scvi_label_transfer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b36e35e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#easy to use recommended datasets, but you can use any reference you want that is labeled\n",
    "#tabula muris\n",
    "#tabula muris senis\n",
    "#tabula sapiens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "86b962d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100 3868M  100 3868M    0     0  14.5M      0  0:04:25  0:04:25 --:--:-- 15.7M\n"
     ]
    }
   ],
   "source": [
    "!curl -O \"https://czb-tabula-muris-senis.s3.us-west-2.amazonaws.com/Data-objects/tabula-muris-senis-droplet-official-raw-obj.h5ad\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8295e05b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#example human tabula sapiens dataset, more on figshare\n",
    "#!wget https://figshare.com/ndownloader/files/34701982\n",
    "#!unzip 34701982"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9de7101b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Global seed set to 0\n"
     ]
    }
   ],
   "source": [
    "import scanpy as sc\n",
    "import scvi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b7094526",
   "metadata": {},
   "outputs": [],
   "source": [
    "ref_data = sc.read_h5ad('tabula-muris-senis-droplet-official-raw-obj.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ecbb33d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Tongue',\n",
       " 'Heart_and_Aorta',\n",
       " 'Marrow',\n",
       " 'Mammary_Gland',\n",
       " 'Fat',\n",
       " 'Kidney',\n",
       " 'Liver',\n",
       " 'Lung',\n",
       " 'Limb_Muscle',\n",
       " 'Pancreas',\n",
       " 'Spleen',\n",
       " 'Thymus',\n",
       " 'Bladder',\n",
       " 'Skin',\n",
       " 'Large_Intestine',\n",
       " 'Trachea']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref_data.obs.tissue.unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1cce5d3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>cell</th>\n",
       "      <th>cell_ontology_class</th>\n",
       "      <th>cell_ontology_id</th>\n",
       "      <th>free_annotation</th>\n",
       "      <th>method</th>\n",
       "      <th>mouse.id</th>\n",
       "      <th>n_genes</th>\n",
       "      <th>sex</th>\n",
       "      <th>subtissue</th>\n",
       "      <th>tissue</th>\n",
       "      <th>tissue_free_annotation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</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>AAACGGGTCGCCCTTA-1-11-0-0</th>\n",
       "      <td>18m</td>\n",
       "      <td>MACA_18m_F_LUNG_50_AAACGGGTCGCCCTTA</td>\n",
       "      <td>B cell</td>\n",
       "      <td>NA</td>\n",
       "      <td>B</td>\n",
       "      <td>droplet</td>\n",
       "      <td>18-F-50</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>female</td>\n",
       "      <td>nan</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAGATGAGCAGACTG-1-11-0-0</th>\n",
       "      <td>18m</td>\n",
       "      <td>MACA_18m_F_LUNG_50_AAAGATGAGCAGACTG</td>\n",
       "      <td>non-classical monocyte</td>\n",
       "      <td>NA</td>\n",
       "      <td>Nonclassical Monocyte</td>\n",
       "      <td>droplet</td>\n",
       "      <td>18-F-50</td>\n",
       "      <td>1333.0</td>\n",
       "      <td>female</td>\n",
       "      <td>nan</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAGATGAGCCGTCGT-1-11-0-0</th>\n",
       "      <td>18m</td>\n",
       "      <td>MACA_18m_F_LUNG_50_AAAGATGAGCCGTCGT</td>\n",
       "      <td>NK cell</td>\n",
       "      <td>NA</td>\n",
       "      <td>Natural Killer</td>\n",
       "      <td>droplet</td>\n",
       "      <td>18-F-50</td>\n",
       "      <td>1455.0</td>\n",
       "      <td>female</td>\n",
       "      <td>nan</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAGCAACATGGTAGG-1-11-0-0</th>\n",
       "      <td>18m</td>\n",
       "      <td>MACA_18m_F_LUNG_50_AAAGCAACATGGTAGG</td>\n",
       "      <td>alveolar macrophage</td>\n",
       "      <td>NA</td>\n",
       "      <td>Alveolar Macrophage</td>\n",
       "      <td>droplet</td>\n",
       "      <td>18-F-50</td>\n",
       "      <td>3322.0</td>\n",
       "      <td>female</td>\n",
       "      <td>nan</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAGCAAGTCTAGTGT-1-11-0-0</th>\n",
       "      <td>18m</td>\n",
       "      <td>MACA_18m_F_LUNG_50_AAAGCAAGTCTAGTGT</td>\n",
       "      <td>B cell</td>\n",
       "      <td>NA</td>\n",
       "      <td>B</td>\n",
       "      <td>droplet</td>\n",
       "      <td>18-F-50</td>\n",
       "      <td>1120.0</td>\n",
       "      <td>female</td>\n",
       "      <td>nan</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGGTTAGGGATACC-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGGTTAGGGATACC</td>\n",
       "      <td>fibroblast of lung</td>\n",
       "      <td>CL:0000499</td>\n",
       "      <td>Alveolar Fibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGGTTTCCTGTACC-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGGTTTCCTGTACC</td>\n",
       "      <td>NK cell</td>\n",
       "      <td>CL:0000623</td>\n",
       "      <td>Natural Killer</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGTCAAGAGTCGGT-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGTCAAGAGTCGGT</td>\n",
       "      <td>pulmonary interstitial fibroblast</td>\n",
       "      <td>CL:0000499</td>\n",
       "      <td>Myofibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGTCACATATGAGA-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGTCACATATGAGA</td>\n",
       "      <td>adventitial cell</td>\n",
       "      <td>CL:0000499</td>\n",
       "      <td>Adventitial Fibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGTCAGTGGTCCGT-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGTCAGTGGTCCGT</td>\n",
       "      <td>fibroblast of lung</td>\n",
       "      <td>CL:0000499</td>\n",
       "      <td>Alveolar Fibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24540 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              age                                 cell  \\\n",
       "index                                                                    \n",
       "AAACGGGTCGCCCTTA-1-11-0-0     18m  MACA_18m_F_LUNG_50_AAACGGGTCGCCCTTA   \n",
       "AAAGATGAGCAGACTG-1-11-0-0     18m  MACA_18m_F_LUNG_50_AAAGATGAGCAGACTG   \n",
       "AAAGATGAGCCGTCGT-1-11-0-0     18m  MACA_18m_F_LUNG_50_AAAGATGAGCCGTCGT   \n",
       "AAAGCAACATGGTAGG-1-11-0-0     18m  MACA_18m_F_LUNG_50_AAAGCAACATGGTAGG   \n",
       "AAAGCAAGTCTAGTGT-1-11-0-0     18m  MACA_18m_F_LUNG_50_AAAGCAAGTCTAGTGT   \n",
       "...                           ...                                  ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1   3m           10X_P8_13_TTTGGTTAGGGATACC   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1   3m           10X_P8_13_TTTGGTTTCCTGTACC   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1   3m           10X_P8_13_TTTGTCAAGAGTCGGT   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1   3m           10X_P8_13_TTTGTCACATATGAGA   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1   3m           10X_P8_13_TTTGTCAGTGGTCCGT   \n",
       "\n",
