Celltypist_script.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "174f9d62",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scanpy as sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5e132cc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import celltypist\n",
    "from celltypist import models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6cab5c24",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata = sc.read_h5ad(\"/Users/hauke//Desktop/T2D_Myeloid/data/Corrected_Myeloid.h5ad\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ca71cec3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8509, 43086)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8b56c782",
   "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>vf_vst_counts.CV7320_mean</th>\n",
       "      <th>vf_vst_counts.CV7320_variance</th>\n",
       "      <th>vf_vst_counts.CV7320_variance.expected</th>\n",
       "      <th>vf_vst_counts.CV7320_variance.standardized</th>\n",
       "      <th>vf_vst_counts.CV7320_variable</th>\n",
       "      <th>vf_vst_counts.CV7320_rank</th>\n",
       "      <th>vf_vst_counts.one_mean</th>\n",
       "      <th>vf_vst_counts.one_variance</th>\n",
       "      <th>vf_vst_counts.one_variance.expected</th>\n",
       "      <th>vf_vst_counts.one_variance.standardized</th>\n",
       "      <th>...</th>\n",
       "      <th>vf_vst_counts.seven_variable</th>\n",
       "      <th>vf_vst_counts.seven_rank</th>\n",
       "      <th>vf_vst_counts.eight_mean</th>\n",
       "      <th>vf_vst_counts.eight_variance</th>\n",
       "      <th>vf_vst_counts.eight_variance.expected</th>\n",
       "      <th>vf_vst_counts.eight_variance.standardized</th>\n",
       "      <th>vf_vst_counts.eight_variable</th>\n",
       "      <th>vf_vst_counts.eight_rank</th>\n",
       "      <th>var.features</th>\n",
       "      <th>var.features.rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MIR1302-2HG</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>-2147483648</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>-2147483648</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>False</td>\n",
       "      <td>-2147483648</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2147483648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FAM138A</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>-2147483648</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>-2147483648</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>False</td>\n",
       "      <td>-2147483648</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2147483648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OR4F5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>-2147483648</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>-2147483648</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>False</td>\n",
       "      <td>-2147483648</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2147483648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL627309.1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>-2147483648</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>-2147483648</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>False</td>\n",
       "      <td>-2147483648</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2147483648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL627309.3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>-2147483648</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>-2147483648</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>False</td>\n",
       "      <td>-2147483648</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2147483648</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>AC011840.1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>-2147483648</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>-2147483648</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>False</td>\n",
       "      <td>-2147483648</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2147483648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AC020911.3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>-2147483648</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>-2147483648</td>\n",
       "      <td>0.003717</td>\n",
       "      <td>0.003717</td>\n",
       "      <td>0.003718</td>\n",
       "      <td>0.99989</td>\n",
       "      <td>False</td>\n",
       "      <td>-2147483648</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2147483648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U62631.1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>-2147483648</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>-2147483648</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>False</td>\n",
       "      <td>-2147483648</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2147483648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AP000553.9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>-2147483648</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>-2147483648</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>False</td>\n",
       "      <td>-2147483648</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2147483648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AC234775.4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>-2147483648</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>-2147483648</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>False</td>\n",
       "      <td>-2147483648</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2147483648</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43086 rows × 176 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             vf_vst_counts.CV7320_mean  vf_vst_counts.CV7320_variance  \\\n",
       "MIR1302-2HG                        0.0                            0.0   \n",
       "FAM138A                            0.0                            0.0   \n",
       "OR4F5                              0.0                            0.0   \n",
       "AL627309.1                         0.0                            0.0   \n",
       "AL627309.3                         0.0                            0.0   \n",
       "...                                ...                            ...   \n",
       "AC011840.1                         0.0                            0.0   \n",
