Compare original SN to Kanemaru SN cells.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rationale: Compare deconvoluted single cell datasets generated with different references (Kanemaru et al. vs Linscheid et al.)\n",
    "\n",
    "To demonstrate whether deconvoluting the human Sinus Node (SN) with Bulk2Space is a robust approach, we compared the single cell deconvoluted results obtained using the original reference dataset (Linscheid et al.) with those obtained using a different reference dataset (Kanemaru et al.)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scanpy\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as pearsonr\n",
    "import bulk2space\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the input files\n",
    "\n",
    "Loading the SN single cell data generated using Linscheid et al. (original dataset) and Kanemaru et al (different dataset).\n",
    "The following files will be loaded:\n",
    "- SN sc data generated from Linscheid et al.\n",
    "- SN sc meta generated from Linscheid et al.\n",
    "- SN sc data generated from Kanemaru et al.\n",
    "- SN sc meta generated from Kanemaru et al."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data......\n",
      "load data done!\n"
     ]
    }
   ],
   "source": [
    "# Sinus Node data generated from Linscheid et al (ORIGINAL)\n",
    "sn_ms_data = bulk2space.utils.load_data(\n",
    "    input_bulk_path=\"C:/Users/chelu/OneDrive - The University of Manchester/PhD OneDrive/Coding/Python Material/Bulk2space/Datasets/Halina Dobrzynski/Sinus Node/SN_human_bulk_cleaned.csv\",\n",
    "    input_sc_data_path=\"C:/Users/chelu/OneDrive - The University of Manchester/PhD OneDrive/Coding/Python Material/Bulk2space/Datasets/Halina Dobrzynski/Sinus Node/output/SN_human_from_mouse_sc_data.csv\",\n",
    "    input_sc_meta_path=\"C:/Users/chelu/OneDrive - The University of Manchester/PhD OneDrive/Coding/Python Material/Bulk2space/Datasets/Halina Dobrzynski/Sinus Node/output/SN_human_from_mouse_sc_celltype.csv\",\n",
    "    input_st_data_path=\"C:/Users/chelu/OneDrive - The University of Manchester/PhD OneDrive/Coding/Python Material/Bulk2space/Datasets/Halina Dobrzynski/Sinus Node/st_data.csv\",\n",
    "    input_st_meta_path=\"C:/Users/chelu/OneDrive - The University of Manchester/PhD OneDrive/Coding/Python Material/Bulk2space/Datasets/Halina Dobrzynski/Sinus Node/st_meta.csv\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cell</th>\n",
       "      <th>Cell_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C_1</td>\n",
       "      <td>Adipocytes III</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C_2</td>\n",
       "      <td>Sinus node myocytes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C_3</td>\n",
       "      <td>Macrophages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C_4</td>\n",
       "      <td>Adipocytes III</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C_5</td>\n",
       "      <td>Sinus node myocytes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5352</th>\n",
       "      <td>C_5353</td>\n",
       "      <td>Macrophages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5353</th>\n",
       "      <td>C_5354</td>\n",
       "      <td>Macrophages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5354</th>\n",
       "      <td>C_5355</td>\n",
       "      <td>Macrophages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5355</th>\n",
       "      <td>C_5356</td>\n",
       "      <td>Macrophages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5356</th>\n",
       "      <td>C_5357</td>\n",
       "      <td>Macrophages</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5357 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Cell            Cell_type\n",