       "                                            cell_ontology_class  \\\n",
       "index                                                             \n",
       "AAACGGGTCGCCCTTA-1-11-0-0                                B cell   \n",
       "AAAGATGAGCAGACTG-1-11-0-0                non-classical monocyte   \n",
       "AAAGATGAGCCGTCGT-1-11-0-0                               NK cell   \n",
       "AAAGCAACATGGTAGG-1-11-0-0                   alveolar macrophage   \n",
       "AAAGCAAGTCTAGTGT-1-11-0-0                                B cell   \n",
       "...                                                         ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1                 fibroblast of lung   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1                            NK cell   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1  pulmonary interstitial fibroblast   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1                   adventitial cell   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1                 fibroblast of lung   \n",
       "\n",
       "                             cell_ontology_id         free_annotation  \\\n",
       "index                                                                   \n",
       "AAACGGGTCGCCCTTA-1-11-0-0                  NA                       B   \n",
       "AAAGATGAGCAGACTG-1-11-0-0                  NA   Nonclassical Monocyte   \n",
       "AAAGATGAGCCGTCGT-1-11-0-0                  NA          Natural Killer   \n",
       "AAAGCAACATGGTAGG-1-11-0-0                  NA     Alveolar Macrophage   \n",
       "AAAGCAAGTCTAGTGT-1-11-0-0                  NA                       B   \n",
       "...                                       ...                     ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1       CL:0000499     Alveolar Fibroblast   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1       CL:0000623          Natural Killer   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1       CL:0000499           Myofibroblast   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1       CL:0000499  Adventitial Fibroblast   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1       CL:0000499     Alveolar Fibroblast   \n",
       "\n",
       "                               method mouse.id  n_genes     sex  \\\n",
       "index                                                             \n",
       "AAACGGGTCGCCCTTA-1-11-0-0     droplet  18-F-50   1821.0  female   \n",
       "AAAGATGAGCAGACTG-1-11-0-0     droplet  18-F-50   1333.0  female   \n",
       "AAAGATGAGCCGTCGT-1-11-0-0     droplet  18-F-50   1455.0  female   \n",
       "AAAGCAACATGGTAGG-1-11-0-0     droplet  18-F-50   3322.0  female   \n",
       "AAAGCAAGTCTAGTGT-1-11-0-0     droplet  18-F-50   1120.0  female   \n",
       "...                               ...      ...      ...     ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1  droplet  3-M-7/8      NaN    male   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1  droplet  3-M-7/8      NaN    male   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1  droplet  3-M-7/8      NaN    male   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1  droplet  3-M-7/8      NaN    male   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1  droplet  3-M-7/8      NaN    male   \n",
       "\n",
       "                                         subtissue tissue  \\\n",
       "index                                                       \n",
       "AAACGGGTCGCCCTTA-1-11-0-0                      nan   Lung   \n",
       "AAAGATGAGCAGACTG-1-11-0-0                      nan   Lung   \n",
       "AAAGATGAGCCGTCGT-1-11-0-0                      nan   Lung   \n",
       "AAAGCAACATGGTAGG-1-11-0-0                      nan   Lung   \n",
       "AAAGCAAGTCTAGTGT-1-11-0-0                      nan   Lung   \n",
       "...                                            ...    ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1  immune-endo-depleted   Lung   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1  immune-endo-depleted   Lung   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1  immune-endo-depleted   Lung   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1  immune-endo-depleted   Lung   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1  immune-endo-depleted   Lung   \n",
       "\n",
       "                             tissue_free_annotation  \n",
       "index                                                \n",
       "AAACGGGTCGCCCTTA-1-11-0-0                      Lung  \n",
       "AAAGATGAGCAGACTG-1-11-0-0                      Lung  \n",
       "AAAGATGAGCCGTCGT-1-11-0-0                      Lung  \n",
       "AAAGCAACATGGTAGG-1-11-0-0                      Lung  \n",
       "AAAGCAAGTCTAGTGT-1-11-0-0                      Lung  \n",
       "...                                             ...  \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1                   Lung  \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1                   Lung  \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1                   Lung  \n",
       "10X_P8_13_TTTGTCACATATGAGA-1                   Lung  \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1                   Lung  \n",
       "\n",
       "[24540 rows x 12 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref_data = ref_data[ref_data.obs.tissue == 'Lung']\n",
    "ref_data.obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3f4cfd32",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jrlab2019/miniconda3/envs/sc/lib/python3.9/site-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "  utils.warn_names_duplicates(\"var\")\n"
     ]
    }
   ],
   "source": [
    "adata = sc.read_10x_h5('lung1.h5')\n",
    "adata.var_names_make_unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "21d97283",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jrlab2019/miniconda3/envs/sc/lib/python3.9/site-packages/anndata/_core/anndata.py:1785: FutureWarning: X.dtype being converted to np.float32 from float64. In the next version of anndata (0.9) conversion will not be automatic. Pass dtype explicitly to avoid this warning. Pass `AnnData(X, dtype=X.dtype, ...)` to get the future behavour.\n",
      "  [AnnData(sparse.csr_matrix(a.shape), obs=a.obs) for a in all_adatas],\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>cell</th>\n",
       "      <th>cell_ontology_class</th>\n",
       "      <th>cell_ontology_id</th>\n",
       "      <th>free_annotation</th>\n",
       "      <th>method</th>\n",
       "      <th>mouse.id</th>\n",
       "      <th>n_genes</th>\n",
       "      <th>sex</th>\n",
       "      <th>subtissue</th>\n",
       "      <th>tissue</th>\n",
       "      <th>tissue_free_annotation</th>\n",
       "      <th>batch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAACCCAAGCAACAAT-1-0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCCAAGGAGGGTG-1-0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCCAAGGTCCCTG-1-0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCCACAACGCATT-1-0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCCACACCAGCGT-1-0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGGTTAGGGATACC-1-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGGTTAGGGATACC</td>\n",