       "AC020911.3                         0.0                            0.0   \n",
       "U62631.1                           0.0                            0.0   \n",
       "AP000553.9                         0.0                            0.0   \n",
       "AC234775.4                         0.0                            0.0   \n",
       "\n",
       "             vf_vst_counts.CV7320_variance.expected  \\\n",
       "MIR1302-2HG                                     0.0   \n",
       "FAM138A                                         0.0   \n",
       "OR4F5                                           0.0   \n",
       "AL627309.1                                      0.0   \n",
       "AL627309.3                                      0.0   \n",
       "...                                             ...   \n",
       "AC011840.1                                      0.0   \n",
       "AC020911.3                                      0.0   \n",
       "U62631.1                                        0.0   \n",
       "AP000553.9                                      0.0   \n",
       "AC234775.4                                      0.0   \n",
       "\n",
       "             vf_vst_counts.CV7320_variance.standardized  \\\n",
       "MIR1302-2HG                                         0.0   \n",
       "FAM138A                                             0.0   \n",
       "OR4F5                                               0.0   \n",
       "AL627309.1                                          0.0   \n",
       "AL627309.3                                          0.0   \n",
       "...                                                 ...   \n",
       "AC011840.1                                          0.0   \n",
       "AC020911.3                                          0.0   \n",
       "U62631.1                                            0.0   \n",
       "AP000553.9                                          0.0   \n",
       "AC234775.4                                          0.0   \n",
       "\n",
       "             vf_vst_counts.CV7320_variable  vf_vst_counts.CV7320_rank  \\\n",
       "MIR1302-2HG                          False                -2147483648   \n",
       "FAM138A                              False                -2147483648   \n",
       "OR4F5                                False                -2147483648   \n",
       "AL627309.1                           False                -2147483648   \n",
       "AL627309.3                           False                -2147483648   \n",
       "...                                    ...                        ...   \n",
       "AC011840.1                           False                -2147483648   \n",
       "AC020911.3                           False                -2147483648   \n",
       "U62631.1                             False                -2147483648   \n",
       "AP000553.9                           False                -2147483648   \n",
       "AC234775.4                           False                -2147483648   \n",
       "\n",
       "             vf_vst_counts.one_mean  vf_vst_counts.one_variance  \\\n",
       "MIR1302-2HG                     0.0                         0.0   \n",
       "FAM138A                         0.0                         0.0   \n",
       "OR4F5                           0.0                         0.0   \n",
       "AL627309.1                      0.0                         0.0   \n",
       "AL627309.3                      0.0                         0.0   \n",
       "...                             ...                         ...   \n",
       "AC011840.1                      0.0                         0.0   \n",
       "AC020911.3                      0.0                         0.0   \n",
       "U62631.1                        0.0                         0.0   \n",
       "AP000553.9                      0.0                         0.0   \n",
       "AC234775.4                      0.0                         0.0   \n",
       "\n",
       "             vf_vst_counts.one_variance.expected  \\\n",
       "MIR1302-2HG                                  0.0   \n",
       "FAM138A                                      0.0   \n",
       "OR4F5                                        0.0   \n",
       "AL627309.1                                   0.0   \n",
       "AL627309.3                                   0.0   \n",
       "...                                          ...   \n",
       "AC011840.1                                   0.0   \n",
       "AC020911.3                                   0.0   \n",
       "U62631.1                                     0.0   \n",
       "AP000553.9                                   0.0   \n",
       "AC234775.4                                   0.0   \n",
       "\n",
       "             vf_vst_counts.one_variance.standardized  ...  \\\n",
       "MIR1302-2HG                                      0.0  ...   \n",
       "FAM138A                                          0.0  ...   \n",
       "OR4F5                                            0.0  ...   \n",
       "AL627309.1                                       0.0  ...   \n",
       "AL627309.3                                       0.0  ...   \n",
       "...                                              ...  ...   \n",
       "AC011840.1                                       0.0  ...   \n",
       "AC020911.3                                       0.0  ...   \n",
       "U62631.1                                         0.0  ...   \n",
       "AP000553.9                                       0.0  ...   \n",
       "AC234775.4                                       0.0  ...   \n",
       "\n",
       "             vf_vst_counts.seven_variable  vf_vst_counts.seven_rank  \\\n",
       "MIR1302-2HG                         False               -2147483648   \n",
       "FAM138A                             False               -2147483648   \n",
       "OR4F5                               False               -2147483648   \n",
       "AL627309.1                          False               -2147483648   \n",
       "AL627309.3                          False               -2147483648   \n",
       "...                                   ...                       ...   \n",
       "AC011840.1                          False               -2147483648   \n",
