FigureGeneration/Figure6a.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2773674e-7588-411d-9e0c-b81b39d78d18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scanpy==1.8.2 anndata==0.8.0 umap==0.5.2 numpy==1.20.3 scipy==1.7.3 pandas==1.3.5 scikit-learn==1.0.2 statsmodels==0.13.1 python-igraph==0.9.8 pynndescent==0.5.5\n",
      "squidpy==1.1.2\n"
     ]
    }
   ],
   "source": [
    "from soview import *\n",
    "import pandas as pd\n",
    "import datetime\n",
    "import palettable\n",
    "import squarify"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7348b358-982e-4bfc-8dd2-6d3a520a49ac",
   "metadata": {},
   "source": [
    "use http://www.interactivenn.net/index.html to plot venn plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "59dbdbb5-d1f8-40ce-862d-bf9814d9ffcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "method_review = pd.read_excel('method_review_data.xlsx',sheet_name=0,header=None)\n",
    "method_dataset = pd.read_excel('method_review_data.xlsx',sheet_name=1,header=None)\n",
    "review = pd.read_excel('method_review_data.xlsx',sheet_name=2,header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2430fcb4-9637-4ca6-afe2-5254f069d5fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "method_review = method_review.replace(np.nan,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "451f59ca-14cb-47dc-8375-512c74084f4e",
   "metadata": {},
   "source": [
    "## venn plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9d1cf917-a9dc-450e-99ff-c9d2662fd003",
   "metadata": {},
   "outputs": [],
   "source": [
    "review_dict = {\n",
    "    '1':[],\n",
    "    '2':[],\n",
    "    '3':[],\n",
    "    '4':[],\n",
    "    '5':[],\n",
    "\n",
    "    '6':[],\n",
    "\n",
    "    '7':[],\n",
    "}\n",
    "for i in range(len(method_review)):\n",
    "    method_name = method_review.iloc[i][0]\n",
    "    for j in range(3,10):\n",
    "        if method_review.iloc[i][j] !=0:\n",
    "            review_dict[str(j-2)].append(method_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "da74febe-2020-40fd-bc27-478d7b35b747",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trendsceek\n",
      "SpatialDE\n",
      "SPARK\n",
      "SPARK-X\n",
      "sepal\n",
      "SpaGCN[SA]\n",
      "GLISS\n",
      "stLearn\n",
      "MULTILAYER\n",
      "smfishHMRF\n",
      "BayesSpace[SC]\n",
      "SC-MEB\n",
      "STAGATE\n",
      "SpaGCN[SC]\n",
      "spatialDWLS\n",
      "SPOTlight \n",
      "RCTD\n",
      "stereoscope\n",
      "DSTG\n",
      "Tangram[SD]\n",
      "Cell2location\n",
      "MIA\n",
      "Tangram[GI]\n",
      "gimVI\n",
      "Harmony\n",
      "LIGER\n",
      "SpaGE\n",
      "stPlus\n",
      "HistoGene\n",
      "ST-Net\n",
      "xFuse\n",
      "SVCA\n",
      "GCNG\n",
      "NCEM\n",
      "MISTy\n",
      "Squidpy\n",
      "BayesSpace[RE]\n",
      "Seurat\n"
     ]
    }
   ],
   "source": [
    "tmp = review_dict['1']\n",
    "for i in tmp:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d7122956-0d16-433c-85ce-7ca10c3cff2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trendsceek\n",
      "SpatialDE\n",
      "SPARK\n",
      "stLearn\n",
      "BayesSpace[SC]\n",
      "spatialDWLS\n",
      "SPOTlight \n",
      "RCTD\n",
      "stereoscope\n",
      "Tangram[SD]\n",
      "Cell2location\n",
      "MIA\n",
      "Tangram[GI]\n",
      "ST-Net\n",
      "xFuse\n",
      "JSTA\n",
      "Baysor\n",
      "BayesSpace[RE]\n",
      "pciSeq\n",
      "Giotto\n",
      "Seurat\n",
      "DestVI\n",
      "FICT\n",
      "Haystack\n",
      "PASTE\n"
     ]
    }
   ],
   "source": [
    "tmp = review_dict['5']\n",
    "for i in tmp:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22813330-bbaf-4c80-92cb-aa746fdc2eed",
   "metadata": {},
   "source": [
    "## method_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d6a6763a-2d5f-4903-84be-b2f5b9d7a077",
   "metadata": {},
   "outputs": [],
   "source": [
    "method_dataset = method_dataset.replace(np.nan,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "02b36c8f-0b4f-44a9-89fa-b312de755a81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     -1\n",
       "1     -1\n",
       "2     -1\n",
       "3   -1.0\n",
       "4     -1\n",
       "5     -1\n",
       "6   -1.0\n",
       "7   -1.0\n",
       "8     -1\n",
       "Name: 4, dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "method_dataset.iloc[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e65044f4-c341-4c72-a12b-553bb759c6ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "method_dataset = method_dataset.drop(index=[4,37,49])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "98a25fe3-f8ad-4c5b-9e37-16fd46ffe862",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>is_covered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Trendsceek</td>\n",
       "      <td>Identification of spatial expression trends in...</td>\n",
       "      <td>22</td>\n",
       "      <td>93.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SpatialDE</td>\n",
       "      <td>SpatialDE: identification of spatially variabl...</td>\n",
       "      <td>22</td>\n",
       "      <td>93.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SPARK</td>\n",
       "      <td>Statistical analysis of spatial expression pat...</td>\n",
       "      <td>22</td>\n",
       "      <td>34.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SPARK-X</td>\n",
       "      <td>SPARK-X: non-parametric modeling enables scala...</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>90</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>sepal</td>\n",
       "      <td>sepal: identifying transcript profiles with sp...</td>\n",
       "      <td>78</td>\n",
       "      <td>19.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>MERINGUE</td>\n",
       "      <td>Characterizing spatial gene expression heterog...</td>\n",
       "      <td>34</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Haystack</td>\n",
       "      <td>A clustering-independent method for finding di...</td>\n",
       "      <td>78</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>PASTE</td>\n",
       "      <td>Alignment and integration of spatial transcrip...</td>\n",
       "      <td>22</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>scGCO</td>\n",
       "      <td>Identification of spatially variable genes wit...</td>\n",