       "      <td>fibroblast of lung</td>\n",
       "      <td>CL:0000499</td>\n",
       "      <td>Alveolar Fibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGGTTTCCTGTACC-1-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGGTTTCCTGTACC</td>\n",
       "      <td>NK cell</td>\n",
       "      <td>CL:0000623</td>\n",
       "      <td>Natural Killer</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGTCAAGAGTCGGT-1-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGTCAAGAGTCGGT</td>\n",
       "      <td>pulmonary interstitial fibroblast</td>\n",
       "      <td>CL:0000499</td>\n",
       "      <td>Myofibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGTCACATATGAGA-1-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGTCACATATGAGA</td>\n",
       "      <td>adventitial cell</td>\n",
       "      <td>CL:0000499</td>\n",
       "      <td>Adventitial Fibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGTCAGTGGTCCGT-1-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGTCAGTGGTCCGT</td>\n",
       "      <td>fibroblast of lung</td>\n",
       "      <td>CL:0000499</td>\n",
       "      <td>Alveolar Fibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36864 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                age                        cell  \\\n",
       "AAACCCAAGCAACAAT-1-0            NaN                         NaN   \n",
       "AAACCCAAGGAGGGTG-1-0            NaN                         NaN   \n",
       "AAACCCAAGGTCCCTG-1-0            NaN                         NaN   \n",
       "AAACCCACAACGCATT-1-0            NaN                         NaN   \n",
       "AAACCCACACCAGCGT-1-0            NaN                         NaN   \n",
       "...                             ...                         ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1-1   3m  10X_P8_13_TTTGGTTAGGGATACC   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1-1   3m  10X_P8_13_TTTGGTTTCCTGTACC   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1-1   3m  10X_P8_13_TTTGTCAAGAGTCGGT   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1-1   3m  10X_P8_13_TTTGTCACATATGAGA   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1-1   3m  10X_P8_13_TTTGTCAGTGGTCCGT   \n",
       "\n",
       "                                              cell_ontology_class  \\\n",
       "AAACCCAAGCAACAAT-1-0                                          NaN   \n",
       "AAACCCAAGGAGGGTG-1-0                                          NaN   \n",
       "AAACCCAAGGTCCCTG-1-0                                          NaN   \n",
       "AAACCCACAACGCATT-1-0                                          NaN   \n",
       "AAACCCACACCAGCGT-1-0                                          NaN   \n",
       "...                                                           ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1-1                 fibroblast of lung   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1-1                            NK cell   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1-1  pulmonary interstitial fibroblast   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1-1                   adventitial cell   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1-1                 fibroblast of lung   \n",
       "\n",
       "                               cell_ontology_id         free_annotation  \\\n",
       "AAACCCAAGCAACAAT-1-0                        NaN                     NaN   \n",
       "AAACCCAAGGAGGGTG-1-0                        NaN                     NaN   \n",
       "AAACCCAAGGTCCCTG-1-0                        NaN                     NaN   \n",
       "AAACCCACAACGCATT-1-0                        NaN                     NaN   \n",
       "AAACCCACACCAGCGT-1-0                        NaN                     NaN   \n",
       "...                                         ...                     ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1-1       CL:0000499     Alveolar Fibroblast   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1-1       CL:0000623          Natural Killer   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1-1       CL:0000499           Myofibroblast   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1-1       CL:0000499  Adventitial Fibroblast   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1-1       CL:0000499     Alveolar Fibroblast   \n",
       "\n",
       "                                 method mouse.id  n_genes   sex  \\\n",
       "AAACCCAAGCAACAAT-1-0                NaN      NaN      NaN   NaN   \n",
       "AAACCCAAGGAGGGTG-1-0                NaN      NaN      NaN   NaN   \n",
       "AAACCCAAGGTCCCTG-1-0                NaN      NaN      NaN   NaN   \n",
       "AAACCCACAACGCATT-1-0                NaN      NaN      NaN   NaN   \n",
       "AAACCCACACCAGCGT-1-0                NaN      NaN      NaN   NaN   \n",
       "...                                 ...      ...      ...   ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1-1  droplet  3-M-7/8      NaN  male   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1-1  droplet  3-M-7/8      NaN  male   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1-1  droplet  3-M-7/8      NaN  male   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1-1  droplet  3-M-7/8      NaN  male   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1-1  droplet  3-M-7/8      NaN  male   \n",
       "\n",
       "                                           subtissue tissue  \\\n",
       "AAACCCAAGCAACAAT-1-0                             NaN    NaN   \n",
       "AAACCCAAGGAGGGTG-1-0                             NaN    NaN   \n",
       "AAACCCAAGGTCCCTG-1-0                             NaN    NaN   \n",
       "AAACCCACAACGCATT-1-0                             NaN    NaN   \n",
       "AAACCCACACCAGCGT-1-0                             NaN    NaN   \n",
       "...                                              ...    ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1-1  immune-endo-depleted   Lung   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1-1  immune-endo-depleted   Lung   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1-1  immune-endo-depleted   Lung   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1-1  immune-endo-depleted   Lung   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1-1  immune-endo-depleted   Lung   \n",
       "\n",
       "                               tissue_free_annotation batch  \n",
       "AAACCCAAGCAACAAT-1-0                              NaN     0  \n",
       "AAACCCAAGGAGGGTG-1-0                              NaN     0  \n",
       "AAACCCAAGGTCCCTG-1-0                              NaN     0  \n",
       "AAACCCACAACGCATT-1-0                              NaN     0  \n",
       "AAACCCACACCAGCGT-1-0                              NaN     0  \n",
       "...                                               ...   ...  \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1-1                   Lung     1  \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1-1                   Lung     1  \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1-1                   Lung     1  \n",
       "10X_P8_13_TTTGTCACATATGAGA-1-1                   Lung     1  \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1-1                   Lung     1  \n",