       "AC020911.3                          False               -2147483648   \n",
       "U62631.1                            False               -2147483648   \n",
       "AP000553.9                          False               -2147483648   \n",
       "AC234775.4                          False               -2147483648   \n",
       "\n",
       "             vf_vst_counts.eight_mean  vf_vst_counts.eight_variance  \\\n",
       "MIR1302-2HG                  0.000000                      0.000000   \n",
       "FAM138A                      0.000000                      0.000000   \n",
       "OR4F5                        0.000000                      0.000000   \n",
       "AL627309.1                   0.000000                      0.000000   \n",
       "AL627309.3                   0.000000                      0.000000   \n",
       "...                               ...                           ...   \n",
       "AC011840.1                   0.000000                      0.000000   \n",
       "AC020911.3                   0.003717                      0.003717   \n",
       "U62631.1                     0.000000                      0.000000   \n",
       "AP000553.9                   0.000000                      0.000000   \n",
       "AC234775.4                   0.000000                      0.000000   \n",
       "\n",
       "             vf_vst_counts.eight_variance.expected  \\\n",
       "MIR1302-2HG                               0.000000   \n",
       "FAM138A                                   0.000000   \n",
       "OR4F5                                     0.000000   \n",
       "AL627309.1                                0.000000   \n",
       "AL627309.3                                0.000000   \n",
       "...                                            ...   \n",
       "AC011840.1                                0.000000   \n",
       "AC020911.3                                0.003718   \n",
       "U62631.1                                  0.000000   \n",
       "AP000553.9                                0.000000   \n",
       "AC234775.4                                0.000000   \n",
       "\n",
       "             vf_vst_counts.eight_variance.standardized  \\\n",
       "MIR1302-2HG                                    0.00000   \n",
       "FAM138A                                        0.00000   \n",
       "OR4F5                                          0.00000   \n",
       "AL627309.1                                     0.00000   \n",
       "AL627309.3                                     0.00000   \n",
       "...                                                ...   \n",
       "AC011840.1                                     0.00000   \n",
       "AC020911.3                                     0.99989   \n",
       "U62631.1                                       0.00000   \n",
       "AP000553.9                                     0.00000   \n",
       "AC234775.4                                     0.00000   \n",
       "\n",
       "             vf_vst_counts.eight_variable  vf_vst_counts.eight_rank  \\\n",
       "MIR1302-2HG                         False               -2147483648   \n",
       "FAM138A                             False               -2147483648   \n",
       "OR4F5                               False               -2147483648   \n",
       "AL627309.1                          False               -2147483648   \n",
       "AL627309.3                          False               -2147483648   \n",
       "...                                   ...                       ...   \n",
       "AC011840.1                          False               -2147483648   \n",
       "AC020911.3                          False               -2147483648   \n",
       "U62631.1                            False               -2147483648   \n",
       "AP000553.9                          False               -2147483648   \n",
       "AC234775.4                          False               -2147483648   \n",
       "\n",
       "             var.features  var.features.rank  \n",
       "MIR1302-2HG           NaN        -2147483648  \n",
       "FAM138A               NaN        -2147483648  \n",
       "OR4F5                 NaN        -2147483648  \n",
       "AL627309.1            NaN        -2147483648  \n",
       "AL627309.3            NaN        -2147483648  \n",
       "...                   ...                ...  \n",
       "AC011840.1            NaN        -2147483648  \n",
       "AC020911.3            NaN        -2147483648  \n",
       "U62631.1              NaN        -2147483648  \n",
       "AP000553.9            NaN        -2147483648  \n",
       "AC234775.4            NaN        -2147483648  \n",
       "\n",
       "[43086 rows x 176 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "710fc282",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[10000.],\n",
       "        [10000.],\n",
       "        [10000.],\n",
       "        ...,\n",
       "        [10000.],\n",
       "        [10000.],\n",
       "        [10000.]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.X.expm1().sum(axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "97698420-f16d-4341-ace9-3e4ac6e67d6c",
   "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>nCount_RNA</th>\n",
       "      <th>nFeature_RNA</th>\n",
       "      <th>conditions</th>\n",
       "      <th>study</th>\n",
       "      <th>bmi</th>\n",
       "      <th>Carotid_stenosis</th>\n",
       "      <th>Pat_ID</th>\n",
       "      <th>combined_ID</th>\n",
       "      <th>percent.mt</th>\n",
       "      <th>percent.rps</th>\n",
       "      <th>percent.rpl</th>\n",
       "      <th>cca_clusters</th>\n",
       "      <th>seurat_clusters</th>\n",
       "      <th>rpca_clusters</th>\n",
       "      <th>harmony_clusters</th>\n",
       "      <th>mnn_clusters</th>\n",
       "      <th>scvi_clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>G_AAAGATGGTCTGCAAT-1</th>\n",
       "      <td>14608.0</td>\n",
       "      <td>3278</td>\n",
       "      <td>ND</td>\n",
       "      <td>G</td>\n",
       "      <td>lean</td>\n",
       "      <td>Symptomatic</td>\n",
       "      <td>CV7225</td>\n",
       "      <td>G_CV7225_ND</td>\n",
       "      <td>4.100493</td>\n",
       "      <td>9.412651</td>\n",