       "0        C_1       Adipocytes III\n",
       "1        C_2  Sinus node myocytes\n",
       "2        C_3          Macrophages\n",
       "3        C_4       Adipocytes III\n",
       "4        C_5  Sinus node myocytes\n",
       "...      ...                  ...\n",
       "5352  C_5353          Macrophages\n",
       "5353  C_5354          Macrophages\n",
       "5354  C_5355          Macrophages\n",
       "5355  C_5356          Macrophages\n",
       "5356  C_5357          Macrophages\n",
       "\n",
       "[5357 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(sn_ms_data['input_sc_meta'])\n",
    "\n",
    "# Create a scanpy object for later use\n",
    "sc = scanpy.AnnData(sn_ms_data[\"input_sc_data\"].T)\n",
    "sc.obs = sn_ms_data[\"input_sc_meta\"][[\"Cell_type\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Cell</th>\n",
       "      <th>Cell_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>C_1</td>\n",
       "      <td>FB4_activated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>C_2</td>\n",
       "      <td>FB4_activated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>C_3</td>\n",
       "      <td>FB4_activated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>C_4</td>\n",
       "      <td>PC3_str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>C_5</td>\n",
       "      <td>FB4_activated</td>\n",
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      "text/plain": [
       "   Unnamed: 0 Cell      Cell_type\n",
       "0           0  C_1  FB4_activated\n",
       "1           1  C_2  FB4_activated\n",
       "2           2  C_3  FB4_activated\n",
       "3           3  C_4        PC3_str\n",
       "4           4  C_5  FB4_activated"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sinus Node data generated from Kanemaru et al.\n",
    "sn_kane_data = pd.read_csv(\"Central + peripheral SAN/output_SAN_complete/SAN_complete_sc_data.csv\")\n",
    "sn_kane_meta = pd.read_csv(\"Central + peripheral SAN/output_SAN_complete/SAN_complete_sc_celltype.csv\")\n",
    "\n",
    "sn_kane_meta.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation matrix\n",
    "\n",
    "Calculate 200 marker genes of SN cells generate using Linscheid et al,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = scanpy.AnnData(sn_ms_data[\"input_sc_data\"].T)\n",
    "sc.obs = sn_ms_data[\"input_sc_meta\"][[\"Cell_type\"]]\n",
    "scanpy.tl.rank_genes_groups(sc, \"Cell_type\", method=\"wilcoxon\")\n",
    "marker_df = pd.DataFrame(sc.uns[\"rank_genes_groups\"][\"names\"]).head(200)\n",
    "marker = list(np.unique(np.ravel(np.array(marker_df))))\n",
    "\n",
    "# the mean expression of 200 marker genes of input sc data\n",
    "sc_marker = sn_ms_data[\"input_sc_data\"].loc[marker, :].T\n",
    "new_index = sn_ms_data['input_sc_meta']['Cell'].to_list()\n",
    "sn_ms_data['input_sc_meta'] = sn_ms_data['input_sc_meta'].set_index('Cell') \n",
    "sc_marker[\"Cell_type\"] = sn_ms_data[\"input_sc_meta\"][\"Cell_type\"]\n",
    "sc_marker_mean = sc_marker.groupby(\"Cell_type\")[marker].mean()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A4GALT</th>\n",
       "      <th>AACS</th>\n",
       "      <th>ABCA1</th>\n",
       "      <th>ABCA6</th>\n",
       "      <th>ABCA8</th>\n",
       "      <th>ABCA9</th>\n",
       "      <th>ABHD15</th>\n",
       "      <th>ABI1</th>\n",
       "      <th>ABLIM2</th>\n",
       "      <th>ABLIM3</th>\n",
       "      <th>...</th>\n",
       "      <th>ZEB2</th>\n",
       "      <th>ZFHX3</th>\n",
       "      <th>ZFHX4</th>\n",
       "      <th>ZFPM2</th>\n",
       "      <th>ZNF185</th>\n",
       "      <th>ZNF385B</th>\n",
       "      <th>ZNF423</th>\n",
       "      <th>ZNF462</th>\n",
       "      <th>ZNF804B</th>\n",