       "\n",
       "[36864 rows x 13 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata = adata.concatenate(ref_data)\n",
    "adata.obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "345dfc18",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.layers[\"counts\"] = adata.X.copy()\n",
    "sc.pp.normalize_total(adata, target_sum=1e4)\n",
    "sc.pp.log1p(adata)\n",
    "adata.raw = adata\n",
    "\n",
    "sc.pp.highly_variable_genes(adata, flavor = 'seurat_v3', n_top_genes=2000,\n",
    "                            layer = \"counts\", batch_key=\"batch\", subset = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4efec30e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jrlab2019/miniconda3/envs/sc/lib/python3.9/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: Torchmetrics v0.9 introduced a new argument class property called `full_state_update` that has\n",
      "                not been set for this class (ElboMetric). The property determines if `update` by\n",
      "                default needs access to the full metric state. If this is not the case, significant speedups can be\n",
      "                achieved and we recommend setting this to `False`.\n",
      "                We provide an checking function\n",
      "                `from torchmetrics.utilities import check_forward_no_full_state`\n",
      "                that can be used to check if the `full_state_update=True` (old and potential slower behaviour,\n",
      "                default for now) or if `full_state_update=False` can be used safely.\n",
      "                \n",
      "  warnings.warn(*args, **kwargs)\n",
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/217:   0%|                                      | 0/217 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jrlab2019/miniconda3/envs/sc/lib/python3.9/site-packages/scvi/distributions/_negative_binomial.py:61: UserWarning: Specified kernel cache directory could not be created! This disables kernel caching. Specified directory is /home/jrlab2019/.cache/torch/kernels. This warning will appear only once per process. (Triggered internally at  ../aten/src/ATen/native/cuda/jit_utils.cpp:860.)\n",
      "  + torch.lgamma(x + theta)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 217/217: 100%|███████| 217/217 [05:21<00:00,  1.48s/it, loss=548, v_num=1]\n"
     ]
    }
   ],
   "source": [
    "scvi.model.SCVI.setup_anndata(adata, layer = 'counts', batch_key='batch')\n",
    "vae = scvi.model.SCVI(adata)\n",
    "vae.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bc3c940d",
   "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>age</th>\n",
       "      <th>cell</th>\n",
       "      <th>cell_ontology_class</th>\n",
       "      <th>cell_ontology_id</th>\n",
       "      <th>free_annotation</th>\n",
       "      <th>method</th>\n",
       "      <th>mouse.id</th>\n",
       "      <th>n_genes</th>\n",
       "      <th>sex</th>\n",
       "      <th>subtissue</th>\n",
       "      <th>tissue</th>\n",
       "      <th>tissue_free_annotation</th>\n",
       "      <th>batch</th>\n",
       "      <th>_scvi_batch</th>\n",
       "      <th>_scvi_labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAACCCAAGCAACAAT-1-0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCCAAGGAGGGTG-1-0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCCAAGGTCCCTG-1-0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCCACAACGCATT-1-0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCCACACCAGCGT-1-0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGGTTAGGGATACC-1-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGGTTAGGGATACC</td>\n",
       "      <td>fibroblast of lung</td>\n",
       "      <td>CL:0000499</td>\n",
       "      <td>Alveolar Fibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGGTTTCCTGTACC-1-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGGTTTCCTGTACC</td>\n",
       "      <td>NK cell</td>\n",
       "      <td>CL:0000623</td>\n",
       "      <td>Natural Killer</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGTCAAGAGTCGGT-1-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGTCAAGAGTCGGT</td>\n",
       "      <td>pulmonary interstitial fibroblast</td>\n",
       "      <td>CL:0000499</td>\n",
       "      <td>Myofibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGTCACATATGAGA-1-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGTCACATATGAGA</td>\n",
       "      <td>adventitial cell</td>\n",
       "      <td>CL:0000499</td>\n",
       "      <td>Adventitial Fibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGTCAGTGGTCCGT-1-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGTCAGTGGTCCGT</td>\n",
       "      <td>fibroblast of lung</td>\n",
       "      <td>CL:0000499</td>\n",
       "      <td>Alveolar Fibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36864 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                age                        cell  \\\n",
       "AAACCCAAGCAACAAT-1-0            NaN                         NaN   \n",
       "AAACCCAAGGAGGGTG-1-0            NaN                         NaN   \n",
       "AAACCCAAGGTCCCTG-1-0            NaN                         NaN   \n",
       "AAACCCACAACGCATT-1-0            NaN                         NaN   \n",
       "AAACCCACACCAGCGT-1-0            NaN                         NaN   \n",
       "...                             ...                         ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1-1   3m  10X_P8_13_TTTGGTTAGGGATACC   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1-1   3m  10X_P8_13_TTTGGTTTCCTGTACC   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1-1   3m  10X_P8_13_TTTGTCAAGAGTCGGT   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1-1   3m  10X_P8_13_TTTGTCACATATGAGA   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1-1   3m  10X_P8_13_TTTGTCAGTGGTCCGT   \n",
       "\n",
       "                                              cell_ontology_class  \\\n",
       "AAACCCAAGCAACAAT-1-0                                      Unknown   \n",
       "AAACCCAAGGAGGGTG-1-0                                      Unknown   \n",
       "AAACCCAAGGTCCCTG-1-0                                      Unknown   \n",
       "AAACCCACAACGCATT-1-0                                      Unknown   \n",
       "AAACCCACACCAGCGT-1-0                                      Unknown   \n",
       "...                                                           ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1-1                 fibroblast of lung   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1-1                            NK cell   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1-1  pulmonary interstitial fibroblast   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1-1                   adventitial cell   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1-1                 fibroblast of lung   \n",
       "\n",
       "                               cell_ontology_id         free_annotation  \\\n",
       "AAACCCAAGCAACAAT-1-0                        NaN                     NaN   \n",
       "AAACCCAAGGAGGGTG-1-0                        NaN                     NaN   \n",