       "      <td>12.821742</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G_AACTCTTGTCGGGTCT-1</th>\n",
       "      <td>779.0</td>\n",
       "      <td>424</td>\n",
       "      <td>ND</td>\n",
       "      <td>G</td>\n",
       "      <td>lean</td>\n",
       "      <td>Symptomatic</td>\n",
       "      <td>CV7225</td>\n",
       "      <td>G_CV7225_ND</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.621309</td>\n",
       "      <td>6.931964</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G_AACTTTCGTCCTGCTT-1</th>\n",
       "      <td>4362.0</td>\n",
       "      <td>1325</td>\n",
       "      <td>ND</td>\n",
       "      <td>G</td>\n",
       "      <td>lean</td>\n",
       "      <td>Symptomatic</td>\n",
       "      <td>CV7225</td>\n",
       "      <td>G_CV7225_ND</td>\n",
       "      <td>3.943145</td>\n",
       "      <td>13.801009</td>\n",
       "      <td>20.197157</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G_AAGCCGCCATGCCTTC-1</th>\n",
       "      <td>5877.0</td>\n",
       "      <td>1461</td>\n",
       "      <td>ND</td>\n",
       "      <td>G</td>\n",
       "      <td>lean</td>\n",
       "      <td>Symptomatic</td>\n",
       "      <td>CV7225</td>\n",
       "      <td>G_CV7225_ND</td>\n",
       "      <td>2.654416</td>\n",
       "      <td>9.954058</td>\n",
       "      <td>14.224945</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G_AAGGTTCAGCACGCCT-1</th>\n",
       "      <td>6510.0</td>\n",
       "      <td>1478</td>\n",
       "      <td>ND</td>\n",
       "      <td>G</td>\n",
       "      <td>lean</td>\n",
       "      <td>Symptomatic</td>\n",
       "      <td>CV7225</td>\n",
       "      <td>G_CV7225_ND</td>\n",
       "      <td>2.273425</td>\n",
       "      <td>7.680492</td>\n",
       "      <td>10.721966</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H_eight_TTGGCAACAGCCACCA.1</th>\n",
       "      <td>12248.0</td>\n",
       "      <td>3503</td>\n",
       "      <td>ND</td>\n",
       "      <td>H</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>eight</td>\n",
       "      <td>H_eight_ND</td>\n",
       "      <td>0.922600</td>\n",
       "      <td>12.516329</td>\n",
       "      <td>16.067929</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H_eight_TTGTAGGTCTCCAGGG.1</th>\n",
       "      <td>12608.0</td>\n",
       "      <td>3078</td>\n",
       "      <td>ND</td>\n",
       "      <td>H</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>eight</td>\n",
       "      <td>H_eight_ND</td>\n",
       "      <td>2.054251</td>\n",
       "      <td>9.835025</td>\n",
       "      <td>14.141815</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H_eight_TTTACTGAGGGTTTCT.1</th>\n",
       "      <td>8933.0</td>\n",
       "      <td>2806</td>\n",
       "      <td>ND</td>\n",
       "      <td>H</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>eight</td>\n",
       "      <td>H_eight_ND</td>\n",
       "      <td>3.156834</td>\n",
       "      <td>6.627113</td>\n",
       "      <td>9.694392</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H_eight_TTTCCTCAGCGTAGTG.1</th>\n",
       "      <td>3210.0</td>\n",
       "      <td>1508</td>\n",
       "      <td>ND</td>\n",
       "      <td>H</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>eight</td>\n",
       "      <td>H_eight_ND</td>\n",
       "      <td>6.012461</td>\n",
       "      <td>3.862928</td>\n",
       "      <td>5.295950</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H_eight_TTTGCGCTCTGGTGTA.1</th>\n",
       "      <td>5051.0</td>\n",
       "      <td>1861</td>\n",
       "      <td>ND</td>\n",
       "      <td>H</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>eight</td>\n",
       "      <td>H_eight_ND</td>\n",
       "      <td>1.979806</td>\n",
       "      <td>9.641655</td>\n",
       "      <td>14.175411</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8509 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            nCount_RNA  nFeature_RNA conditions study   bmi  \\\n",
       "G_AAAGATGGTCTGCAAT-1           14608.0          3278         ND     G  lean   \n",
       "G_AACTCTTGTCGGGTCT-1             779.0           424         ND     G  lean   \n",
       "G_AACTTTCGTCCTGCTT-1            4362.0          1325         ND     G  lean   \n",
       "G_AAGCCGCCATGCCTTC-1            5877.0          1461         ND     G  lean   \n",
       "G_AAGGTTCAGCACGCCT-1            6510.0          1478         ND     G  lean   \n",
       "...                                ...           ...        ...   ...   ...   \n",
       "H_eight_TTGGCAACAGCCACCA.1     12248.0          3503         ND     H   NaN   \n",
       "H_eight_TTGTAGGTCTCCAGGG.1     12608.0          3078         ND     H   NaN   \n",
       "H_eight_TTTACTGAGGGTTTCT.1      8933.0          2806         ND     H   NaN   \n",
       "H_eight_TTTCCTCAGCGTAGTG.1      3210.0          1508         ND     H   NaN   \n",
       "H_eight_TTTGCGCTCTGGTGTA.1      5051.0          1861         ND     H   NaN   \n",
       "\n",
       "                           Carotid_stenosis  Pat_ID  combined_ID  percent.mt  \\\n",
       "G_AAAGATGGTCTGCAAT-1            Symptomatic  CV7225  G_CV7225_ND    4.100493   \n",
       "G_AACTCTTGTCGGGTCT-1            Symptomatic  CV7225  G_CV7225_ND    0.000000   \n",
       "G_AACTTTCGTCCTGCTT-1            Symptomatic  CV7225  G_CV7225_ND    3.943145   \n",
       "G_AAGCCGCCATGCCTTC-1            Symptomatic  CV7225  G_CV7225_ND    2.654416   \n",
       "G_AAGGTTCAGCACGCCT-1            Symptomatic  CV7225  G_CV7225_ND    2.273425   \n",
       "...                                     ...     ...          ...         ...   \n",
       "H_eight_TTGGCAACAGCCACCA.1              NaN   eight   H_eight_ND    0.922600   \n",
       "H_eight_TTGTAGGTCTCCAGGG.1              NaN   eight   H_eight_ND    2.054251   \n",
       "H_eight_TTTACTGAGGGTTTCT.1              NaN   eight   H_eight_ND    3.156834   \n",
       "H_eight_TTTCCTCAGCGTAGTG.1              NaN   eight   H_eight_ND    6.012461   \n",
       "H_eight_TTTGCGCTCTGGTGTA.1              NaN   eight   H_eight_ND    1.979806   \n",
       "\n",
       "                            percent.rps  percent.rpl cca_clusters  \\\n",
       "G_AAAGATGGTCTGCAAT-1           9.412651    12.821742           11   \n",