       "      <td>22</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>SEDR</td>\n",
       "      <td>Unsupervised Spatial Embedded Deep Representat...</td>\n",
       "      <td>6</td>\n",
       "      <td>78.0</td>\n",
       "      <td>82</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0                                                  1   2     3  \\\n",
       "0   Trendsceek  Identification of spatial expression trends in...  22  93.0   \n",
       "1    SpatialDE  SpatialDE: identification of spatially variabl...  22  93.0   \n",
       "2        SPARK  Statistical analysis of spatial expression pat...  22  34.0   \n",
       "3      SPARK-X  SPARK-X: non-parametric modeling enables scala...   1   2.0   \n",
       "5        sepal  sepal: identifying transcript profiles with sp...  78  19.0   \n",
       "..         ...                                                ...  ..   ...   \n",
       "63    MERINGUE  Characterizing spatial gene expression heterog...  34   2.0   \n",
       "64    Haystack  A clustering-independent method for finding di...  78  -1.0   \n",
       "65       PASTE  Alignment and integration of spatial transcrip...  22   6.0   \n",
       "66       scGCO  Identification of spatially variable genes wit...  22  -1.0   \n",
       "67        SEDR  Unsupervised Spatial Embedded Deep Representat...   6  78.0   \n",
       "\n",
       "     4   5    6    7   8  is_covered  \n",
       "0   -1  -1 -1.0 -1.0  -1           1  \n",
       "1   -1  -1 -1.0 -1.0  -1           1  \n",
       "2   -1  -1 -1.0 -1.0  -1           1  \n",
       "3   90  -1 -1.0 -1.0  -1           1  \n",
       "5   -1  -1 -1.0 -1.0  -1           1  \n",
       "..  ..  ..  ...  ...  ..         ...  \n",
       "63  -1  -1 -1.0 -1.0  -1           1  \n",
       "64  -1  -1 -1.0 -1.0  -1           1  \n",
       "65  -1  -1 -1.0 -1.0  -1           1  \n",
       "66  -1  -1 -1.0 -1.0  -1           1  \n",
       "67  82  -1 -1.0 -1.0  -1           1  \n",
       "\n",
       "[65 rows x 10 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "method_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "bf4490ca-93ce-4c83-95a4-190cbe34e13f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "is_covered_list = []\n",
    "for i in range(len(method_dataset)):\n",
    "    is_covered = 1\n",
    "    positive_count = 0\n",
    "    for j in range(2,9):\n",
    "        if 'et al' in str(method_dataset.iloc[i][j]):\n",
    "            is_covered = 0\n",
    "        else:\n",
    "            if int(method_dataset.iloc[i][j])>=0:\n",
    "                positive_count+=1\n",
    "    if is_covered==0:\n",
    "        if positive_count>0:\n",
    "            is_covered_list.append(1)\n",
    "        else:\n",
    "            is_covered_list.append(0)\n",
    "    else:\n",
    "        is_covered_list.append(2)\n",
    "\n",
    "\n",
    "        \n",
    "    \n",
    "method_dataset['is_covered'] = is_covered_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "3a7c92aa-c7d7-4bf5-a5da-d681782cfef0",
   "metadata": {},
   "outputs": [],
   "source": [
    "method_dataset = method_dataset.set_index(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "74e65f68-a2dc-4f3c-bb61-69338bd8abed",
   "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>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>is_covered</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Trendsceek</th>\n",
       "      <td>Identification of spatial expression trends in...</td>\n",
       "      <td>22</td>\n",
       "      <td>93.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpatialDE</th>\n",
       "      <td>SpatialDE: identification of spatially variabl...</td>\n",
       "      <td>22</td>\n",
       "      <td>93.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPARK</th>\n",
       "      <td>Statistical analysis of spatial expression pat...</td>\n",
       "      <td>22</td>\n",
       "      <td>34.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPARK-X</th>\n",
       "      <td>SPARK-X: non-parametric modeling enables scala...</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>90</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal</th>\n",
       "      <td>sepal: identifying transcript profiles with sp...</td>\n",
       "      <td>78</td>\n",
       "      <td>19.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</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>MERINGUE</th>\n",
       "      <td>Characterizing spatial gene expression heterog...</td>\n",
       "      <td>34</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haystack</th>\n",
       "      <td>A clustering-independent method for finding di...</td>\n",
       "      <td>78</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PASTE</th>\n",
       "      <td>Alignment and integration of spatial transcrip...</td>\n",
       "      <td>22</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scGCO</th>\n",
       "      <td>Identification of spatially variable genes wit...</td>\n",
       "      <td>22</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEDR</th>\n",
       "      <td>Unsupervised Spatial Embedded Deep Representat...</td>\n",
       "      <td>6</td>\n",
       "      <td>78.0</td>\n",
       "      <td>82</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                            1   2     3   4  \\\n",
       "0                                                                             \n",
       "Trendsceek  Identification of spatial expression trends in...  22  93.0  -1   \n",
       "SpatialDE   SpatialDE: identification of spatially variabl...  22  93.0  -1   \n",
       "SPARK       Statistical analysis of spatial expression pat...  22  34.0  -1   \n",
       "SPARK-X     SPARK-X: non-parametric modeling enables scala...   1   2.0  90   \n",
       "sepal       sepal: identifying transcript profiles with sp...  78  19.0  -1   \n",
       "...                                                       ...  ..   ...  ..   \n",
       "MERINGUE    Characterizing spatial gene expression heterog...  34   2.0  -1   \n",
       "Haystack    A clustering-independent method for finding di...  78  -1.0  -1   \n",
       "PASTE       Alignment and integration of spatial transcrip...  22   6.0  -1   \n",
       "scGCO       Identification of spatially variable genes wit...  22  -1.0  -1   \n",