       "      <th>ZSWIM6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell_type</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>Adipocytes III</th>\n",
       "      <td>0.254569</td>\n",
       "      <td>0.969846</td>\n",
       "      <td>1.278523</td>\n",
       "      <td>0.169589</td>\n",
       "      <td>0.212056</td>\n",
       "      <td>0.264903</td>\n",
       "      <td>0.385168</td>\n",
       "      <td>1.740469</td>\n",
       "      <td>0.111793</td>\n",
       "      <td>0.325266</td>\n",
       "      <td>...</td>\n",
       "      <td>1.988630</td>\n",
       "      <td>1.511998</td>\n",
       "      <td>0.766161</td>\n",
       "      <td>1.923833</td>\n",
       "      <td>0.111058</td>\n",
       "      <td>0.066902</td>\n",
       "      <td>1.088450</td>\n",
       "      <td>0.652292</td>\n",
       "      <td>0.254955</td>\n",
       "      <td>1.126906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fibroblasts II</th>\n",
       "      <td>0.571111</td>\n",
       "      <td>0.179639</td>\n",
       "      <td>0.887027</td>\n",
       "      <td>0.489026</td>\n",
       "      <td>0.605920</td>\n",
       "      <td>0.599685</td>\n",
       "      <td>0.074060</td>\n",
       "      <td>1.908632</td>\n",
       "      <td>0.099027</td>\n",
       "      <td>0.321999</td>\n",
       "      <td>...</td>\n",
       "      <td>2.147140</td>\n",
       "      <td>1.329200</td>\n",
       "      <td>0.587171</td>\n",
       "      <td>2.532043</td>\n",
       "      <td>0.066158</td>\n",
       "      <td>0.141318</td>\n",
       "      <td>0.889078</td>\n",
       "      <td>0.896516</td>\n",
       "      <td>0.296138</td>\n",
       "      <td>1.420086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Macrophages</th>\n",
       "      <td>0.208898</td>\n",
       "      <td>0.128620</td>\n",
       "      <td>0.938952</td>\n",
       "      <td>0.192719</td>\n",
       "      <td>0.125040</td>\n",
       "      <td>0.900125</td>\n",
       "      <td>0.068199</td>\n",
       "      <td>1.785459</td>\n",
       "      <td>0.095809</td>\n",
       "      <td>0.178641</td>\n",
       "      <td>...</td>\n",
       "      <td>2.716170</td>\n",
       "      <td>1.474500</td>\n",
       "      <td>0.305612</td>\n",
       "      <td>1.927865</td>\n",
       "      <td>0.059565</td>\n",
       "      <td>0.086084</td>\n",
       "      <td>0.499218</td>\n",
       "      <td>0.485112</td>\n",
       "      <td>0.193720</td>\n",
       "      <td>2.065986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neurons</th>\n",
       "      <td>0.231531</td>\n",
       "      <td>0.178607</td>\n",
       "      <td>0.746882</td>\n",
       "      <td>0.246140</td>\n",
       "      <td>0.290833</td>\n",
       "      <td>0.305251</td>\n",
       "      <td>0.042553</td>\n",
       "      <td>2.471123</td>\n",
       "      <td>0.109850</td>\n",
       "      <td>1.044463</td>\n",
       "      <td>...</td>\n",
       "      <td>1.327541</td>\n",
       "      <td>1.153753</td>\n",
       "      <td>0.931531</td>\n",
       "      <td>2.679212</td>\n",
       "      <td>0.339381</td>\n",
       "      <td>0.123671</td>\n",
       "      <td>0.456086</td>\n",
       "      <td>1.277222</td>\n",
       "      <td>2.162625</td>\n",
       "      <td>0.993463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sinus node myocytes</th>\n",
       "      <td>0.238069</td>\n",
       "      <td>0.367246</td>\n",
       "      <td>0.554757</td>\n",
       "      <td>0.155429</td>\n",
       "      <td>0.214873</td>\n",
       "      <td>0.206228</td>\n",
       "      <td>0.149913</td>\n",
       "      <td>1.514831</td>\n",
       "      <td>0.745457</td>\n",
       "      <td>0.343763</td>\n",
       "      <td>...</td>\n",
       "      <td>1.660301</td>\n",