       "AAACCCAAGGTCCCTG-1-0                        NaN                     NaN   \n",
       "AAACCCACAACGCATT-1-0                        NaN                     NaN   \n",
       "AAACCCACACCAGCGT-1-0                        NaN                     NaN   \n",
       "...                                         ...                     ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1-1       CL:0000499     Alveolar Fibroblast   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1-1       CL:0000623          Natural Killer   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1-1       CL:0000499           Myofibroblast   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1-1       CL:0000499  Adventitial Fibroblast   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1-1       CL:0000499     Alveolar Fibroblast   \n",
       "\n",
       "                                 method mouse.id  n_genes   sex  \\\n",
       "AAACCCAAGCAACAAT-1-0                NaN      NaN      NaN   NaN   \n",
       "AAACCCAAGGAGGGTG-1-0                NaN      NaN      NaN   NaN   \n",
       "AAACCCAAGGTCCCTG-1-0                NaN      NaN      NaN   NaN   \n",
       "AAACCCACAACGCATT-1-0                NaN      NaN      NaN   NaN   \n",
       "AAACCCACACCAGCGT-1-0                NaN      NaN      NaN   NaN   \n",
       "...                                 ...      ...      ...   ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1-1  droplet  3-M-7/8      NaN  male   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1-1  droplet  3-M-7/8      NaN  male   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1-1  droplet  3-M-7/8      NaN  male   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1-1  droplet  3-M-7/8      NaN  male   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1-1  droplet  3-M-7/8      NaN  male   \n",
       "\n",
       "                                           subtissue tissue  \\\n",
       "AAACCCAAGCAACAAT-1-0                             NaN    NaN   \n",
       "AAACCCAAGGAGGGTG-1-0                             NaN    NaN   \n",
       "AAACCCAAGGTCCCTG-1-0                             NaN    NaN   \n",
       "AAACCCACAACGCATT-1-0                             NaN    NaN   \n",
       "AAACCCACACCAGCGT-1-0                             NaN    NaN   \n",
       "...                                              ...    ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1-1  immune-endo-depleted   Lung   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1-1  immune-endo-depleted   Lung   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1-1  immune-endo-depleted   Lung   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1-1  immune-endo-depleted   Lung   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1-1  immune-endo-depleted   Lung   \n",
       "\n",
       "                               tissue_free_annotation batch  _scvi_batch  \\\n",
       "AAACCCAAGCAACAAT-1-0                              NaN     0            0   \n",
       "AAACCCAAGGAGGGTG-1-0                              NaN     0            0   \n",
       "AAACCCAAGGTCCCTG-1-0                              NaN     0            0   \n",
       "AAACCCACAACGCATT-1-0                              NaN     0            0   \n",
       "AAACCCACACCAGCGT-1-0                              NaN     0            0   \n",
       "...                                               ...   ...          ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1-1                   Lung     1            1   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1-1                   Lung     1            1   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1-1                   Lung     1            1   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1-1                   Lung     1            1   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1-1                   Lung     1            1   \n",
       "\n",
       "                                _scvi_labels  \n",
       "AAACCCAAGCAACAAT-1-0                       0  \n",
       "AAACCCAAGGAGGGTG-1-0                       0  \n",
       "AAACCCAAGGTCCCTG-1-0                       0  \n",
       "AAACCCACAACGCATT-1-0                       0  \n",
       "AAACCCACACCAGCGT-1-0                       0  \n",
       "...                                      ...  \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1-1             0  \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1-1             0  \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1-1             0  \n",
       "10X_P8_13_TTTGTCACATATGAGA-1-1             0  \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1-1             0  \n",
       "\n",
       "[36864 rows x 15 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs['cell_ontology_class'] = adata.obs['cell_ontology_class'].cat.add_categories('Unknown')\n",
    "adata.obs = adata.obs.fillna(value = {'cell_ontology_class': 'Unknown'})\n",
    "adata.obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "caa8f32f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mINFO    \u001b[0m Training for \u001b[1;36m20\u001b[0m epochs.                                                             \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jrlab2019/miniconda3/envs/sc/lib/python3.9/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: Torchmetrics v0.9 introduced a new argument class property called `full_state_update` that has\n",
      "                not been set for this class (ElboMetric). The property determines if `update` by\n",
      "                default needs access to the full metric state. If this is not the case, significant speedups can be\n",
      "                achieved and we recommend setting this to `False`.\n",
      "                We provide an checking function\n",
      "                `from torchmetrics.utilities import check_forward_no_full_state`\n",
      "                that can be used to check if the `full_state_update=True` (old and potential slower behaviour,\n",
      "                default for now) or if `full_state_update=False` can be used safely.\n",
      "                \n",
      "  warnings.warn(*args, **kwargs)\n",
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 20/20: 100%|███████████| 20/20 [01:11<00:00,  3.57s/it, loss=660, v_num=1]\n"
     ]
    }
   ],
   "source": [
    "lvae = scvi.model.SCANVI.from_scvi_model(vae, adata = adata, unlabeled_category = 'Unknown',\n",
    "                                        labels_key = 'cell_ontology_class')\n",
    "\n",
    "lvae.train(max_epochs=20, n_samples_per_label=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8b6c58ed",
   "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>age</th>\n",
       "      <th>cell</th>\n",
       "      <th>cell_ontology_class</th>\n",
       "      <th>cell_ontology_id</th>\n",
       "      <th>free_annotation</th>\n",
       "      <th>method</th>\n",
       "      <th>mouse.id</th>\n",
       "      <th>n_genes</th>\n",
       "      <th>sex</th>\n",
       "      <th>subtissue</th>\n",
       "      <th>tissue</th>\n",
       "      <th>tissue_free_annotation</th>\n",
       "      <th>batch</th>\n",
       "      <th>_scvi_batch</th>\n",