       "SEDR        Unsupervised Spatial Embedded Deep Representat...   6  78.0  82   \n",
       "\n",
       "             5    6    7   8  is_covered  \n",
       "0                                         \n",
       "Trendsceek  -1 -1.0 -1.0  -1           2  \n",
       "SpatialDE   -1 -1.0 -1.0  -1           2  \n",
       "SPARK       -1 -1.0 -1.0  -1           2  \n",
       "SPARK-X     -1 -1.0 -1.0  -1           2  \n",
       "sepal       -1 -1.0 -1.0  -1           2  \n",
       "...         ..  ...  ...  ..         ...  \n",
       "MERINGUE    -1 -1.0 -1.0  -1           2  \n",
       "Haystack    -1 -1.0 -1.0  -1           2  \n",
       "PASTE       -1 -1.0 -1.0  -1           2  \n",
       "scGCO       -1 -1.0 -1.0  -1           2  \n",
       "SEDR        -1 -1.0 -1.0  -1           2  \n",
       "\n",
       "[65 rows x 9 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "method_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "bb86d4f9-8997-4b4e-99df-06f15662c0c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "method_review = method_review.set_index(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "a4e72ec7-c842-4a9e-8870-9a65ff71be56",
   "metadata": {},
   "outputs": [],
   "source": [
    "method_dataset['type'] = method_review.loc[method_dataset.index][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "e1c97f33-eabd-4fe9-89fe-a715a1518eb3",
   "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>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>is_covered</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Trendsceek</th>\n",
       "      <td>Identification of spatial expression trends in...</td>\n",
       "      <td>22</td>\n",
       "      <td>93.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>SE analysis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpatialDE</th>\n",
       "      <td>SpatialDE: identification of spatially variabl...</td>\n",
       "      <td>22</td>\n",
       "      <td>93.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>SE analysis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPARK</th>\n",
       "      <td>Statistical analysis of spatial expression pat...</td>\n",
       "      <td>22</td>\n",
       "      <td>34.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>SE analysis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPARK-X</th>\n",
       "      <td>SPARK-X: non-parametric modeling enables scala...</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>90</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>SE analysis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal</th>\n",
       "      <td>sepal: identifying transcript profiles with sp...</td>\n",
       "      <td>78</td>\n",
       "      <td>19.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>SE analysis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MERINGUE</th>\n",
       "      <td>Characterizing spatial gene expression heterog...</td>\n",
       "      <td>34</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>SE analysis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haystack</th>\n",
       "      <td>A clustering-independent method for finding di...</td>\n",
       "      <td>78</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>SE analysis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PASTE</th>\n",
       "      <td>Alignment and integration of spatial transcrip...</td>\n",
       "      <td>22</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>Alignment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scGCO</th>\n",
       "      <td>Identification of spatially variable genes wit...</td>\n",
       "      <td>22</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>SE analysis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEDR</th>\n",
       "      <td>Unsupervised Spatial Embedded Deep Representat...</td>\n",
       "      <td>6</td>\n",
       "      <td>78.0</td>\n",
       "      <td>82</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>Spatial clustering</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                            1   2     3   4  \\\n",
       "0                                                                             \n",
       "Trendsceek  Identification of spatial expression trends in...  22  93.0  -1   \n",
       "SpatialDE   SpatialDE: identification of spatially variabl...  22  93.0  -1   \n",
       "SPARK       Statistical analysis of spatial expression pat...  22  34.0  -1   \n",
       "SPARK-X     SPARK-X: non-parametric modeling enables scala...   1   2.0  90   \n",
       "sepal       sepal: identifying transcript profiles with sp...  78  19.0  -1   \n",
       "...                                                       ...  ..   ...  ..   \n",
       "MERINGUE    Characterizing spatial gene expression heterog...  34   2.0  -1   \n",
       "Haystack    A clustering-independent method for finding di...  78  -1.0  -1   \n",
       "PASTE       Alignment and integration of spatial transcrip...  22   6.0  -1   \n",
       "scGCO       Identification of spatially variable genes wit...  22  -1.0  -1   \n",
       "SEDR        Unsupervised Spatial Embedded Deep Representat...   6  78.0  82   \n",
       "\n",
       "             5    6    7   8  is_covered                type  \n",
       "0                                                             \n",
       "Trendsceek  -1 -1.0 -1.0  -1           2         SE analysis  \n",
       "SpatialDE   -1 -1.0 -1.0  -1           2         SE analysis  \n",
       "SPARK       -1 -1.0 -1.0  -1           2         SE analysis  \n",
       "SPARK-X     -1 -1.0 -1.0  -1           2         SE analysis  \n",
       "sepal       -1 -1.0 -1.0  -1           2         SE analysis  \n",
       "...         ..  ...  ...  ..         ...                 ...  \n",
       "MERINGUE    -1 -1.0 -1.0  -1           2         SE analysis  \n",
       "Haystack    -1 -1.0 -1.0  -1           2         SE analysis  \n",
       "PASTE       -1 -1.0 -1.0  -1           2           Alignment  \n",
       "scGCO       -1 -1.0 -1.0  -1           2         SE analysis  \n",
       "SEDR        -1 -1.0 -1.0  -1           2  Spatial clustering  \n",
       "\n",