       "      <td>0.899912</td>\n",
       "      <td>0.382194</td>\n",
       "      <td>2.299245</td>\n",
       "      <td>0.039805</td>\n",
       "      <td>0.845756</td>\n",
       "      <td>0.551702</td>\n",
       "      <td>0.729570</td>\n",
       "      <td>0.241310</td>\n",
       "      <td>0.952740</td>\n",
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       "<p>5 rows × 968 columns</p>\n",
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      "text/plain": [
       "                       A4GALT      AACS     ABCA1     ABCA6     ABCA8  \\\n",
       "Cell_type                                                               \n",
       "Adipocytes III       0.254569  0.969846  1.278523  0.169589  0.212056   \n",
       "Fibroblasts II       0.571111  0.179639  0.887027  0.489026  0.605920   \n",
       "Macrophages          0.208898  0.128620  0.938952  0.192719  0.125040   \n",
       "Neurons              0.231531  0.178607  0.746882  0.246140  0.290833   \n",
       "Sinus node myocytes  0.238069  0.367246  0.554757  0.155429  0.214873   \n",
       "\n",
       "                        ABCA9    ABHD15      ABI1    ABLIM2    ABLIM3  ...  \\\n",
       "Cell_type                                                              ...   \n",
       "Adipocytes III       0.264903  0.385168  1.740469  0.111793  0.325266  ...   \n",
       "Fibroblasts II       0.599685  0.074060  1.908632  0.099027  0.321999  ...   \n",
       "Macrophages          0.900125  0.068199  1.785459  0.095809  0.178641  ...   \n",
       "Neurons              0.305251  0.042553  2.471123  0.109850  1.044463  ...   \n",
       "Sinus node myocytes  0.206228  0.149913  1.514831  0.745457  0.343763  ...   \n",
       "\n",
       "                         ZEB2     ZFHX3     ZFHX4     ZFPM2    ZNF185  \\\n",
       "Cell_type                                                               \n",
       "Adipocytes III       1.988630  1.511998  0.766161  1.923833  0.111058   \n",
       "Fibroblasts II       2.147140  1.329200  0.587171  2.532043  0.066158   \n",
       "Macrophages          2.716170  1.474500  0.305612  1.927865  0.059565   \n",
       "Neurons              1.327541  1.153753  0.931531  2.679212  0.339381   \n",
       "Sinus node myocytes  1.660301  0.899912  0.382194  2.299245  0.039805   \n",
       "\n",
       "                      ZNF385B    ZNF423    ZNF462   ZNF804B    ZSWIM6  \n",
       "Cell_type                                                              \n",
       "Adipocytes III       0.066902  1.088450  0.652292  0.254955  1.126906  \n",
       "Fibroblasts II       0.141318  0.889078  0.896516  0.296138  1.420086  \n",
       "Macrophages          0.086084  0.499218  0.485112  0.193720  2.065986  \n",
       "Neurons              0.123671  0.456086  1.277222  2.162625  0.993463  \n",
       "Sinus node myocytes  0.845756  0.551702  0.729570  0.241310  0.952740  \n",
       "\n",
       "[5 rows x 968 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(sc_marker_mean)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate 200 marker genes of SN cells generate using Kanemaru et al."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "sn_kane_meta.index = list(sn_kane_meta[\"Cell\"])\n",
    "sn_kane_data_new = sn_kane_data.T\n",
    "new_header = sn_kane_data_new.iloc[0]\n",
    "sn_kane_data_new = sn_kane_data_new[1:]\n",
    "sn_kane_data_new.columns = new_header\n",
    "# ---------------------------------------------------------\n",
    "# Added this line to fix code breaking at groupby\n",
    "sn_kane_data_new = sn_kane_data_new.astype(float)\n",
    "# ---------------------------------------------------------\n",
    "\n",
    "# Extract mutual marker genes\n",
    "common_markers = list(set(sn_kane_data_new.columns).intersection(set(marker)))\n",