       "G_AACTCTTGTCGGGTCT-1           4.621309     6.931964            3   \n",
       "G_AACTTTCGTCCTGCTT-1          13.801009    20.197157            1   \n",
       "G_AAGCCGCCATGCCTTC-1           9.954058    14.224945            5   \n",
       "G_AAGGTTCAGCACGCCT-1           7.680492    10.721966            9   \n",
       "...                                 ...          ...          ...   \n",
       "H_eight_TTGGCAACAGCCACCA.1    12.516329    16.067929           12   \n",
       "H_eight_TTGTAGGTCTCCAGGG.1     9.835025    14.141815            5   \n",
       "H_eight_TTTACTGAGGGTTTCT.1     6.627113     9.694392            1   \n",
       "H_eight_TTTCCTCAGCGTAGTG.1     3.862928     5.295950            1   \n",
       "H_eight_TTTGCGCTCTGGTGTA.1     9.641655    14.175411            1   \n",
       "\n",
       "                           seurat_clusters rpca_clusters harmony_clusters  \\\n",
       "G_AAAGATGGTCTGCAAT-1                     9            17                9   \n",
       "G_AACTCTTGTCGGGTCT-1                     3             3                3   \n",
       "G_AACTTTCGTCCTGCTT-1                     9            17                9   \n",
       "G_AAGCCGCCATGCCTTC-1                    18            10               18   \n",
       "G_AAGGTTCAGCACGCCT-1                     6             4                6   \n",
       "...                                    ...           ...              ...   \n",
       "H_eight_TTGGCAACAGCCACCA.1               2             5                2   \n",
       "H_eight_TTGTAGGTCTCCAGGG.1               4             2                4   \n",
       "H_eight_TTTACTGAGGGTTTCT.1               5             1                5   \n",
       "H_eight_TTTCCTCAGCGTAGTG.1              10            13               10   \n",
       "H_eight_TTTGCGCTCTGGTGTA.1               1            14                1   \n",
       "\n",
       "                           mnn_clusters scvi_clusters  \n",
       "G_AAAGATGGTCTGCAAT-1                  9             9  \n",
       "G_AACTCTTGTCGGGTCT-1                  3             3  \n",
       "G_AACTTTCGTCCTGCTT-1                  9             9  \n",
       "G_AAGCCGCCATGCCTTC-1                 18            18  \n",
       "G_AAGGTTCAGCACGCCT-1                  6             6  \n",
       "...                                 ...           ...  \n",
       "H_eight_TTGGCAACAGCCACCA.1            2             2  \n",
       "H_eight_TTGTAGGTCTCCAGGG.1            4             4  \n",
       "H_eight_TTTACTGAGGGTTTCT.1            5             5  \n",
       "H_eight_TTTCCTCAGCGTAGTG.1           10            10  \n",
       "H_eight_TTTGCGCTCTGGTGTA.1            1             1  \n",
       "\n",
       "[8509 rows x 17 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "59830c73",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "📜 Retrieving model list from server https://celltypist.cog.sanger.ac.uk/models/models.json\n",
      "📚 Total models in list: 48\n",
      "📂 Storing models in /Users/hauke/.celltypist/data/models\n",
      "💾 Downloading model [1/48]: Immune_All_Low.pkl\n",
      "💾 Downloading model [2/48]: Immune_All_High.pkl\n",
      "💾 Downloading model [3/48]: Adult_CynomolgusMacaque_Hippocampus.pkl\n",
      "💾 Downloading model [4/48]: Adult_Human_PancreaticIslet.pkl\n",
      "💾 Downloading model [5/48]: Adult_Human_Skin.pkl\n",
      "💾 Downloading model [6/48]: Adult_Mouse_Gut.pkl\n",
      "💾 Downloading model [7/48]: Adult_Mouse_OlfactoryBulb.pkl\n",
      "💾 Downloading model [8/48]: Adult_Pig_Hippocampus.pkl\n",
      "💾 Downloading model [9/48]: Adult_RhesusMacaque_Hippocampus.pkl\n",
      "💾 Downloading model [10/48]: Autopsy_COVID19_Lung.pkl\n",
      "💾 Downloading model [11/48]: COVID19_HumanChallenge_Blood.pkl\n",
      "💾 Downloading model [12/48]: COVID19_Immune_Landscape.pkl\n",
      "💾 Downloading model [13/48]: Cells_Adult_Breast.pkl\n",
      "💾 Downloading model [14/48]: Cells_Fetal_Lung.pkl\n",
      "💾 Downloading model [15/48]: Cells_Human_Tonsil.pkl\n",
      "💾 Downloading model [16/48]: Cells_Intestinal_Tract.pkl\n",
      "💾 Downloading model [17/48]: Cells_Lung_Airway.pkl\n",
      "💾 Downloading model [18/48]: Developing_Human_Brain.pkl\n",
      "💾 Downloading model [19/48]: Developing_Human_Gonads.pkl\n",
      "💾 Downloading model [20/48]: Developing_Human_Hippocampus.pkl\n",
      "💾 Downloading model [21/48]: Developing_Human_Organs.pkl\n",
      "💾 Downloading model [22/48]: Developing_Human_Thymus.pkl\n",
      "💾 Downloading model [23/48]: Developing_Mouse_Brain.pkl\n",
      "💾 Downloading model [24/48]: Developing_Mouse_Hippocampus.pkl\n",
      "💾 Downloading model [25/48]: Fetal_Human_AdrenalGlands.pkl\n",
      "💾 Downloading model [26/48]: Fetal_Human_Pancreas.pkl\n",
      "💾 Downloading model [27/48]: Fetal_Human_Pituitary.pkl\n",
      "💾 Downloading model [28/48]: Fetal_Human_Retina.pkl\n",
      "💾 Downloading model [29/48]: Fetal_Human_Skin.pkl\n",
      "💾 Downloading model [30/48]: Healthy_Adult_Heart.pkl\n",
      "💾 Downloading model [31/48]: Healthy_COVID19_PBMC.pkl\n",
      "💾 Downloading model [32/48]: Healthy_Human_Liver.pkl\n",
      "💾 Downloading model [33/48]: Healthy_Mouse_Liver.pkl\n",
      "💾 Downloading model [34/48]: Human_AdultAged_Hippocampus.pkl\n",
      "💾 Downloading model [35/48]: Human_Colorectal_Cancer.pkl\n",
      "💾 Downloading model [36/48]: Human_Developmental_Retina.pkl\n",
      "💾 Downloading model [37/48]: Human_Embryonic_YolkSac.pkl\n",
      "💾 Downloading model [38/48]: Human_IPF_Lung.pkl\n",
      "💾 Downloading model [39/48]: Human_Longitudinal_Hippocampus.pkl\n",
      "💾 Downloading model [40/48]: Human_Lung_Atlas.pkl\n",
      "💾 Downloading model [41/48]: Human_PF_Lung.pkl\n",
      "💾 Downloading model [42/48]: Lethal_COVID19_Lung.pkl\n",
      "💾 Downloading model [43/48]: Mouse_Dentate_Gyrus.pkl\n",
      "💾 Downloading model [44/48]: Mouse_Isocortex_Hippocampus.pkl\n",
      "💾 Downloading model [45/48]: Mouse_Postnatal_DentateGyrus.pkl\n",
      "💾 Downloading model [46/48]: Mouse_Whole_Brain.pkl\n",
      "💾 Downloading model [47/48]: Nuclei_Lung_Airway.pkl\n",
      "💾 Downloading model [48/48]: Pan_Fetal_Human.pkl\n"
     ]
    }
   ],
   "source": [
    "# Enabling `force_update = True` will overwrite existing (old) models.\n",