       "[65 rows x 10 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "method_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "9853f6e1-5d74-4728-94a7-90955951c105",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_unique = method_dataset.values[:,1:8].astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "7364feac-34ad-44c8-ab8b-16bc0a9f6c1d",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['22', '93.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['22', '93.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['22', '34.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['1', '2.0', '90', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['78', '19.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['15', '6.0', '78', '91', '-1.0', '-1.0', '-1'],\n",
       "       ['22', '-1.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['78', '6.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['22', '18.0', '15', '76', '2.0', '20.0', '-1'],\n",
       "       ['Zhu et al., 2018', '-1.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['6', '19.0', '78', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['6', '-1.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['6', '19.0', '82', '78', '-1.0', '-1.0', '-1'],\n",
       "       ['15', '6.0', '78', '91', '-1.0', '-1.0', '-1'],\n",
       "       ['38', '18.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['78', '-1.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['1', '2.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['78', '18.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['1', '78.0', '15', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['30', '-1.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['Kleshchevnikov et al., 2022', '1.0', '78', '11', '-1.0', '-1.0',\n",
       "        '-1'],\n",
       "       ['15', '-1.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['30', '-1.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['35', '91.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['34', '-1.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['91', '-1.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['35', '91.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['35', '91.0', '34', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['26', '-1.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['26', '-1.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['28', '22.0', '78', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['93', '-1.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['38', '33.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['30', '-1.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['78', '-1.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['36', '34.0', '1', '78', '-1.0', '-1.0', '-1'],\n",
       "       ['34', '35.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['35', '34.0', 'Qian et al., 2020', '91', '38.0', '36.0',\n",
       "        'Petukhov et al., 2021'],\n",
       "       ['6', '19.0', '78', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['38', '91.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['35', '34.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['Qian et al., 2019', '-1.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['38', '78.0', '2', '35', '91.0', '34.0', '-1'],\n",
       "       ['38', '-1.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['22', '-1.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['78', '-1.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['78', '2.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['91', '-1.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['Lopez et al., 2022', '-1.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['78', '-1.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['22', '-1.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['22', '-1.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['2', '-1.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['34', '2.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['6', '78.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['34', '35.0', '91', 'Qian et al., 2019', '-1.0', '-1.0', '-1'],\n",
       "       ['34', '35.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['6', '-1.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['1', '21.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['34', '22.0', '76', '2', '-1.0', '-1.0', '-1'],\n",
       "       ['34', '2.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['78', '-1.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['22', '6.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['22', '-1.0', '-1', '-1', '-1.0', '-1.0', '-1'],\n",
       "       ['6', '78.0', '82', '-1', '-1.0', '-1.0', '-1']], dtype='<U27')"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "b289cd0d-e1c3-486a-9951-35cae8902725",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_unique = np.unique(dataset_unique)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "49cdfdd4-3b1d-4a80-9ea4-fd4de8957cc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['-1', '-1.0', '1', '1.0', '11', '15', '18.0', '19.0', '2', '2.0',\n",
       "       '20.0', '21.0', '22', '22.0', '26', '28', '30', '33.0', '34',\n",
       "       '34.0', '35', '35.0', '36', '36.0', '38', '38.0', '6', '6.0', '76',\n",
       "       '78', '78.0', '82', '90', '91', '91.0', '93', '93.0',\n",
       "       'Kleshchevnikov et al., 2022', 'Lopez et al., 2022',\n",
       "       'Petukhov et al., 2021', 'Qian et al., 2019', 'Qian et al., 2020',\n",
       "       'Zhu et al., 2018'], dtype='<U27')"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "b42fcdfa-f23b-4b28-a17b-7331c60d67d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_unique_remove_etal = []\n",
    "for i in dataset_unique:\n",
    "    if 'et al' not in i and '-1' not in i:\n",
    "        dataset_unique_remove_etal.append(i)\n",
    "dataset_unique_remove_etal = np.array(dataset_unique_remove_etal).astype('float').astype('int')\n",
    "# dataset_unique_remove_etal = \n",
    "dataset_unique_remove_etal = np.unique(dataset_unique_remove_etal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "fb505dad-d73a-46ad-8fca-78fd7e0d076f",
   "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>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>is_covered</th>\n",
       "      <th>type</th>\n",
       "      <th>...</th>\n",
       "      <th>dataset_34</th>\n",
       "      <th>dataset_35</th>\n",
       "      <th>dataset_36</th>\n",
       "      <th>dataset_38</th>\n",