       "      <th>_scvi_labels</th>\n",
       "      <th>predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAACCCAAGCAACAAT-1-0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>alveolar macrophage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCCAAGGAGGGTG-1-0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>lung macrophage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCCAAGGTCCCTG-1-0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>lung macrophage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCCACAACGCATT-1-0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>lung macrophage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCCACACCAGCGT-1-0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>intermediate monocyte</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGGTTAGGGATACC-1-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGGTTAGGGATACC</td>\n",
       "      <td>fibroblast of lung</td>\n",
       "      <td>CL:0000499</td>\n",
       "      <td>Alveolar Fibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>fibroblast of lung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGGTTTCCTGTACC-1-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGGTTTCCTGTACC</td>\n",
       "      <td>NK cell</td>\n",
       "      <td>CL:0000623</td>\n",
       "      <td>Natural Killer</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NK cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGTCAAGAGTCGGT-1-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGTCAAGAGTCGGT</td>\n",
       "      <td>pulmonary interstitial fibroblast</td>\n",
       "      <td>CL:0000499</td>\n",
       "      <td>Myofibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>pulmonary interstitial fibroblast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGTCACATATGAGA-1-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGTCACATATGAGA</td>\n",
       "      <td>adventitial cell</td>\n",
       "      <td>CL:0000499</td>\n",
       "      <td>Adventitial Fibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>fibroblast of lung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGTCAGTGGTCCGT-1-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGTCAGTGGTCCGT</td>\n",
       "      <td>fibroblast of lung</td>\n",
       "      <td>CL:0000499</td>\n",
       "      <td>Alveolar Fibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>fibroblast of lung</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36864 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                age                        cell  \\\n",
       "AAACCCAAGCAACAAT-1-0            NaN                         NaN   \n",
       "AAACCCAAGGAGGGTG-1-0            NaN                         NaN   \n",
       "AAACCCAAGGTCCCTG-1-0            NaN                         NaN   \n",
       "AAACCCACAACGCATT-1-0            NaN                         NaN   \n",
       "AAACCCACACCAGCGT-1-0            NaN                         NaN   \n",
       "...                             ...                         ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1-1   3m  10X_P8_13_TTTGGTTAGGGATACC   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1-1   3m  10X_P8_13_TTTGGTTTCCTGTACC   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1-1   3m  10X_P8_13_TTTGTCAAGAGTCGGT   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1-1   3m  10X_P8_13_TTTGTCACATATGAGA   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1-1   3m  10X_P8_13_TTTGTCAGTGGTCCGT   \n",
       "\n",
       "                                              cell_ontology_class  \\\n",
       "AAACCCAAGCAACAAT-1-0                                      Unknown   \n",
       "AAACCCAAGGAGGGTG-1-0                                      Unknown   \n",
       "AAACCCAAGGTCCCTG-1-0                                      Unknown   \n",
       "AAACCCACAACGCATT-1-0                                      Unknown   \n",
       "AAACCCACACCAGCGT-1-0                                      Unknown   \n",
       "...                                                           ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1-1                 fibroblast of lung   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1-1                            NK cell   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1-1  pulmonary interstitial fibroblast   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1-1                   adventitial cell   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1-1                 fibroblast of lung   \n",
       "\n",
       "                               cell_ontology_id         free_annotation  \\\n",
       "AAACCCAAGCAACAAT-1-0                        NaN                     NaN   \n",
       "AAACCCAAGGAGGGTG-1-0                        NaN                     NaN   \n",
       "AAACCCAAGGTCCCTG-1-0                        NaN                     NaN   \n",
       "AAACCCACAACGCATT-1-0                        NaN                     NaN   \n",
       "AAACCCACACCAGCGT-1-0                        NaN                     NaN   \n",
       "...                                         ...                     ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1-1       CL:0000499     Alveolar Fibroblast   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1-1       CL:0000623          Natural Killer   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1-1       CL:0000499           Myofibroblast   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1-1       CL:0000499  Adventitial Fibroblast   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1-1       CL:0000499     Alveolar Fibroblast   \n",
       "\n",
       "                                 method mouse.id  n_genes   sex  \\\n",
       "AAACCCAAGCAACAAT-1-0                NaN      NaN      NaN   NaN   \n",
       "AAACCCAAGGAGGGTG-1-0                NaN      NaN      NaN   NaN   \n",
       "AAACCCAAGGTCCCTG-1-0                NaN      NaN      NaN   NaN   \n",
       "AAACCCACAACGCATT-1-0                NaN      NaN      NaN   NaN   \n",
       "AAACCCACACCAGCGT-1-0                NaN      NaN      NaN   NaN   \n",
       "...                                 ...      ...      ...   ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1-1  droplet  3-M-7/8      NaN  male   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1-1  droplet  3-M-7/8      NaN  male   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1-1  droplet  3-M-7/8      NaN  male   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1-1  droplet  3-M-7/8      NaN  male   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1-1  droplet  3-M-7/8      NaN  male   \n",
       "\n",
       "                                           subtissue tissue  \\\n",
       "AAACCCAAGCAACAAT-1-0                             NaN    NaN   \n",
       "AAACCCAAGGAGGGTG-1-0                             NaN    NaN   \n",
       "AAACCCAAGGTCCCTG-1-0                             NaN    NaN   \n",
       "AAACCCACAACGCATT-1-0                             NaN    NaN   \n",
       "AAACCCACACCAGCGT-1-0                             NaN    NaN   \n",
