values_dup[i] = tentative_new_name\n",
    "                break\n",
    "            issue_interpretation_warning = True\n",
    "            if len(example_colliding_values) < 5:\n",
    "                example_colliding_values.append(tentative_new_name)\n",
    "\n",
    "    if issue_interpretation_warning:\n",
    "        warnings.warn(\n",
    "            f\"Suffix used ({join}[0-9]+) to deduplicate index values may make index \"\n",
    "            + \"values difficult to interpret. There values with a similar suffixes in \"\n",
    "            + \"the index. Consider using a different delimiter by passing \"\n",
    "            + \"`join={delimiter}`\"\n",
    "            + \"Example key collisions generated by the make_index_unique algorithm: \"\n",
    "            + str(example_colliding_values)\n",
    "        )\n",
    "    values[indices_dup] = values_dup\n",
    "    index = pd.Index(values, name=index.name)\n",
    "    return index\n",
    "\"\"\"\n",
    "usage: df.index=make_index_unique(df.index)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./mouse.count',sep='\\t',index_col=0,header=0)\n",
    "df.index=df.index.str.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index=make_index_unique(df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "wild = ['NC12_0','NC4_0','NC5_0']\n",
    "kdr = ['KDR2_0','KDR3_0','KDR4_0']\n",
    "flt4 = ['Flt1_0','Flt2_0']\n",
    "sample = wild+kdr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df[sample].to_csv('./wild_vs_flt4.count',sep='\\t',index=True,header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x13986144a60>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.clustermap(df[sample].corr(),cmap='RdBu_r',vmin=-1,vmax=1,center=0,annot=True,fmt='.2f',square=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "kdr = pd.read_csv('./kdr_wild_all_genes_DESeq2.txt',sep='\\t',index_col=0,header=0)\n",
    "flt4 = pd.read_csv('./flt4_wild_all_genes_DESeq2.txt',sep='\\t',index_col=0,header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "kdr = kdr[kdr['padj']<0.05]\n",
    "flt4 = flt4[flt4['padj']<0.05]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n=0\n",
    "for i in kdr[kdr['log2FoldChange']>1].index.to_list():\n",
    "    n+=1\n",
    "    print(i)\n",
    "print(n)\n"
   ]
  }
 ],
 "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
}