       "      <th>dataset_76</th>\n",
       "      <th>dataset_78</th>\n",
       "      <th>dataset_82</th>\n",
       "      <th>dataset_90</th>\n",
       "      <th>dataset_91</th>\n",
       "      <th>dataset_93</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Trendsceek</th>\n",
       "      <td>Identification of spatial expression trends in...</td>\n",
       "      <td>22</td>\n",
       "      <td>93.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>SE analysis</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpatialDE</th>\n",
       "      <td>SpatialDE: identification of spatially variabl...</td>\n",
       "      <td>22</td>\n",
       "      <td>93.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>SE analysis</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPARK</th>\n",
       "      <td>Statistical analysis of spatial expression pat...</td>\n",
       "      <td>22</td>\n",
       "      <td>34.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>SE analysis</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPARK-X</th>\n",
       "      <td>SPARK-X: non-parametric modeling enables scala...</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>90</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>SE analysis</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal</th>\n",
       "      <td>sepal: identifying transcript profiles with sp...</td>\n",
       "      <td>78</td>\n",
       "      <td>19.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>SE analysis</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MERINGUE</th>\n",
       "      <td>Characterizing spatial gene expression heterog...</td>\n",
       "      <td>34</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>SE analysis</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haystack</th>\n",
       "      <td>A clustering-independent method for finding di...</td>\n",
       "      <td>78</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>SE analysis</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PASTE</th>\n",
       "      <td>Alignment and integration of spatial transcrip...</td>\n",
       "      <td>22</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>Alignment</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scGCO</th>\n",
       "      <td>Identification of spatially variable genes wit...</td>\n",
       "      <td>22</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>SE analysis</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEDR</th>\n",
       "      <td>Unsupervised Spatial Embedded Deep Representat...</td>\n",
       "      <td>6</td>\n",
       "      <td>78.0</td>\n",
       "      <td>82</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>Spatial clustering</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                            1   2     3   4  \\\n",
       "0                                                                             \n",
       "Trendsceek  Identification of spatial expression trends in...  22  93.0  -1   \n",
       "SpatialDE   SpatialDE: identification of spatially variabl...  22  93.0  -1   \n",
       "SPARK       Statistical analysis of spatial expression pat...  22  34.0  -1   \n",
       "SPARK-X     SPARK-X: non-parametric modeling enables scala...   1   2.0  90   \n",
       "sepal       sepal: identifying transcript profiles with sp...  78  19.0  -1   \n",
       "...                                                       ...  ..   ...  ..   \n",
       "MERINGUE    Characterizing spatial gene expression heterog...  34   2.0  -1   \n",
       "Haystack    A clustering-independent method for finding di...  78  -1.0  -1   \n",
       "PASTE       Alignment and integration of spatial transcrip...  22   6.0  -1   \n",
       "scGCO       Identification of spatially variable genes wit...  22  -1.0  -1   \n",
       "SEDR        Unsupervised Spatial Embedded Deep Representat...   6  78.0  82   \n",
       "\n",
       "             5    6    7   8  is_covered                type  ...  dataset_34  \\\n",
       "0                                                             ...               \n",
       "Trendsceek  -1 -1.0 -1.0  -1           2         SE analysis  ...         0.0   \n",
       "SpatialDE   -1 -1.0 -1.0  -1           2         SE analysis  ...         0.0   \n",
       "SPARK       -1 -1.0 -1.0  -1           2         SE analysis  ...         0.0   \n",
       "SPARK-X     -1 -1.0 -1.0  -1           2         SE analysis  ...         0.0   \n",
       "sepal       -1 -1.0 -1.0  -1           2         SE analysis  ...         0.0   \n",
       "...         ..  ...  ...  ..         ...                 ...  ...         ...   \n",
       "MERINGUE    -1 -1.0 -1.0  -1           2         SE analysis  ...         0.0   \n",
       "Haystack    -1 -1.0 -1.0  -1           2         SE analysis  ...         0.0   \n",
       "PASTE       -1 -1.0 -1.0  -1           2           Alignment  ...         0.0   \n",
       "scGCO       -1 -1.0 -1.0  -1           2         SE analysis  ...         0.0   \n",
       "SEDR        -1 -1.0 -1.0  -1           2  Spatial clustering  ...         0.0   \n",
       "\n",
       "            dataset_35  dataset_36  dataset_38  dataset_76  dataset_78  \\\n",
       "0                                                                        \n",
       "Trendsceek         0.0         0.0         0.0         0.0         0.0   \n",
       "SpatialDE          0.0         0.0         0.0         0.0         0.0   \n",
       "SPARK              0.0         0.0         0.0         0.0         0.0   \n",
       "SPARK-X            0.0         0.0         0.0         0.0         0.0   \n",
       "sepal              0.0         0.0         0.0         0.0         0.0   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "MERINGUE           0.0         0.0         0.0         0.0         0.0   \n",
       "Haystack           0.0         0.0         0.0         0.0         0.0   \n",
       "PASTE              0.0         0.0         0.0         0.0         0.0   \n",
       "scGCO              0.0         0.0         0.0         0.0         0.0   \n",
       "SEDR               0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "            dataset_82  dataset_90  dataset_91  dataset_93  \n",
       "0                                                           \n",