    "models.download_models(force_update = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e66ff4e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Indeed, the `model` argument defaults to `Immune_All_Low.pkl`.\n",
    "model = models.Model.load(model = 'Immune_All_Low.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c4573063",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CellTypist model with 98 cell types and 6639 features\n",
       "    date: 2022-07-16 00:20:42.927778\n",
       "    details: immune sub-populations combined from 20 tissues of 18 studies\n",
       "    source: https://doi.org/10.1126/science.abl5197\n",
       "    version: v2\n",
       "    cell types: Age-associated B cells, Alveolar macrophages, ..., pDC precursor\n",
       "    features: A1BG, A2M, ..., ZYX"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8376abb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Age-associated B cells', 'Alveolar macrophages', 'B cells',\n",
       "       'CD16+ NK cells', 'CD16- NK cells', 'CD8a/a', 'CD8a/b(entry)',\n",
       "       'CMP', 'CRTAM+ gamma-delta T cells', 'Classical monocytes',\n",
       "       'Cycling B cells', 'Cycling DCs', 'Cycling NK cells',\n",
       "       'Cycling T cells', 'Cycling gamma-delta T cells',\n",
       "       'Cycling monocytes', 'DC', 'DC precursor', 'DC1', 'DC2', 'DC3',\n",
       "       'Double-negative thymocytes', 'Double-positive thymocytes', 'ELP',\n",
       "       'ETP', 'Early MK', 'Early erythroid', 'Early lymphoid/T lymphoid',\n",
       "       'Endothelial cells', 'Epithelial cells', 'Erythrocytes',\n",
       "       'Erythrophagocytic macrophages', 'Fibroblasts',\n",
       "       'Follicular B cells', 'Follicular helper T cells', 'GMP',\n",
       "       'Germinal center B cells', 'Granulocytes', 'HSC/MPP',\n",
       "       'Hofbauer cells', 'ILC', 'ILC precursor', 'ILC1', 'ILC2', 'ILC3',\n",
       "       'Intermediate macrophages', 'Intestinal macrophages',\n",
       "       'Kidney-resident macrophages', 'Kupffer cells',\n",
       "       'Large pre-B cells', 'Late erythroid', 'MAIT cells', 'MEMP', 'MNP',\n",
       "       'Macrophages', 'Mast cells', 'Megakaryocyte precursor',\n",
       "       'Megakaryocyte-erythroid-mast cell progenitor',\n",
       "       'Megakaryocytes/platelets', 'Memory B cells',\n",
       "       'Memory CD4+ cytotoxic T cells', 'Mid erythroid', 'Migratory DCs',\n",
       "       'Mono-mac', 'Monocyte precursor', 'Monocytes', 'Myelocytes',\n",
       "       'NK cells', 'NKT cells', 'Naive B cells',\n",
       "       'Neutrophil-myeloid progenitor', 'Neutrophils',\n",
       "       'Non-classical monocytes', 'Plasma cells', 'Plasmablasts',\n",
       "       'Pre-pro-B cells', 'Pro-B cells',\n",
       "       'Proliferative germinal center B cells', 'Promyelocytes',\n",
       "       'Regulatory T cells', 'Small pre-B cells', 'T(agonist)',\n",
       "       'Tcm/Naive cytotoxic T cells', 'Tcm/Naive helper T cells',\n",
       "       'Tem/Effector helper T cells', 'Tem/Effector helper T cells PD1+',\n",
       "       'Tem/Temra cytotoxic T cells', 'Tem/Trm cytotoxic T cells',\n",
       "       'Transitional B cells', 'Transitional DC', 'Transitional NK',\n",
       "       'Treg(diff)', 'Trm cytotoxic T cells', 'Type 1 helper T cells',\n",
       "       'Type 17 helper T cells', 'gamma-delta T cells', 'pDC',\n",
       "       'pDC precursor'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.cell_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7ff4bb6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "🔬 Input data has 8509 cells and 43086 genes\n",
      "🔗 Matching reference genes in the model\n",
      "🧬 6404 features used for prediction\n",
      "⚖️ Scaling input data\n",
      "🖋️ Predicting labels\n",
      "✅ Prediction done!\n",
      "👀 Can not detect a neighborhood graph, will construct one before the over-clustering\n",
      "⛓️ Over-clustering input data with resolution set to 10\n",
      "🗳️ Majority voting the predictions\n",
      "✅ Majority voting done!\n"
     ]
    }
   ],
   "source": [
    "# Not run; predict cell identities using this loaded model.\n",
    "#predictions = celltypist.annotate(adata_2000, model = model, majority_voting = True)\n",
    "# Alternatively, just specify the model name (recommended as this ensures the model is intact every time it is loaded).\n",
    "predictions = celltypist.annotate(adata, model = model, majority_voting = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0c78413a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get an `AnnData` with predicted labels embedded into the cell metadata columns.\n",
    "adata = predictions.to_adata()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "732495a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 816x555 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "celltypist.dotplot(predictions, use_as_reference = 'harmony_clusters', use_as_prediction = 'majority_voting')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "25b6ffff-3afc-4b8e-8a5f-811499dc1f98",
   "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>nCount_RNA</th>\n",
       "      <th>nFeature_RNA</th>\n",
       "      <th>conditions</th>\n",
       "      <th>study</th>\n",
       "      <th>bmi</th>\n",
       "      <th>Carotid_stenosis</th>\n",
       "      <th>Pat_ID</th>\n",
       "      <th>combined_ID</th>\n",
       "      <th>percent.mt</th>\n",
       "      <th>percent.rps</th>\n",
       "      <th>...</th>\n",
       "      <th>cca_clusters</th>\n",
       "      <th>seurat_clusters</th>\n",
       "      <th>rpca_clusters</th>\n",
       "      <th>harmony_clusters</th>\n",
       "      <th>mnn_clusters</th>\n",
       "      <th>scvi_clusters</th>\n",
       "      <th>predicted_labels</th>\n",
       "      <th>over_clustering</th>\n",
       "      <th>majority_voting</th>\n",
       "      <th>conf_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>G_AAAGATGGTCTGCAAT-1</th>\n",
       "      <td>14608.0</td>\n",
       "      <td>3278</td>\n",
       "      <td>ND</td>\n",
       "      <td>G</td>\n",
       "      <td>lean</td>\n",
       "      <td>Symptomatic</td>\n",
       "      <td>CV7225</td>\n",
       "      <td>G_CV7225_ND</td>\n",
       "      <td>4.100493</td>\n",