    "sn_kane_data_new = sn_kane_data_new[common_markers]\n",
    "\n",
    "sn_kane_data_new[\"Cell_type\"] = sn_kane_meta[\"Cell_type\"]\n",
    "sn_kane_marker_mean = sn_kane_data_new.groupby([\"Cell_type\"])[common_markers].mean()\n",
    "\n",
    "sc_marker_mean = sc_marker_mean[common_markers]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\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>Unnamed: 0</th>\n",
       "      <th>LTBP1</th>\n",
       "      <th>MT-CO1</th>\n",
       "      <th>PLCL2</th>\n",
       "      <th>CASK</th>\n",
       "      <th>NDUFA4</th>\n",
       "      <th>KIF6</th>\n",
       "      <th>C11orf54</th>\n",
       "      <th>COL8A1</th>\n",
       "      <th>ERBB4</th>\n",
       "      <th>SYNPO2</th>\n",
       "      <th>...</th>\n",
       "      <th>CSGALNACT1</th>\n",
       "      <th>ARHGAP28</th>\n",
       "      <th>CLIP2</th>\n",
       "      <th>SHANK2</th>\n",
       "      <th>WNK2</th>\n",
       "      <th>MT-CO3</th>\n",
       "      <th>DPP4</th>\n",
       "      <th>TMEM119</th>\n",
       "      <th>COLGALT2</th>\n",
       "      <th>TMEM120B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell_type</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>FB4_activated</th>\n",
       "      <td>2.037936</td>\n",
       "      <td>2.709564</td>\n",
       "      <td>0.248940</td>\n",
       "      <td>0.503479</td>\n",
       "      <td>0.355535</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.141967</td>\n",
       "      <td>1.899161</td>\n",
       "      <td>0.151794</td>\n",
       "      <td>1.174601</td>\n",
       "      <td>...</td>\n",
       "      <td>0.549244</td>\n",
       "      <td>0.048431</td>\n",
       "      <td>0.074076</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013711</td>\n",
       "      <td>2.367966</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.024933</td>\n",
       "      <td>0.824811</td>\n",
       "      <td>0.111443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC2_glial_NGF+</th>\n",
       "      <td>1.143074</td>\n",
       "      <td>2.894083</td>\n",
       "      <td>0.375144</td>\n",
       "      <td>0.597483</td>\n",
       "      <td>0.609486</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.251340</td>\n",
       "      <td>0.993193</td>\n",
       "      <td>0.427494</td>\n",
       "      <td>1.101191</td>\n",
       "      <td>...</td>\n",
       "      <td>0.899348</td>\n",
       "      <td>0.139076</td>\n",
       "      <td>0.243870</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.081866</td>\n",
       "      <td>2.903011</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.008132</td>\n",
       "      <td>0.347321</td>\n",
       "      <td>0.117665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC1_vent</th>\n",
       "      <td>0.045083</td>\n",
       "      <td>0.711945</td>\n",
       "      <td>0.000387</td>\n",
       "      <td>0.371978</td>\n",
       "      <td>0.014992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.567979</td>\n",
       "      <td>0.837601</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.237568</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.193325</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC3_str</th>\n",
       "      <td>0.173226</td>\n",
       "      <td>2.860225</td>\n",
       "      <td>0.125174</td>\n",
       "      <td>0.176727</td>\n",
       "      <td>0.327876</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.090975</td>\n",
       "      <td>0.018398</td>\n",
       "      <td>0.029199</td>\n",
       "      <td>0.863903</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001627</td>\n",
       "      <td>0.020410</td>\n",
