{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import sparse, io, stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re \n",
    "def sorted_nicely( l ): \n",
    "    \"\"\" Sort the given iterable in the way that humans expect.\"\"\" \n",
    "    convert = lambda text: int(text) if text.isdigit() else text \n",
    "    alphanum_key = lambda key: [ convert(c) for c in re.split('([0-9]+)', key) ] \n",
    "    return sorted(l, key = alphanum_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2_or_agg = pd.read_hdf('/data/lohia/gene_distance_expresseion/dist_files/norm_dist_files/combined_dist_500_agg.h5' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2_or_rao = pd.read_hdf('/data/lohia/gene_distance_expresseion/dist_files/norm_dist_files/combined_dist_500_rao.h5' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2_or_rao_kr = pd.read_hdf('/data/lohia/gene_distance_expresseion/dist_files/norm_dist_files/combined_dist_with_georg_hic_rao_kr_vc_500.h5' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2_or_rao_kr.rename(columns={\"hi-c-rao\": \"VC_tss_median\" }, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2_or_rao['pairs'] = [str(x)+\"_\"+str(y) for x,y in zip(df_2_or_rao['Gene stable ID_y'], df_2_or_rao['Gene stable ID_x'])]\n",
    "df_2_or_rao.set_index('pairs', inplace=True)\n",
    "\n",
    "df_2_or_agg['pairs'] = [str(x)+\"_\"+str(y) for x,y in zip(df_2_or_agg['Gene stable ID_y'], df_2_or_agg['Gene stable ID_x'])]\n",
    "df_2_or_agg.set_index('pairs', inplace=True)\n",
    "\n",
    "df_2_or_rao_kr['pairs'] = [str(x)+\"_\"+str(y) for x,y in zip(df_2_or_rao_kr['Gene stable ID_y'], df_2_or_rao_kr['Gene stable ID_x'])]\n",
    "df_2_or_rao_kr.set_index('pairs', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "subset_cols = list (set(df_2_or_rao.index) & set(df_2_or_agg.index) & set(df_2_or_rao_kr.index)) # I only care for genes subset for which expression ans tss is present        \n",
    "df_2_or_agg = df_2_or_agg.loc[subset_cols, :]\n",
    "df_2_or_rao = df_2_or_rao.loc[subset_cols, :]\n",
    "df_2_or_rao_kr = df_2_or_rao_kr.loc[subset_cols, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "chrm_list = []\n",
    "net_type = []\n",
    "corr = []\n",
    "p_val = []\n",
    "agg_method = []\n",
    "\n",
    "            \n",
    "change_group_level_1 = df_2_or_rao.groupby(['chrom_x'])\n",
    "for network_type in ['VC_tss']:\n",
    "    for chrm in sorted_nicely(change_group_level_1.groups.keys()): \n",
    "            df = change_group_level_1.get_group(chrm)\n",
    "            #df = df[df['tss_tss'] >= 10000000] # liming the matrix to only chosen values for rank standerization\n",
    "            #df = df.dropna(subset=[network_type])\n",
    "            c = stats.pearsonr(df[network_type].to_numpy(), df['exp'].to_numpy() )\n",
    "            chrm_list.append(chrm)\n",
    "            net_type.append(network_type)\n",
    "            corr.append(c[0])\n",
    "            p_val.append(c[1])\n",
    "            agg_method.append('rao')\n",
    "            \n",
    "change_group_level_1 = df_2_or_rao_kr.groupby(['chrom_x'])\n",
    "for network_type in ['VC_tss_median']:\n",
    "    for chrm in sorted_nicely(change_group_level_1.groups.keys()): \n",
    "            df = change_group_level_1.get_group(chrm)\n",
    "            #df = df[df['tss_tss'] >= 10000000] # liming the matrix to only chosen values for rank standerization\n",
    "            #df = df.dropna(subset=[network_type])\n",
    "            c = stats.pearsonr(df[network_type].to_numpy(), df['exp'].to_numpy() )\n",
    "            chrm_list.append(chrm)\n",
    "            net_type.append(network_type)\n",
    "            corr.append(c[0])\n",