       "      <td>9.412651</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>DC1</td>\n",
       "      <td>74</td>\n",
       "      <td>Macrophages</td>\n",
       "      <td>0.055636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G_AACTCTTGTCGGGTCT-1</th>\n",
       "      <td>779.0</td>\n",
       "      <td>424</td>\n",
       "      <td>ND</td>\n",
       "      <td>G</td>\n",
       "      <td>lean</td>\n",
       "      <td>Symptomatic</td>\n",
       "      <td>CV7225</td>\n",
       "      <td>G_CV7225_ND</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.621309</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Alveolar macrophages</td>\n",
       "      <td>58</td>\n",
       "      <td>Macrophages</td>\n",
       "      <td>0.453090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G_AACTTTCGTCCTGCTT-1</th>\n",
       "      <td>4362.0</td>\n",
       "      <td>1325</td>\n",
       "      <td>ND</td>\n",
       "      <td>G</td>\n",
       "      <td>lean</td>\n",
       "      <td>Symptomatic</td>\n",
       "      <td>CV7225</td>\n",
       "      <td>G_CV7225_ND</td>\n",
       "      <td>3.943145</td>\n",
       "      <td>13.801009</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>Classical monocytes</td>\n",
       "      <td>16</td>\n",
       "      <td>Classical monocytes</td>\n",
       "      <td>0.018434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G_AAGCCGCCATGCCTTC-1</th>\n",
       "      <td>5877.0</td>\n",
       "      <td>1461</td>\n",
       "      <td>ND</td>\n",
       "      <td>G</td>\n",
       "      <td>lean</td>\n",
       "      <td>Symptomatic</td>\n",
       "      <td>CV7225</td>\n",
       "      <td>G_CV7225_ND</td>\n",
       "      <td>2.654416</td>\n",
       "      <td>9.954058</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>Migratory DCs</td>\n",
       "      <td>76</td>\n",
       "      <td>Migratory DCs</td>\n",
       "      <td>0.993403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G_AAGGTTCAGCACGCCT-1</th>\n",
       "      <td>6510.0</td>\n",
       "      <td>1478</td>\n",
       "      <td>ND</td>\n",
       "      <td>G</td>\n",
       "      <td>lean</td>\n",
       "      <td>Symptomatic</td>\n",
       "      <td>CV7225</td>\n",
       "      <td>G_CV7225_ND</td>\n",
       "      <td>2.273425</td>\n",
       "      <td>7.680492</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>Macrophages</td>\n",
       "      <td>28</td>\n",
       "      <td>Intermediate macrophages</td>\n",
       "      <td>0.823976</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>H_eight_TTGGCAACAGCCACCA.1</th>\n",
       "      <td>12248.0</td>\n",
       "      <td>3503</td>\n",
       "      <td>ND</td>\n",
       "      <td>H</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>eight</td>\n",
       "      <td>H_eight_ND</td>\n",
       "      <td>0.922600</td>\n",
       "      <td>12.516329</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>pDC</td>\n",
       "      <td>68</td>\n",
       "      <td>pDC</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H_eight_TTGTAGGTCTCCAGGG.1</th>\n",
       "      <td>12608.0</td>\n",
       "      <td>3078</td>\n",
       "      <td>ND</td>\n",
       "      <td>H</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>eight</td>\n",
       "      <td>H_eight_ND</td>\n",
       "      <td>2.054251</td>\n",
       "      <td>9.835025</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>DC2</td>\n",
       "      <td>59</td>\n",
       "      <td>DC2</td>\n",
       "      <td>0.999993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H_eight_TTTACTGAGGGTTTCT.1</th>\n",
       "      <td>8933.0</td>\n",
       "      <td>2806</td>\n",
       "      <td>ND</td>\n",
       "      <td>H</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>eight</td>\n",
       "      <td>H_eight_ND</td>\n",
       "      <td>3.156834</td>\n",
       "      <td>6.627113</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>Intermediate macrophages</td>\n",
       "      <td>62</td>\n",
       "      <td>Intermediate macrophages</td>\n",
       "      <td>0.116293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H_eight_TTTCCTCAGCGTAGTG.1</th>\n",
       "      <td>3210.0</td>\n",
       "      <td>1508</td>\n",
       "      <td>ND</td>\n",
       "      <td>H</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>eight</td>\n",
       "      <td>H_eight_ND</td>\n",
       "      <td>6.012461</td>\n",
       "      <td>3.862928</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Classical monocytes</td>\n",
       "      <td>15</td>\n",
       "      <td>Classical monocytes</td>\n",
       "      <td>0.983729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H_eight_TTTGCGCTCTGGTGTA.1</th>\n",
       "      <td>5051.0</td>\n",
       "      <td>1861</td>\n",
       "      <td>ND</td>\n",
       "      <td>H</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>eight</td>\n",
       "      <td>H_eight_ND</td>\n",
       "      <td>1.979806</td>\n",
       "      <td>9.641655</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Classical monocytes</td>\n",
       "      <td>83</td>\n",
       "      <td>Classical monocytes</td>\n",
       "      <td>0.997093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8509 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            nCount_RNA  nFeature_RNA conditions study   bmi  \\\n",
       "G_AAAGATGGTCTGCAAT-1           14608.0          3278         ND     G  lean   \n",
       "G_AACTCTTGTCGGGTCT-1             779.0           424         ND     G  lean   \n",
       "G_AACTTTCGTCCTGCTT-1            4362.0          1325         ND     G  lean   \n",
       "G_AAGCCGCCATGCCTTC-1            5877.0          1461         ND     G  lean   \n",
       "G_AAGGTTCAGCACGCCT-1            6510.0          1478         ND     G  lean   \n",
       "...                                ...           ...        ...   ...   ...   \n",
       "H_eight_TTGGCAACAGCCACCA.1     12248.0          3503         ND     H   NaN   \n",
       "H_eight_TTGTAGGTCTCCAGGG.1     12608.0          3078         ND     H   NaN   \n",
       "H_eight_TTTACTGAGGGTTTCT.1      8933.0          2806         ND     H   NaN   \n",
       "H_eight_TTTCCTCAGCGTAGTG.1      3210.0          1508         ND     H   NaN   \n",