       "      <td>0.124171</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.029623</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018241</td>\n",
       "      <td>0.047312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAN_P_cell</th>\n",
       "      <td>1.838200</td>\n",
       "      <td>2.013733</td>\n",
       "      <td>1.605384</td>\n",
       "      <td>0.283153</td>\n",
       "      <td>0.738591</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.334224</td>\n",
       "      <td>0.152026</td>\n",
       "      <td>2.155888</td>\n",
       "      <td>1.258881</td>\n",
       "      <td>...</td>\n",
       "      <td>0.856240</td>\n",
       "      <td>0.145996</td>\n",
       "      <td>0.004135</td>\n",
       "      <td>0.011501</td>\n",
       "      <td>1.143246</td>\n",
       "      <td>1.880239</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100706</td>\n",
       "      <td>0.172029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 968 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Unnamed: 0         LTBP1    MT-CO1     PLCL2      CASK    NDUFA4  KIF6  \\\n",
       "Cell_type                                                                \n",
       "FB4_activated   2.037936  2.709564  0.248940  0.503479  0.355535   0.0   \n",
       "NC2_glial_NGF+  1.143074  2.894083  0.375144  0.597483  0.609486   0.0   \n",
       "PC1_vent        0.045083  0.711945  0.000387  0.371978  0.014992   0.0   \n",
       "PC3_str         0.173226  2.860225  0.125174  0.176727  0.327876   0.0   \n",
       "SAN_P_cell      1.838200  2.013733  1.605384  0.283153  0.738591   0.0   \n",
       "\n",
       "Unnamed: 0      C11orf54    COL8A1     ERBB4    SYNPO2  ...  CSGALNACT1  \\\n",
       "Cell_type                                               ...               \n",
       "FB4_activated   0.141967  1.899161  0.151794  1.174601  ...    0.549244   \n",
       "NC2_glial_NGF+  0.251340  0.993193  0.427494  1.101191  ...    0.899348   \n",
       "PC1_vent        0.000000  0.000000  0.567979  0.837601  ...    0.000000   \n",
       "PC3_str         0.090975  0.018398  0.029199  0.863903  ...    0.001627   \n",
       "SAN_P_cell      0.334224  0.152026  2.155888  1.258881  ...    0.856240   \n",
       "\n",
       "Unnamed: 0      ARHGAP28     CLIP2    SHANK2      WNK2    MT-CO3  DPP4  \\\n",
       "Cell_type                                                                \n",
       "FB4_activated   0.048431  0.074076  0.000000  0.013711  2.367966   0.0   \n",
       "NC2_glial_NGF+  0.139076  0.243870  0.000049  0.081866  2.903011   0.0   \n",
       "PC1_vent        0.000000  0.000000  0.237568  0.000000  1.193325   0.0   \n",
       "PC3_str         0.020410  0.124171  0.000039  0.000000  3.029623   0.0   \n",
       "SAN_P_cell      0.145996  0.004135  0.011501  1.143246  1.880239   0.0   \n",
       "\n",
       "Unnamed: 0       TMEM119  COLGALT2  TMEM120B  \n",
       "Cell_type                                     \n",
       "FB4_activated   0.024933  0.824811  0.111443  \n",
       "NC2_glial_NGF+  0.008132  0.347321  0.117665  \n",
       "PC1_vent        0.000000  0.000000  0.000000  \n",
       "PC3_str         0.000000  0.018241  0.047312  \n",
       "SAN_P_cell      0.000000  0.100706  0.172029  \n",
       "\n",
       "[5 rows x 968 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(sn_kane_marker_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transpose both datasets prior to correlation calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc_marker_mean = sc_marker_mean.T\n",
    "sn_kane_marker_mean = sn_kane_marker_mean.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the correlation matrix between SN single cells generated from different references"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5/5 [00:00<00:00, 1665.46it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coeffmat = np.zeros(\n",
