plot_impute.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import scanpy as sc\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tool.impute_metric import CalDataMetric\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\n",
      "[1549 rows x 981 columns]\n"
     ]
    }
   ],
   "source": [
    "DataDir=\"data/DataUpload/Dataset3/\"\n",
    "Spatial_file = DataDir + 'Insitu_count.txt'\n",
    "raw_count = pd.read_csv(Spatial_file, header = 0, sep=\"\\t\")\n",
    "raw_count = raw_count.loc[:,np.unique(raw_count.columns)]\n",
    "raw_count.columns = [column.lower() for column in raw_count.columns]\n",
    "print(raw_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "coGCN_impute_count = pd.read_csv(DataDir + \"results/coGCN_impute.csv\", header = 0, index_col = 0)\n",
    "coGCN_impute_count = coGCN_impute_count.loc[:,np.unique(coGCN_impute_count.columns)]\n",
    "coGCN_impute_count.columns = [column.lower() for column in coGCN_impute_count.columns]\n",
    "\n",
    "Tangram_impute_count = pd.read_csv(DataDir + \"results/Tangram_impute.csv\", header = 0, index_col = 0)\n",
    "Tangram_impute_count = Tangram_impute_count.loc[:,np.unique(Tangram_impute_count.columns)]\n",
    "Tangram_impute_count.columns = [column.lower() for column in Tangram_impute_count.columns]\n",
    "\n",
    "LIGER_impute_count = pd.read_csv(DataDir + \"results/LIGER_impute.csv\", header = 0, index_col = 0)\n",
    "LIGER_impute_count = LIGER_impute_count.loc[:,np.unique(LIGER_impute_count.columns)]\n",
    "Index = [(int(x.replace('V', '')) -1) for x in LIGER_impute_count.index]\n",
    "Index = sorted(Index)\n",
    "LIGER_impute_count.index = Index\n",
    "LIGER_impute_count.columns = [column.lower() for column in LIGER_impute_count.columns]\n",
    "\n",
    "Seurat_impute_count = pd.read_csv(DataDir + \"results/Seurat_impute.csv\", header = 0, index_col = 0)\n",
    "Seurat_impute_count = Seurat_impute_count.loc[:,np.unique(Seurat_impute_count.columns)]\n",
    "Index = [(int(x) -1) for x in Seurat_impute_count.index]\n",
    "Index = sorted(Index)\n",
    "Seurat_impute_count.index = Index\n",
    "Seurat_impute_count.columns = [column.lower() for column in Seurat_impute_count.columns]\n",
    "\n",
    "novoSpaRc_impute_count = pd.read_csv(DataDir + \"results/novoSpaRc_impute.csv\", header = 0, index_col = 0)\n",
    "novoSpaRc_impute_count = novoSpaRc_impute_count.loc[:,np.unique(novoSpaRc_impute_count.columns)]\n",
    "novoSpaRc_impute_count.columns = [column.lower() for column in novoSpaRc_impute_count.columns]\n",
    "\n",
    "SpaGE_impute_count = pd.read_csv(DataDir + \"results/SpaGE_impute.csv\", header = 0, index_col = 0)\n",
    "SpaGE_impute_count = SpaGE_impute_count.loc[:,np.unique(SpaGE_impute_count.columns)]\n",
    "SpaGE_impute_count.columns = [column.lower() for column in SpaGE_impute_count.columns]\n",
    "\n",
    "SpaOTsc_impute_count = pd.read_csv(DataDir + \"results/SpaOTsc_impute.csv\", header = 0, index_col = 0)\n",
    "SpaOTsc_impute_count = SpaOTsc_impute_count.loc[:,np.unique(SpaOTsc_impute_count.columns)]\n",
    "SpaOTsc_impute_count.columns = [column.lower() for column in SpaOTsc_impute_count.columns]\n",
    "\n",
    "stPlus_impute_count = pd.read_csv(DataDir + \"results/stPlus_impute.csv\", header = 0, index_col = 0)\n",
    "stPlus_impute_count = stPlus_impute_count.loc[:,np.unique(stPlus_impute_count.columns)]\n",
    "stPlus_impute_count.columns = [column.lower() for column in stPlus_impute_count.columns]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We are calculating the : impute matrix\n",
      "\n",
      "We are calculating the : impute matrix\n",
      "\n",
      "We are calculating the : impute matrix\n",
      "\n",
      "We are calculating the : impute matrix\n",
      "\n",
      "We are calculating the : impute matrix\n",
      "\n",