       "Trendsceek         0.0         0.0         0.0         0.0  \n",
       "SpatialDE          0.0         0.0         0.0         0.0  \n",
       "SPARK              0.0         0.0         0.0         0.0  \n",
       "SPARK-X            0.0         0.0         0.0         0.0  \n",
       "sepal              0.0         0.0         0.0         0.0  \n",
       "...                ...         ...         ...         ...  \n",
       "MERINGUE           0.0         0.0         0.0         0.0  \n",
       "Haystack           0.0         0.0         0.0         0.0  \n",
       "PASTE              0.0         0.0         0.0         0.0  \n",
       "scGCO              0.0         0.0         0.0         0.0  \n",
       "SEDR               0.0         0.0         0.0         0.0  \n",
       "\n",
       "[65 rows x 34 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "method_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "442a350f-7d7e-4c32-99fe-d23a68e88fbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "for dsu in dataset_unique_remove_etal:\n",
    "    column_name = f'dataset_{dsu}'\n",
    "    method_dataset[column_name] = np.zeros(shape=(len(method_dataset),))\n",
    "\n",
    "for i in range(len(method_dataset)):\n",
    "    for j in range(2,9):\n",
    "        cur_dataset = str(method_dataset.iloc[i][j])\n",
    "        if 'et al' in cur_dataset:\n",
    "            continue\n",
    "        if '-1' in cur_dataset:\n",
    "            continue\n",
    "        cur_dataset_int = int(float(cur_dataset))\n",
    "        method_dataset.iloc[i, method_dataset.columns.get_loc(f'dataset_{cur_dataset_int}')] = 1\n",
    "        # method_dataset.iloc[i][f'dataset_{cur_dataset_int}']=1\n",
    "        \n",
    "        \n",
    "# dataset_dict = {}\n",
    "# for i in range(len(method_dataset)):\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "e238f529-6b6b-4fa9-895f-9ad87901ad87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "method_dataset.loc['PASTE','dataset_6']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "db38e768-90db-46ef-9d1a-e68dd3c7b1ad",
   "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>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>is_covered</th>\n",
       "      <th>type</th>\n",
       "      <th>...</th>\n",
       "      <th>dataset_34</th>\n",
       "      <th>dataset_35</th>\n",
       "      <th>dataset_36</th>\n",
       "      <th>dataset_38</th>\n",
       "      <th>dataset_76</th>\n",
       "      <th>dataset_78</th>\n",
       "      <th>dataset_82</th>\n",
       "      <th>dataset_90</th>\n",
       "      <th>dataset_91</th>\n",
       "      <th>dataset_93</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Trendsceek</th>\n",
       "      <td>Identification of spatial expression trends in...</td>\n",
       "      <td>22</td>\n",
       "      <td>93.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>SE analysis</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpatialDE</th>\n",
       "      <td>SpatialDE: identification of spatially variabl...</td>\n",
       "      <td>22</td>\n",
       "      <td>93.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>SE analysis</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPARK</th>\n",
       "      <td>Statistical analysis of spatial expression pat...</td>\n",
       "      <td>22</td>\n",
       "      <td>34.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>SE analysis</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPARK-X</th>\n",
       "      <td>SPARK-X: non-parametric modeling enables scala...</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>90</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>SE analysis</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal</th>\n",
       "      <td>sepal: identifying transcript profiles with sp...</td>\n",
       "      <td>78</td>\n",
       "      <td>19.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>SE analysis</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MERINGUE</th>\n",
       "      <td>Characterizing spatial gene expression heterog...</td>\n",
       "      <td>34</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>SE analysis</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haystack</th>\n",
       "      <td>A clustering-independent method for finding di...</td>\n",
       "      <td>78</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>SE analysis</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PASTE</th>\n",
       "      <td>Alignment and integration of spatial transcrip...</td>\n",
       "      <td>22</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>Alignment</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scGCO</th>\n",
       "      <td>Identification of spatially variable genes wit...</td>\n",
       "      <td>22</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>SE analysis</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEDR</th>\n",
       "      <td>Unsupervised Spatial Embedded Deep Representat...</td>\n",
       "      <td>6</td>\n",
       "      <td>78.0</td>\n",
       "      <td>82</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>Spatial clustering</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                            1   2     3   4  \\\n",
       "0                                                                             \n",
       "Trendsceek  Identification of spatial expression trends in...  22  93.0  -1   \n",
       "SpatialDE   SpatialDE: identification of spatially variabl...  22  93.0  -1   \n",
       "SPARK       Statistical analysis of spatial expression pat...  22  34.0  -1   \n",
       "SPARK-X     SPARK-X: non-parametric modeling enables scala...   1   2.0  90   \n",
       "sepal       sepal: identifying transcript profiles with sp...  78  19.0  -1   \n",
       "...                                                       ...  ..   ...  ..   \n",
       "MERINGUE    Characterizing spatial gene expression heterog...  34   2.0  -1   \n",
       "Haystack    A clustering-independent method for finding di...  78  -1.0  -1   \n",
       "PASTE       Alignment and integration of spatial transcrip...  22   6.0  -1   \n",
       "scGCO       Identification of spatially variable genes wit...  22  -1.0  -1   \n",
       "SEDR        Unsupervised Spatial Embedded Deep Representat...   6  78.0  82   \n",