       "H_eight_TTTGCGCTCTGGTGTA.1      5051.0          1861         ND     H   NaN   \n",
       "\n",
       "                           Carotid_stenosis  Pat_ID  combined_ID  percent.mt  \\\n",
       "G_AAAGATGGTCTGCAAT-1            Symptomatic  CV7225  G_CV7225_ND    4.100493   \n",
       "G_AACTCTTGTCGGGTCT-1            Symptomatic  CV7225  G_CV7225_ND    0.000000   \n",
       "G_AACTTTCGTCCTGCTT-1            Symptomatic  CV7225  G_CV7225_ND    3.943145   \n",
       "G_AAGCCGCCATGCCTTC-1            Symptomatic  CV7225  G_CV7225_ND    2.654416   \n",
       "G_AAGGTTCAGCACGCCT-1            Symptomatic  CV7225  G_CV7225_ND    2.273425   \n",
       "...                                     ...     ...          ...         ...   \n",
       "H_eight_TTGGCAACAGCCACCA.1              NaN   eight   H_eight_ND    0.922600   \n",
       "H_eight_TTGTAGGTCTCCAGGG.1              NaN   eight   H_eight_ND    2.054251   \n",
       "H_eight_TTTACTGAGGGTTTCT.1              NaN   eight   H_eight_ND    3.156834   \n",
       "H_eight_TTTCCTCAGCGTAGTG.1              NaN   eight   H_eight_ND    6.012461   \n",
       "H_eight_TTTGCGCTCTGGTGTA.1              NaN   eight   H_eight_ND    1.979806   \n",
       "\n",
       "                            percent.rps  ...  cca_clusters seurat_clusters  \\\n",
       "G_AAAGATGGTCTGCAAT-1           9.412651  ...            11               9   \n",
       "G_AACTCTTGTCGGGTCT-1           4.621309  ...             3               3   \n",
       "G_AACTTTCGTCCTGCTT-1          13.801009  ...             1               9   \n",
       "G_AAGCCGCCATGCCTTC-1           9.954058  ...             5              18   \n",
       "G_AAGGTTCAGCACGCCT-1           7.680492  ...             9               6   \n",
       "...                                 ...  ...           ...             ...   \n",
       "H_eight_TTGGCAACAGCCACCA.1    12.516329  ...            12               2   \n",
       "H_eight_TTGTAGGTCTCCAGGG.1     9.835025  ...             5               4   \n",
       "H_eight_TTTACTGAGGGTTTCT.1     6.627113  ...             1               5   \n",
       "H_eight_TTTCCTCAGCGTAGTG.1     3.862928  ...             1              10   \n",
       "H_eight_TTTGCGCTCTGGTGTA.1     9.641655  ...             1               1   \n",
       "\n",
       "                           rpca_clusters harmony_clusters mnn_clusters  \\\n",
       "G_AAAGATGGTCTGCAAT-1                  17                9            9   \n",
       "G_AACTCTTGTCGGGTCT-1                   3                3            3   \n",
       "G_AACTTTCGTCCTGCTT-1                  17                9            9   \n",
       "G_AAGCCGCCATGCCTTC-1                  10               18           18   \n",
       "G_AAGGTTCAGCACGCCT-1                   4                6            6   \n",
       "...                                  ...              ...          ...   \n",
       "H_eight_TTGGCAACAGCCACCA.1             5                2            2   \n",
       "H_eight_TTGTAGGTCTCCAGGG.1             2                4            4   \n",
       "H_eight_TTTACTGAGGGTTTCT.1             1                5            5   \n",
       "H_eight_TTTCCTCAGCGTAGTG.1            13               10           10   \n",
       "H_eight_TTTGCGCTCTGGTGTA.1            14                1            1   \n",
       "\n",
       "                           scvi_clusters          predicted_labels  \\\n",
       "G_AAAGATGGTCTGCAAT-1                   9                       DC1   \n",
       "G_AACTCTTGTCGGGTCT-1                   3      Alveolar macrophages   \n",
       "G_AACTTTCGTCCTGCTT-1                   9       Classical monocytes   \n",
       "G_AAGCCGCCATGCCTTC-1                  18             Migratory DCs   \n",
       "G_AAGGTTCAGCACGCCT-1                   6               Macrophages   \n",
       "...                                  ...                       ...   \n",
       "H_eight_TTGGCAACAGCCACCA.1             2                       pDC   \n",
       "H_eight_TTGTAGGTCTCCAGGG.1             4                       DC2   \n",
       "H_eight_TTTACTGAGGGTTTCT.1             5  Intermediate macrophages   \n",
       "H_eight_TTTCCTCAGCGTAGTG.1            10       Classical monocytes   \n",
       "H_eight_TTTGCGCTCTGGTGTA.1             1       Classical monocytes   \n",
       "\n",
       "                           over_clustering           majority_voting  \\\n",
       "G_AAAGATGGTCTGCAAT-1                    74               Macrophages   \n",
       "G_AACTCTTGTCGGGTCT-1                    58               Macrophages   \n",
       "G_AACTTTCGTCCTGCTT-1                    16       Classical monocytes   \n",
       "G_AAGCCGCCATGCCTTC-1                    76             Migratory DCs   \n",
       "G_AAGGTTCAGCACGCCT-1                    28  Intermediate macrophages   \n",
       "...                                    ...                       ...   \n",
       "H_eight_TTGGCAACAGCCACCA.1              68                       pDC   \n",
       "H_eight_TTGTAGGTCTCCAGGG.1              59                       DC2   \n",
       "H_eight_TTTACTGAGGGTTTCT.1              62  Intermediate macrophages   \n",
       "H_eight_TTTCCTCAGCGTAGTG.1              15       Classical monocytes   \n",
       "H_eight_TTTGCGCTCTGGTGTA.1              83       Classical monocytes   \n",
       "\n",
       "                           conf_score  \n",
       "G_AAAGATGGTCTGCAAT-1         0.055636  \n",
       "G_AACTCTTGTCGGGTCT-1         0.453090  \n",
       "G_AACTTTCGTCCTGCTT-1         0.018434  \n",
       "G_AAGCCGCCATGCCTTC-1         0.993403  \n",
       "G_AAGGTTCAGCACGCCT-1         0.823976  \n",
       "...                               ...  \n",
       "H_eight_TTGGCAACAGCCACCA.1   1.000000  \n",
       "H_eight_TTGTAGGTCTCCAGGG.1   0.999993  \n",
       "H_eight_TTTACTGAGGGTTTCT.1   0.116293  \n",
       "H_eight_TTTCCTCAGCGTAGTG.1   0.983729  \n",
       "H_eight_TTTGCGCTCTGGTGTA.1   0.997093  \n",
       "\n",
       "[8509 rows x 21 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ae6e1b73-ffff-4044-8092-7e22f6fb5250",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.write_h5ad(\"/Users/hauke//Desktop/T2D_Myeloid/data/CellTypist_Myeloid.h5ad\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05f90c15-5c49-4563-9575-a0322061d755",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}