      "We are calculating the : impute matrix\n",
      "\n",
      "We are calculating the : impute matrix\n",
      "\n",
      "We are calculating the : impute matrix\n",
      "\n"
     ]
    }
   ],
   "source": [
    "coGCN_result_metric=CalDataMetric(raw_count,coGCN_impute_count)\n",
    "Tangram_result_metric=CalDataMetric(raw_count,Tangram_impute_count)\n",
    "LIGER_result_metric=CalDataMetric(raw_count,LIGER_impute_count)\n",
    "Seurat_result_metric=CalDataMetric(raw_count,Seurat_impute_count)\n",
    "novoSpaRc_result_metric=CalDataMetric(raw_count,novoSpaRc_impute_count)\n",
    "SpaGE_result_metric=CalDataMetric(raw_count,SpaGE_impute_count)\n",
    "SpaOTsc_result_metric=CalDataMetric(raw_count,SpaOTsc_impute_count)\n",
    "stPlus_result_metric=CalDataMetric(raw_count,stPlus_impute_count)\n",
    "\n",
    "spots_pcc=pd.concat([coGCN_result_metric.iloc[0],Tangram_result_metric.iloc[0],LIGER_result_metric.iloc[0],Seurat_result_metric.iloc[0],\n",
    "                    novoSpaRc_result_metric.iloc[0],SpaGE_result_metric.iloc[0],SpaOTsc_result_metric.iloc[0],stPlus_result_metric.iloc[0]],axis=1)\n",
    "spots_pcc.columns=['SpatialcoGCN','Tangram','LIGER','Seurat','novoSpaRc','SpaGE','SpaOTsc','stPlus']\n",
    "\n",
    "spots_ssim=pd.concat([coGCN_result_metric.iloc[1],Tangram_result_metric.iloc[1],LIGER_result_metric.iloc[1],Seurat_result_metric.iloc[1],\n",
    "                    novoSpaRc_result_metric.iloc[1],SpaGE_result_metric.iloc[1],SpaOTsc_result_metric.iloc[1],stPlus_result_metric.iloc[1]],axis=1)\n",
    "spots_ssim.columns=['SpatialcoGCN','Tangram','LIGER','Seurat','novoSpaRc','SpaGE','SpaOTsc','stPlus']\n",
    "\n",
    "spots_cossim=pd.concat([coGCN_result_metric.iloc[2],Tangram_result_metric.iloc[2],LIGER_result_metric.iloc[2],Seurat_result_metric.iloc[2],\n",
    "                    novoSpaRc_result_metric.iloc[2],SpaGE_result_metric.iloc[2],SpaOTsc_result_metric.iloc[2],stPlus_result_metric.iloc[2]],axis=1)\n",
    "spots_cossim.columns=['SpatialcoGCN','Tangram','LIGER','Seurat','novoSpaRc','SpaGE','SpaOTsc','stPlus']\n",
    "\n",
    "spots_rmse=pd.concat([coGCN_result_metric.iloc[3],Tangram_result_metric.iloc[3],LIGER_result_metric.iloc[3],Seurat_result_metric.iloc[3],\n",
    "                    novoSpaRc_result_metric.iloc[3],SpaGE_result_metric.iloc[3],SpaOTsc_result_metric.iloc[3],stPlus_result_metric.iloc[3]],axis=1)\n",
    "spots_rmse.columns=['SpatialcoGCN','Tangram','LIGER','Seurat','novoSpaRc','SpaGE','SpaOTsc','stPlus']\n",
    "\n",
    "spots_jsd=pd.concat([coGCN_result_metric.iloc[4],Tangram_result_metric.iloc[4],LIGER_result_metric.iloc[4],Seurat_result_metric.iloc[4],\n",
    "                    novoSpaRc_result_metric.iloc[4],SpaGE_result_metric.iloc[4],SpaOTsc_result_metric.iloc[4],stPlus_result_metric.iloc[4]],axis=1)\n",
    "spots_jsd.columns=['SpatialcoGCN','Tangram','LIGER','Seurat','novoSpaRc','SpaGE','SpaOTsc','stPlus']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pcc      ssim      rmse       jsd    cossim\n",
      "SpatialcoGCN  0.238628  0.214820  1.181394  0.394794  0.409382\n",
      "Tangram       0.236461  0.098857  1.231968  0.406046  0.437646\n",
      "LIGER         0.114918  0.066051  1.326564  0.464684  0.304116\n",
      "Seurat        0.121191  0.102490  1.321841  0.455448  0.317695\n",
      "novoSpaRc     0.061203  0.039829  1.367961  0.437481  0.367601\n",
      "SpaGE         0.201815  0.144942  1.257646  0.431175  0.360530\n",
      "SpaOTsc       0.099213  0.074209  1.338444  0.450979  0.328694\n",
      "stPlus        0.065431  0.047322  1.363835  0.521049  0.297753\n"
     ]
    }
   ],
   "source": [
    "mean_spots_pcc=spots_pcc.mean(0)\n",
    "mean_spots_ssim=spots_ssim.mean(0)\n",
    "mean_spots_rmse=spots_rmse.mean(0)\n",
    "mean_spots_jsd=spots_jsd.mean(0)\n",
    "mean_spots_cossim=spots_cossim.mean(0)\n",