       "\n",
       "             5    6    7   8  is_covered                type  ...  dataset_34  \\\n",
       "0                                                             ...               \n",
       "Trendsceek  -1 -1.0 -1.0  -1           2         SE analysis  ...         0.0   \n",
       "SpatialDE   -1 -1.0 -1.0  -1           2         SE analysis  ...         0.0   \n",
       "SPARK       -1 -1.0 -1.0  -1           2         SE analysis  ...         1.0   \n",
       "SPARK-X     -1 -1.0 -1.0  -1           2         SE analysis  ...         0.0   \n",
       "sepal       -1 -1.0 -1.0  -1           2         SE analysis  ...         0.0   \n",
       "...         ..  ...  ...  ..         ...                 ...  ...         ...   \n",
       "MERINGUE    -1 -1.0 -1.0  -1           2         SE analysis  ...         1.0   \n",
       "Haystack    -1 -1.0 -1.0  -1           2         SE analysis  ...         0.0   \n",
       "PASTE       -1 -1.0 -1.0  -1           2           Alignment  ...         0.0   \n",
       "scGCO       -1 -1.0 -1.0  -1           2         SE analysis  ...         0.0   \n",
       "SEDR        -1 -1.0 -1.0  -1           2  Spatial clustering  ...         0.0   \n",
       "\n",
       "            dataset_35  dataset_36  dataset_38  dataset_76  dataset_78  \\\n",
       "0                                                                        \n",
       "Trendsceek         0.0         0.0         0.0         0.0         0.0   \n",
       "SpatialDE          0.0         0.0         0.0         0.0         0.0   \n",
       "SPARK              0.0         0.0         0.0         0.0         0.0   \n",
       "SPARK-X            0.0         0.0         0.0         0.0         0.0   \n",
       "sepal              0.0         0.0         0.0         0.0         1.0   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "MERINGUE           0.0         0.0         0.0         0.0         0.0   \n",
       "Haystack           0.0         0.0         0.0         0.0         1.0   \n",
       "PASTE              0.0         0.0         0.0         0.0         0.0   \n",
       "scGCO              0.0         0.0         0.0         0.0         0.0   \n",
       "SEDR               0.0         0.0         0.0         0.0         1.0   \n",
       "\n",
       "            dataset_82  dataset_90  dataset_91  dataset_93  \n",
       "0                                                           \n",
       "Trendsceek         0.0         0.0         0.0         1.0  \n",
       "SpatialDE          0.0         0.0         0.0         1.0  \n",
       "SPARK              0.0         0.0         0.0         0.0  \n",
       "SPARK-X            0.0         1.0         0.0         0.0  \n",
       "sepal              0.0         0.0         0.0         0.0  \n",
       "...                ...         ...         ...         ...  \n",
       "MERINGUE           0.0         0.0         0.0         0.0  \n",
       "Haystack           0.0         0.0         0.0         0.0  \n",
       "PASTE              0.0         0.0         0.0         0.0  \n",
       "scGCO              0.0         0.0         0.0         0.0  \n",
       "SEDR               1.0         0.0         0.0         0.0  \n",
       "\n",
       "[65 rows x 34 columns]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "method_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "7c5ce8a7-cebe-438f-9df5-bbcd8ddedda8",
   "metadata": {},
   "outputs": [],
   "source": [
    "method_dataset = method_dataset.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "9d5a22a9-ae34-40a3-8224-75d3640f48f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "method_dataset = method_dataset.set_index(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "73d94ccd-b8e8-4b1f-971b-b9b756e12cfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "method_dataset = method_dataset.rename(columns={0:'method',1:'method_pub',2:'2',3:'3',4:'4',5:'5',6:'6',7:'7',8:'8'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "13a61b9e-2b83-42ad-8301-11e95ff00e77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['method_pub', '2', '3', '4', '5', '6', '7', '8', 'is_covered', 'type',\n",
       "       'dataset_1', 'dataset_2', 'dataset_6', 'dataset_11', 'dataset_15',\n",
       "       'dataset_18', 'dataset_19', 'dataset_20', 'dataset_21', 'dataset_22',\n",
       "       'dataset_26', 'dataset_28', 'dataset_30', 'dataset_33', 'dataset_34',\n",
       "       'dataset_35', 'dataset_36', 'dataset_38', 'dataset_76', 'dataset_78',\n",
       "       'dataset_82', 'dataset_90', 'dataset_91', 'dataset_93'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "method_dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "acc888e4-a9bb-4a5f-ae24-ab9bc645ce76",
   "metadata": {},
   "outputs": [],
   "source": [
    "method_dataset = method_dataset.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "25950681-5a88-43a2-be5d-7bd06e374e75",
   "metadata": {},
   "outputs": [],
   "source": [
    "method_dataset = method_dataset.rename(columns={0:'method'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "b380014d-73da-4d03-a021-915e093429b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "method_dataset = method_dataset.astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "672abeca-e7bf-4da4-933c-eb25661794ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "method_dataset.to_feather('method_dataset.feather')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "b5ef3a0b-d175-41f5-81e2-001f89e1253d",
   "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>method</th>\n",
       "      <th>method_pub</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>is_covered</th>\n",
       "      <th>...</th>\n",
       "      <th>dataset_34</th>\n",
       "      <th>dataset_35</th>\n",
       "      <th>dataset_36</th>\n",
       "      <th>dataset_38</th>\n",
       "      <th>dataset_76</th>\n",
       "      <th>dataset_78</th>\n",
       "      <th>dataset_82</th>\n",
       "      <th>dataset_90</th>\n",
       "      <th>dataset_91</th>\n",
       "      <th>dataset_93</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Trendsceek</td>\n",
       "      <td>Identification of spatial expression trends in...</td>\n",
       "      <td>22</td>\n",
       "      <td>93.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SpatialDE</td>\n",