       "...                                              ...    ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1-1  immune-endo-depleted   Lung   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1-1  immune-endo-depleted   Lung   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1-1  immune-endo-depleted   Lung   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1-1  immune-endo-depleted   Lung   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1-1  immune-endo-depleted   Lung   \n",
       "\n",
       "                               tissue_free_annotation batch  _scvi_batch  \\\n",
       "AAACCCAAGCAACAAT-1-0                              NaN     0            0   \n",
       "AAACCCAAGGAGGGTG-1-0                              NaN     0            0   \n",
       "AAACCCAAGGTCCCTG-1-0                              NaN     0            0   \n",
       "AAACCCACAACGCATT-1-0                              NaN     0            0   \n",
       "AAACCCACACCAGCGT-1-0                              NaN     0            0   \n",
       "...                                               ...   ...          ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1-1                   Lung     1            1   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1-1                   Lung     1            1   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1-1                   Lung     1            1   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1-1                   Lung     1            1   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1-1                   Lung     1            1   \n",
       "\n",
       "                                _scvi_labels  \\\n",
       "AAACCCAAGCAACAAT-1-0                      30   \n",
       "AAACCCAAGGAGGGTG-1-0                      30   \n",
       "AAACCCAAGGTCCCTG-1-0                      30   \n",
       "AAACCCACAACGCATT-1-0                      30   \n",
       "AAACCCACACCAGCGT-1-0                      30   \n",
       "...                                      ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1-1            14   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1-1             3   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1-1            25   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1-1             5   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1-1            14   \n",
       "\n",
       "                                                        predicted  \n",
       "AAACCCAAGCAACAAT-1-0                          alveolar macrophage  \n",
       "AAACCCAAGGAGGGTG-1-0                              lung macrophage  \n",
       "AAACCCAAGGTCCCTG-1-0                              lung macrophage  \n",
       "AAACCCACAACGCATT-1-0                              lung macrophage  \n",
       "AAACCCACACCAGCGT-1-0                        intermediate monocyte  \n",
       "...                                                           ...  \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1-1                 fibroblast of lung  \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1-1                            NK cell  \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1-1  pulmonary interstitial fibroblast  \n",
       "10X_P8_13_TTTGTCACATATGAGA-1-1                 fibroblast of lung  \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1-1                 fibroblast of lung  \n",
       "\n",
       "[36864 rows x 16 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs['predicted'] = lvae.predict(adata)\n",
    "adata.obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9573279f",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.obs['bc2'] = adata.obs.index.map(lambda x: x[:-2])\n",
    "\n",
    "cell_mapper = dict(zip(adata.obs.bc2, adata.obs.predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "edc96cff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AAACCCAAGCAACAAT-1': 'alveolar macrophage',\n",
       " 'AAACCCAAGGAGGGTG-1': 'lung macrophage',\n",
       " 'AAACCCAAGGTCCCTG-1': 'lung macrophage',\n",
       " 'AAACCCACAACGCATT-1': 'lung macrophage',\n",
       " 'AAACCCACACCAGCGT-1': 'intermediate monocyte',\n",
       " 'AAACCCACATCGTGCG-1': 'B cell',\n",
       " 'AAACCCACATGCGGTC-1': 'bronchial smooth muscle cell',\n",
       " 'AAACCCAGTGACTATC-1': 'neutrophil',\n",
       " 'AAACCCATCACCGGGT-1': 'bronchial smooth muscle cell',\n",
       " 'AAACCCATCACTGGGC-1': 'intermediate monocyte',\n",
       " 'AAACCCATCCGAGGCT-1': 'mature NK T cell',\n",
       " 'AAACGAACAAGAATGT-1': 'bronchial smooth muscle cell',\n",
       " 'AAACGAACAGCTAACT-1': 'lung macrophage',\n",
       " 'AAACGAAGTAAGTCAA-1': 'classical monocyte',\n",
       " 'AAACGAATCCTGTTAT-1': 'endothelial cell of lymphatic vessel',\n",
       " 'AAACGAATCGTGCACG-1': 'bronchial smooth muscle cell',\n",
       " 'AAACGAATCTCACCCA-1': 'lung macrophage',\n",
       " 'AAACGCTAGCGTATAA-1': 'basophil',\n",
       " 'AAACGCTAGGGCAAGG-1': 'regulatory T cell',\n",
       " 'AAACGCTAGTAAGAGG-1': 'bronchial smooth muscle cell',\n",
       " 'AAACGCTAGTATGACA-1': 'lung macrophage',\n",
       " 'AAACGCTAGTCGCCCA-1': 'alveolar macrophage',\n",
       " 'AAACGCTGTAAGTAGT-1': 'regulatory T cell',\n",
       " 'AAACGCTTCACAGTGT-1': 'myeloid dendritic cell',\n",
       " 'AAACGCTTCCCTGGTT-1': 'mature NK T cell',\n",
       " 'AAACGCTTCTGGGAGA-1': 'intermediate monocyte',\n",
       " 'AAAGAACAGGCGTTAG-1': 'myeloid dendritic cell',\n",
       " 'AAAGAACAGTTGCCCG-1': 'lung macrophage',\n",
       " 'AAAGAACCAAAGGTTA-1': 'mature NK T cell',\n",
       " 'AAAGAACCAGCGTAGA-1': 'NK cell',\n",
       " 'AAAGAACCAGGTACGA-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'AAAGAACGTATCGCGC-1': 'non-classical monocyte',\n",
       " 'AAAGAACGTCTAATCG-1': 'lung macrophage',\n",
       " 'AAAGAACTCTTGCGCT-1': 'NK cell',\n",
       " 'AAAGGATAGCGCGTTC-1': 'basophil',\n",
       " 'AAAGGATAGGTAGCCA-1': 'T cell',\n",
       " 'AAAGGATCACAGTATC-1': 'alveolar macrophage',\n",
       " 'AAAGGATCACATTGTG-1': 'mature NK T cell',\n",
       " 'AAAGGATCACCGTGCA-1': 'regulatory T cell',\n",
       " 'AAAGGATCAGAGGGTT-1': 'plasma cell',\n",
       " 'AAAGGATCATGAGATA-1': 'bronchial smooth muscle cell',\n",
       " 'AAAGGATGTCAGGAGT-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'AAAGGATGTCATTCCC-1': 'neutrophil',\n",
       " 'AAAGGATGTTGGACCC-1': 'T cell',\n",
       " 'AAAGGATTCATACAGC-1': 'lung macrophage',\n",
       " 'AAAGGATTCCCGAATA-1': 'intermediate monocyte',\n",
       " 'AAAGGATTCTCGCTCA-1': 'T cell',\n",
       " 'AAAGGGCAGAATCGCG-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'AAAGGGCAGCTGGCTC-1': 'T cell',\n",
       " 'AAAGGGCAGCTTTGTG-1': 'myeloid dendritic cell',\n",
       " 'AAAGGGCAGGAAGAAC-1': 'lung macrophage',\n",
       " 'AAAGGGCCACAAACGG-1': 'mature NK T cell',\n",
       " 'AAAGGGCCATGCCGCA-1': 'lung macrophage',\n",
       " 'AAAGGGCGTACGTGAG-1': 'lung macrophage',\n",
       " 'AAAGGGCGTGCTCGTG-1': 'lung macrophage',\n",
       " 'AAAGGGCGTGTCCGTG-1': 'T cell',\n",
       " 'AAAGGGCGTTATCTGG-1': 'T cell',\n",
       " 'AAAGGGCGTTGGGAAC-1': 'regulatory T cell',\n",
       " 'AAAGGGCTCCAACACA-1': 'mature NK T cell',\n",
       " 'AAAGGGCTCCCTTGGT-1': 'myeloid dendritic cell',\n",
       " 'AAAGGGCTCTCACCCA-1': 'fibroblast of lung',\n",
       " 'AAAGGTAAGAACTCCT-1': 'intermediate monocyte',\n",