    "            p_val.append(c[1])\n",
    "            agg_method.append('rao_kr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6287fd5110>"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(list(zip(chrm_list, net_type, corr, p_val, agg_method)), \n",
    "               columns =['chrm_list', 'net_type', 'corr', 'p_val', 'agg_method']) \n",
    "df_melted = pd.pivot_table(df, values='corr', index=['chrm_list'],\n",
    "                    columns=['agg_method'])\n",
    "df_melted.plot.bar(figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "chrm_list = []\n",
    "net_type = []\n",
    "corr = []\n",
    "p_val = []\n",
    "agg_method = []\n",
    "change_group_level_1 = df_2_or_agg.groupby(['chrom_x'])\n",
    "for network_type in ['VC_tss']:\n",
    "    for chrm in sorted_nicely(change_group_level_1.groups.keys()): \n",
    "            df = change_group_level_1.get_group(chrm)\n",
    "            #df = df[df['tss_tss'] >= 10000000] # liming the matrix to only chosen values for rank standerization\n",
    "            #df = df.dropna(subset=[network_type])\n",
    "            c = stats.pearsonr(df[network_type].to_numpy(), df['exp'].to_numpy() )\n",
    "            chrm_list.append(chrm)\n",
    "            net_type.append(network_type)\n",
    "            corr.append(c[0])\n",
    "            p_val.append(c[1])\n",
    "            agg_method.append('agg')\n",
    "            \n",
    "change_group_level_1 = df_2_or_rao.groupby(['chrom_x'])\n",
    "for network_type in ['VC_tss']:\n",
    "    for chrm in sorted_nicely(change_group_level_1.groups.keys()): \n",
    "            df = change_group_level_1.get_group(chrm)\n",
    "            #df = df[df['tss_tss'] >= 10000000] # liming the matrix to only chosen values for rank standerization\n",
    "            #df = df.dropna(subset=[network_type])\n",
    "            c = stats.pearsonr(df[network_type].to_numpy(), df['exp'].to_numpy() )\n",
    "            chrm_list.append(chrm)\n",
    "            net_type.append(network_type)\n",
    "            corr.append(c[0])\n",
    "            p_val.append(c[1])\n",
    "            agg_method.append('rao')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f62dc0bab50>"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_agg = pd.DataFrame(list(zip(chrm_list, net_type, corr, p_val, agg_method)), \n",
    "               columns =['chrm_list', 'net_type', 'corr', 'p_val', 'agg_method']) \n",
    "df_melted_agg = pd.pivot_table(df_agg, values='corr', index=['chrm_list'],\n",
    "                    columns=['agg_method'])\n",
    "df_melted_agg.plot.bar(figsize=(8,4), legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f62c68e7550>"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(df_melted_agg['agg'] - df_melted_agg['rao'] ).plot.bar(figsize=(8,4), legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_agg = pd.DataFrame(list(zip(chrm_list, net_type, corr, p_val, agg_method)), \n",
    "               columns =['chrm_list', 'net_type', 'corr', 'p_val', 'agg_method']) \n",
    "df_melted_agg = pd.pivot_table(df_agg, values='corr', index=['chrm_list'],\n",
    "                    columns=['agg_method'])\n",
    "df_melted_agg.plot.bar(figsize=(8,4), legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "chrm_list = []\n",
    "net_type = []\n",
    "corr = []\n",
    "p_val = []\n",
    "agg_method = []\n",
    "change_group_level_1 = df_2_or_agg.groupby(['chrom_x'])\n",
    "for network_type in ['VC_tss', 'VC_max', 'VC_mean']:\n",
    "    for chrm in sorted_nicely(change_group_level_1.groups.keys()): \n",
    "            df = change_group_level_1.get_group(chrm)\n",