       "      <td>SpatialDE: identification of spatially variabl...</td>\n",
       "      <td>22</td>\n",
       "      <td>93.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SPARK</td>\n",
       "      <td>Statistical analysis of spatial expression pat...</td>\n",
       "      <td>22</td>\n",
       "      <td>34.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SPARK-X</td>\n",
       "      <td>SPARK-X: non-parametric modeling enables scala...</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>90</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sepal</td>\n",
       "      <td>sepal: identifying transcript profiles with sp...</td>\n",
       "      <td>78</td>\n",
       "      <td>19.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>MERINGUE</td>\n",
       "      <td>Characterizing spatial gene expression heterog...</td>\n",
       "      <td>34</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Haystack</td>\n",
       "      <td>A clustering-independent method for finding di...</td>\n",
       "      <td>78</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>PASTE</td>\n",
       "      <td>Alignment and integration of spatial transcrip...</td>\n",
       "      <td>22</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>scGCO</td>\n",
       "      <td>Identification of spatially variable genes wit...</td>\n",
       "      <td>22</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>SEDR</td>\n",
       "      <td>Unsupervised Spatial Embedded Deep Representat...</td>\n",
       "      <td>6</td>\n",
       "      <td>78.0</td>\n",
       "      <td>82</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        method                                         method_pub   2     3  \\\n",
       "0   Trendsceek  Identification of spatial expression trends in...  22  93.0   \n",
       "1    SpatialDE  SpatialDE: identification of spatially variabl...  22  93.0   \n",
       "2        SPARK  Statistical analysis of spatial expression pat...  22  34.0   \n",
       "3      SPARK-X  SPARK-X: non-parametric modeling enables scala...   1   2.0   \n",
       "4        sepal  sepal: identifying transcript profiles with sp...  78  19.0   \n",
       "..         ...                                                ...  ..   ...   \n",
       "60    MERINGUE  Characterizing spatial gene expression heterog...  34   2.0   \n",
       "61    Haystack  A clustering-independent method for finding di...  78  -1.0   \n",
       "62       PASTE  Alignment and integration of spatial transcrip...  22   6.0   \n",
       "63       scGCO  Identification of spatially variable genes wit...  22  -1.0   \n",
       "64        SEDR  Unsupervised Spatial Embedded Deep Representat...   6  78.0   \n",
       "\n",
       "     4   5     6     7   8 is_covered  ... dataset_34 dataset_35 dataset_36  \\\n",
       "0   -1  -1  -1.0  -1.0  -1          2  ...        0.0        0.0        0.0   \n",
       "1   -1  -1  -1.0  -1.0  -1          2  ...        0.0        0.0        0.0   \n",
       "2   -1  -1  -1.0  -1.0  -1          2  ...        1.0        0.0        0.0   \n",
       "3   90  -1  -1.0  -1.0  -1          2  ...        0.0        0.0        0.0   \n",
       "4   -1  -1  -1.0  -1.0  -1          2  ...        0.0        0.0        0.0   \n",
       "..  ..  ..   ...   ...  ..        ...  ...        ...        ...        ...   \n",
       "60  -1  -1  -1.0  -1.0  -1          2  ...        1.0        0.0        0.0   \n",
       "61  -1  -1  -1.0  -1.0  -1          2  ...        0.0        0.0        0.0   \n",
       "62  -1  -1  -1.0  -1.0  -1          2  ...        0.0        0.0        0.0   \n",
       "63  -1  -1  -1.0  -1.0  -1          2  ...        0.0        0.0        0.0   \n",
       "64  82  -1  -1.0  -1.0  -1          2  ...        0.0        0.0        0.0   \n",
       "\n",
       "   dataset_38 dataset_76 dataset_78 dataset_82 dataset_90 dataset_91  \\\n",
       "0         0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "1         0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "2         0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "3         0.0        0.0        0.0        0.0        1.0        0.0   \n",
       "4         0.0        0.0        1.0        0.0        0.0        0.0   \n",
       "..        ...        ...        ...        ...        ...        ...   \n",
       "60        0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "61        0.0        0.0        1.0        0.0        0.0        0.0   \n",
       "62        0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "63        0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "64        0.0        0.0        1.0        1.0        0.0        0.0   \n",
       "\n",
       "   dataset_93  \n",
       "0         1.0  \n",
       "1         1.0  \n",
       "2         0.0  \n",
       "3         0.0  \n",
       "4         0.0  \n",
       "..        ...  \n",
       "60        0.0  \n",
       "61        0.0  \n",
       "62        0.0  \n",
       "63        0.0  \n",
       "64        0.0  \n",
       "\n",
       "[65 rows x 35 columns]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "method_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "ecd572e5-aa0e-4813-944c-280990333537",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_used_list = []\n",
    "for i in method_dataset.columns:\n",
    "    if 'dataset' in i:\n",
    "        dataset_used_list.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "31515a90-398a-43c3-8e7f-d0068b1589a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "method_dataset_mat = method_dataset.loc[:,dataset_used_list].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "714dc94b-2bb5-4a41-b2f9-21ca1a086147",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(65, 24)"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "method_dataset_mat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "9c5d7448-b529-47fa-915e-46f62b7fef01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 799,
       "width": 428
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(method_dataset_mat,square=True,linewidths=0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1529993-a1e9-4206-aee4-c554c88b6dcc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "SOView",
   "language": "python",
   "name": "soview"
  },
  "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}