    "df=pd.DataFrame({\"pcc\":mean_spots_pcc,\"ssim\":mean_spots_ssim,\"rmse\":mean_spots_rmse,\n",
    "                \"jsd\":mean_spots_jsd,\"cossim\":mean_spots_cossim})\n",
    "print(df)\n",
    "# df.to_csv(DataDir+'results/final_result.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        SpatialcoGCN  Tangram  LIGER  Seurat  novoSpaRc  SpaGE  SpaOTsc  \\\n",
      "pcc            1.000    0.875  0.500   0.625      0.125  0.750    0.375   \n",
      "ssim           1.000    0.625  0.375   0.750      0.125  0.875    0.500   \n",
      "rmse           1.000    0.875  0.500   0.625      0.125  0.750    0.375   \n",
      "jsd            1.000    0.875  0.250   0.375      0.625  0.750    0.500   \n",
      "cossim         0.875    1.000  0.250   0.375      0.750  0.625    0.500   \n",
      "AVR            0.975    0.850  0.375   0.550      0.350  0.750    0.450   \n",
      "\n",
      "        stPlus  \n",
      "pcc      0.250  \n",
      "ssim     0.250  \n",
      "rmse     0.250  \n",
      "jsd      0.125  \n",
      "cossim   0.125  \n",
      "AVR      0.200  \n"
     ]
    }
   ],
   "source": [
    "final_result=df\n",
    "ordered_result=pd.DataFrame(np.ones((final_result.shape[1],final_result.shape[0])),columns=final_result.index,index=final_result.columns)\n",
    "ordered_result.iloc[0,:]=final_result.iloc[:,0].rank(ascending = True,method='first')\n",
    "ordered_result.iloc[1,:]=final_result.iloc[:,1].rank(ascending = True,method='first')\n",
    "ordered_result.iloc[2,:]=final_result.iloc[:,2].rank(ascending = False,method='first')\n",
    "ordered_result.iloc[3,:]=final_result.iloc[:,3].rank(ascending = False,method='first')\n",
    "ordered_result.iloc[4,:]=final_result.iloc[:,4].rank(ascending = True,method='first')\n",
    "ordered_result=ordered_result/8\n",
    "ordered_result.loc[\"AVR\"]=ordered_result.mean(0)\n",
    "print(ordered_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "fig,axes = plt.subplots(ncols=3,nrows=2,figsize=(12,6))\n",
    "order = ['SpatialcoGCN','Tangram','LIGER','Seurat','novoSpaRc','SpaGE','SpaOTsc','stPlus']\n",
    "colors = [\"#988DC3\",\"#9de846\", '#F9EC31', \"#BBA8D1\", \"#7BD1F1\", \"#F5A8C7\",  \"#A2CBCF\",\"#D6DE23\",\"#1AB5B7\"]\n",
    "sns.boxplot(data=spots_pcc,order=order,palette=colors,ax=axes[0,0],orient='h', showfliers = False,showmeans=True)\n",
    "axes[0,0].set_title('PCC of locations')\n",
    "sns.boxplot(data=spots_ssim,order=order,palette=colors,ax=axes[0,1],orient='h', showfliers = False,showmeans=True)\n",
    "axes[0,1].set_title('SSIM of locations')\n",
    "sns.boxplot(data=spots_rmse,order=order,palette=colors,ax=axes[1,0],orient='h', showfliers = False,showmeans=True)\n",
    "axes[1,0].set_title('RMSE of locations')\n",
    "sns.boxplot(data=spots_jsd,order=order,palette=colors,ax=axes[1,1],orient='h', showfliers = False,showmeans=True)\n",
    "axes[1,1].set_title('JSD of locations')\n",
    "sns.boxplot(data=spots_cossim,order=order,palette=colors,ax=axes[0,2],orient='h', showfliers = False,showmeans=True)\n",
    "axes[0,2].set_title('Cossim of locations')\n",
    "\n",
    "sns.barplot(data=ordered_result.iloc[[-1],:],order=order,palette=colors,ax=axes[1,2],orient='h',errorbar=('ci', 75))\n",
    "axes[1,2].set_title('Average Rank Score')\n",
    "axes[1,2].spines['top'].set_visible(False)\n",
    "axes[1,2].spines['right'].set_visible(False)\n",
    "\n",
    "#plt.delaxes(axes[1, 2])\n",
    "plt.tight_layout()\n",
    "# plot_file = os.path.join(DataDir+\"results/metrics_plot.pdf\")\n",
    "# fig.savefig(plot_file,dpi=600,bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "yw",
   "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": "3.9.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "ebbdff288f15729aecf796ae30123e3e6c6398eceb1e4a6b8963c1ec326e4375"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}