    "            #df = df[df['tss_tss'] >= 10000000] # liming the matrix to only chosen values for rank standerization\n",
    "            #df = df.dropna(subset=[network_type])\n",
    "            c = stats.pearsonr(df[network_type].to_numpy(), df['exp'].to_numpy() )\n",
    "            chrm_list.append(chrm)\n",
    "            net_type.append(network_type)\n",
    "            corr.append(c[0])\n",
    "            p_val.append(c[1])\n",
    "            agg_method.append('agg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f629f417a90>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_map = pd.DataFrame(list(zip(chrm_list, net_type, corr, p_val, agg_method)), \n",
    "               columns =['chrm_list', 'net_type', 'corr', 'p_val', 'agg_method']) \n",
    "df_melted_map = pd.pivot_table(df_map, values='corr', index=['chrm_list'],\n",
    "                    columns=['net_type'])\n",
    "df_melted_map.plot.bar(figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "chrm_list = []\n",
    "net_type = []\n",
    "corr = []\n",
    "p_val = []\n",
    "agg_method = []\n",
    "change_group_level_1 = df_2_or_agg.groupby(['chrom_x'])\n",
    "for network_type in ['VC_rank_max', 'VC_max', 'VC_lib_max']:\n",
    "    for chrm in sorted_nicely(change_group_level_1.groups.keys()): \n",
    "            df = change_group_level_1.get_group(chrm)\n",
    "            df = df[df['tss_tss'] >= 10000000] # liming the matrix to only chosen values for rank standerization\n",
    "            #df = df.dropna(subset=[network_type])\n",
    "            c = stats.pearsonr(df[network_type].to_numpy(), df['exp'].to_numpy() )\n",
    "            chrm_list.append(chrm)\n",
    "            net_type.append(network_type)\n",
    "            corr.append(c[0])\n",
    "            p_val.append(c[1])\n",
    "            agg_method.append('agg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f62adc5e410>"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_norm = pd.DataFrame(list(zip(chrm_list, net_type, corr, p_val, agg_method)), \n",
    "               columns =['chrm_list', 'net_type', 'corr', 'p_val', 'agg_method']) \n",
    "df_melted_norm = pd.pivot_table(df_norm, values='corr', index=['chrm_list'],\n",
    "                    columns=['net_type'])\n",
    "df_melted_norm.plot.bar(figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "chrm_list = []\n",
    "net_type = []\n",
    "corr = []\n",
    "p_val = []\n",
    "agg_method = []\n",
    "change_group_level_1 = df_2_or_rao.groupby(['chrom_x'])\n",
    "for network_type in ['VC_rank_max', 'VC_max', 'VC_lib_max']:\n",
    "    for chrm in sorted_nicely(change_group_level_1.groups.keys()): \n",
    "            df = change_group_level_1.get_group(chrm)\n",
    "            #df = df[df['tss_tss'] >= 10000000] # liming the matrix to only chosen values for rank standerization\n",
    "            #df = df.dropna(subset=[network_type])\n",
    "            c = stats.pearsonr(df[network_type].to_numpy(), df['exp'].to_numpy() )\n",
    "            chrm_list.append(chrm)\n",
    "            net_type.append(network_type)\n",
    "            corr.append(c[0])\n",
    "            p_val.append(c[1])\n",
    "            agg_method.append('agg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f627caa1610>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_norm = pd.DataFrame(list(zip(chrm_list, net_type, corr, p_val, agg_method)), \n",
    "               columns =['chrm_list', 'net_type', 'corr', 'p_val', 'agg_method']) \n",
    "df_melted_norm = pd.pivot_table(df_norm, values='corr', index=['chrm_list'],\n",
    "                    columns=['net_type'])\n",
    "df_melted_norm.plot.bar(figsize=(10,5))"
   ]
  },
  {
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}