    "    (sc_marker_mean.shape[1], sn_kane_marker_mean.shape[1]))\n",
    "for i in tqdm(range(sc_marker_mean.shape[1])):\n",
    "    for j in range(sn_kane_marker_mean.shape[1]):\n",
    "\n",
    "        corrtest = pearsonr.pearsonr(\n",
    "            sc_marker_mean[sc_marker_mean.columns[i]],\n",
    "            sn_kane_marker_mean[sn_kane_marker_mean.columns[j]],\n",
    "        )\n",
    "        coeffmat[i, j] = corrtest[0]\n",
    "\n",
    "tm_ct = list(sc_marker_mean.columns)\n",
    "ts_ct = list(sn_kane_marker_mean.columns)\n",
    "\n",
    "# plot\n",
    "fig, ax = plt.subplots()\n",
    "im = ax.imshow(coeffmat, cmap=\"RdBu_r\")\n",
    "ax.set_xticks(np.arange(len(ts_ct)))\n",
    "ax.set_xticklabels(ts_ct, fontsize = 12)\n",
    "ax.set_yticks(np.arange(len(tm_ct)))\n",
    "ax.set_yticklabels(tm_ct, fontsize = 12)\n",
    "plt.xlabel(\"Human reference\", fontweight = 'bold', fontsize = 13)\n",
    "plt.ylabel(\"Mouse reference\", fontweight = 'bold', fontsize = 13)\n",
    "plt.setp(ax.get_xticklabels(), rotation = 45,  ha=\"right\", rotation_mode=\"anchor\")\n",
    "plt.colorbar(im)\n",
    "ax.set_title(\"Expression correlation\", fontsize = 13)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('Complete comparison.png', dpi =600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Polishing the figure\n",
    "\n",
    "Keep mutual cells across datasets for an appropriate comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cells from Kanemaru et al.\n",
    "reordered_cells = ['FB4_activated', \n",
    "                   'NC2_glial_NGF+',\n",
    "                   'SAN_P_cell']\n",
    "sn_kane_marker_mean = sn_kane_marker_mean[reordered_cells]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cells from Linscheid et al.\n",
    "formatted_names = ['Fibroblasts II', \n",
    "                   'Neurons',\n",
    "                   'Sinus node myocytes']\n",
    "\n",
    "sc_marker_mean = sc_marker_mean[formatted_names]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run again the correlation matrix and plot the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [00:00<00:00, 2998.07it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coeffmat = np.zeros(\n",
    "    (sc_marker_mean.shape[1], sn_kane_marker_mean.shape[1]))\n",
    "for i in tqdm(range(sc_marker_mean.shape[1])):\n",
    "    for j in range(sn_kane_marker_mean.shape[1]):\n",
    "\n",
    "        corrtest = pearsonr.pearsonr(\n",
    "            sc_marker_mean[sc_marker_mean.columns[i]],\n",
    "            sn_kane_marker_mean[sn_kane_marker_mean.columns[j]],\n",
    "        )\n",
    "        coeffmat[i, j] = corrtest[0]\n",
    "\n",
    "tm_ct = list(sc_marker_mean.columns)\n",
    "ts_ct = list(sn_kane_marker_mean.columns)\n",
    "\n",
    "#plot again\n",
    "fig, ax = plt.subplots()\n",
    "im = ax.imshow(coeffmat, cmap=\"RdBu_r\")\n",
    "ax.set_xticks(np.arange(len(ts_ct)))\n",
    "ax.set_xticklabels(ts_ct, fontsize = 12)\n",
    "ax.set_yticks(np.arange(len(tm_ct)))\n",
    "ax.set_yticklabels(tm_ct, fontsize = 12)\n",
    "plt.xlabel(\"Kanemaru et al. reference\", fontweight = 'bold', fontsize = 13)\n",
    "plt.ylabel(\"Linscheid et al. reference\", fontweight = 'bold', fontsize = 13)\n",
    "plt.setp(ax.get_xticklabels(), rotation = 45,  ha=\"right\", rotation_mode=\"anchor\")\n",
    "plt.colorbar(im)\n",
    "ax.set_title(\"Expression correlation\", fontsize = 13)\n",
    "fig.tight_layout()\n",
    "\n",
    "# Save the final figure\n",
    "plt.savefig('Linscheid vs Kanemaru.png', dpi=600)  # Save the figure as PNG with 600 dpi"
   ]
  }
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