       " 'AAAGGTAAGGCACCAA-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'AAAGGTAAGTCTGCGC-1': 'basophil',\n",
       " 'AAAGGTACAACTGATC-1': 'lung macrophage',\n",
       " 'AAAGGTACACTCCCTA-1': 'bronchial smooth muscle cell',\n",
       " 'AAAGGTACATGCGTGC-1': 'lung macrophage',\n",
       " 'AAAGGTAGTATAGGGC-1': 'regulatory T cell',\n",
       " 'AAAGGTATCCTTCTGG-1': 'lung macrophage',\n",
       " 'AAAGGTATCTGGCTGG-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'AAAGTCCAGATGGTCG-1': 'regulatory T cell',\n",
       " 'AAAGTCCAGCACTCGC-1': 'vein endothelial cell',\n",
       " 'AAAGTCCAGCGTATAA-1': 'lung macrophage',\n",
       " 'AAAGTCCAGTCGGGAT-1': 'basophil',\n",
       " 'AAAGTCCCAGACCGCT-1': 'lung macrophage',\n",
       " 'AAAGTCCCATTCGATG-1': 'bronchial smooth muscle cell',\n",
       " 'AAAGTCCCATTGCCGG-1': 'myeloid dendritic cell',\n",
       " 'AAAGTCCCATTGGCAT-1': 'bronchial smooth muscle cell',\n",
       " 'AAAGTCCGTCCACGCA-1': 'neutrophil',\n",
       " 'AAAGTCCGTTTAAGGA-1': 'mature NK T cell',\n",
       " 'AAAGTCCTCTGTCTCG-1': 'mature NK T cell',\n",
       " 'AAAGTGAAGATGACCG-1': 'myeloid dendritic cell',\n",
       " 'AAAGTGAAGCATGTTC-1': 'regulatory T cell',\n",
       " 'AAAGTGAAGCGACTTT-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'AAAGTGAAGTGACCTT-1': 'fibroblast of lung',\n",
       " 'AAAGTGACAAATCCCA-1': 'mature NK T cell',\n",
       " 'AAAGTGACAATCTCTT-1': 'vein endothelial cell',\n",
       " 'AAAGTGACATGCAGGA-1': 'lung macrophage',\n",
       " 'AAAGTGAGTATCCTCC-1': 'lung macrophage',\n",
       " 'AAAGTGATCATGCTAG-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'AAAGTGATCCGGCAGT-1': 'T cell',\n",
       " 'AAAGTGATCGCTCTCA-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'AAATGGAAGCAAACAT-1': 'mature NK T cell',\n",
       " 'AAATGGAAGCCGCACT-1': 'B cell',\n",
       " 'AAATGGAAGCCGTTGC-1': 'bronchial smooth muscle cell',\n",
       " 'AAATGGAAGGATTTGA-1': 'neutrophil',\n",
       " 'AAATGGAAGGCCATAG-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'AAATGGAAGTCTGGTT-1': 'basophil',\n",
       " 'AAATGGACAGAACCGA-1': 'basophil',\n",
       " 'AAATGGACATAATCGC-1': 'mature NK T cell',\n",
       " 'AAATGGACATAGGTAA-1': 'T cell',\n",
       " 'AAATGGAGTACAAGTA-1': 'lung macrophage',\n",
       " 'AAATGGAGTAGGTCAG-1': 'lung macrophage',\n",
       " 'AAATGGAGTGGCAGAT-1': 'basophil',\n",
       " 'AAATGGAGTTTACTGG-1': 'B cell',\n",
       " 'AAATGGATCAGACCCG-1': 'lung macrophage',\n",
       " 'AAATGGATCAGCCTCT-1': 'lung macrophage',\n",
       " 'AAATGGATCATTTCGT-1': 'basophil',\n",
       " 'AAATGGATCCAACCAA-1': 'T cell',\n",
       " 'AAATGGATCTGGCTGG-1': 'lung macrophage',\n",
       " 'AACAAAGAGCAAGCCA-1': 'lung macrophage',\n",
       " 'AACAAAGAGTGGCGAT-1': 'B cell',\n",
       " 'AACAAAGCAAGACGGT-1': 'NK cell',\n",
       " 'AACAAAGCAGCTACCG-1': 'lung macrophage',\n",
       " 'AACAAAGGTCCGAAAG-1': 'classical monocyte',\n",
       " 'AACAAAGGTGAATGAT-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'AACAAAGGTTGCCATA-1': 'B cell',\n",
       " 'AACAAAGTCATCGCTC-1': 'bronchial smooth muscle cell',\n",
       " 'AACAAAGTCCATGCAA-1': 'lung macrophage',\n",
       " 'AACAAAGTCGGCTATA-1': 'neutrophil',\n",
       " 'AACAACCAGCAGAAAG-1': 'mature NK T cell',\n",
       " 'AACAACCAGTACTGTC-1': 'lung macrophage',\n",
       " 'AACAACCAGTTGCGCC-1': 'lung macrophage',\n",
       " 'AACAACCCAAGAAACT-1': 'mature NK T cell',\n",
       " 'AACAACCCAGGAGGTT-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'AACAACCCAGTCCGTG-1': 'lung macrophage',\n",
       " 'AACAACCGTACTCGTA-1': 'lung macrophage',\n",
       " 'AACAACCGTGAGGATC-1': 'myeloid dendritic cell',\n",
       " 'AACAACCGTTAAACCC-1': 'mature NK T cell',\n",
       " 'AACAACCTCAATCAGC-1': 'T cell',\n",
       " 'AACAACCTCAGCTCTC-1': 'T cell',\n",
       " 'AACAACCTCTAGAACC-1': 'B cell',\n",
       " 'AACAAGAAGACAGCTG-1': 'bronchial smooth muscle cell',\n",
       " 'AACAAGAAGTACAACA-1': 'lung macrophage',\n",
       " 'AACAAGAAGTATAGAC-1': 'classical monocyte',\n",
       " 'AACAAGAAGTGTCATC-1': 'T cell',\n",
       " 'AACAAGACAAACTGCT-1': 'mature NK T cell',\n",
       " 'AACAAGACACAGGATG-1': 'T cell',\n",
       " 'AACAAGACACGCAGTC-1': 'T cell',\n",
       " 'AACAAGACAGATCATC-1': 'classical monocyte',\n",
       " 'AACAAGACAGTAACCT-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'AACAAGAGTAGACAAT-1': 'lung macrophage',\n",
       " 'AACAAGAGTCCACTCT-1': 'basophil',\n",
       " 'AACAAGAGTCTTGCTC-1': 'alveolar macrophage',\n",
       " 'AACAAGAGTTCCTAGA-1': 'vein endothelial cell',\n",
       " 'AACAAGATCAAACTGC-1': 'lung macrophage',\n",
       " 'AACAAGATCAAGTGGG-1': 'mature NK T cell',\n",
       " 'AACAAGATCGGTTGTA-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'AACACACCAATCGCCG-1': 'T cell',\n",
       " 'AACACACCACGCTATA-1': 'neutrophil',\n",
       " 'AACACACCATACCACA-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'AACACACCATGACCCG-1': 'lung macrophage',\n",
       " 'AACACACGTATGGTTC-1': 'fibroblast of lung',\n",
       " 'AACACACGTCTACGAT-1': 'classical monocyte',\n",
       " 'AACACACGTCTGCCTT-1': 'fibroblast of lung',\n",
       " 'AACACACGTGGACTGA-1': 'bronchial smooth muscle cell',\n",
       " 'AACACACGTTTCGATG-1': 'myeloid dendritic cell',\n",
       " 'AACACACTCTCATGGA-1': 'neutrophil',\n",
       " 'AACAGGGAGAAATCCA-1': 'B cell',\n",
       " 'AACAGGGAGAGTGTGC-1': 'lung macrophage',\n",
       " 'AACAGGGAGCGTCGAA-1': 'B cell',\n",
       " 'AACAGGGCAGCAGTTT-1': 'T cell',\n",
       " 'AACAGGGCATCTCATT-1': 'basophil',\n",
       " 'AACAGGGGTCAATCTG-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'AACAGGGGTGCCTTTC-1': 'B cell',\n",
       " 'AACAGGGTCATGAAAG-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'AACAGGGTCCGTGTGG-1': 'myeloid dendritic cell',\n",
       " 'AACAGGGTCTGATTCT-1': 'regulatory T cell',\n",
       " 'AACAGGGTCTTCTGTA-1': 'T cell',\n",
       " 'AACCAACAGGTGGGTT-1': 'lung macrophage',\n",
       " 'AACCAACAGTGGACGT-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'AACCAACAGTTCCGGC-1': 'neutrophil',\n",
       " 'AACCAACCAGGTCCGT-1': 'T cell',\n",
       " 'AACCAACCATGACTAC-1': 'B cell',\n",
       " 'AACCAACGTTCGAGCC-1': 'mature NK T cell',\n",
       " 'AACCAACGTTGGCTAT-1': 'B cell',\n",
       " 'AACCAACTCACTTGTT-1': 'basophil',\n",
       " 'AACCAACTCGTACCTC-1': 'lung macrophage',\n",
       " 'AACCACAAGAAATTCG-1': 'basophil',\n",
       " 'AACCACAAGATGTTCC-1': 'fibroblast of lung',\n",
       " 'AACCACAAGCGATGCA-1': 'classical monocyte',\n",
       " 'AACCACAAGTCTTGGT-1': 'bronchial smooth muscle cell',\n",
       " 'AACCACACAATCAAGA-1': 'T cell',\n",
       " 'AACCACACACTCCTGT-1': 'basophil',\n",
       " 'AACCACAGTAATACCC-1': 'fibroblast of lung',\n",
       " 'AACCACAGTCTACGAT-1': 'lung macrophage',\n",
       " 'AACCACATCATAGACC-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'AACCACATCGTGGCTG-1': 'bronchial smooth muscle cell',\n",
       " 'AACCACATCTAGCCTC-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'AACCACATCTTCTGTA-1': 'non-classical monocyte',\n",
       " 'AACCATGAGCGTCTCG-1': 'T cell',\n",
       " 'AACCATGAGGTTGGTG-1': 'basophil',\n",
       " 'AACCATGAGTAGTGCG-1': 'non-classical monocyte',\n",
       " 'AACCATGAGTATTCCG-1': 'basophil',\n",
       " 'AACCATGCAGCTTCGG-1': 'basophil',\n",
       " 'AACCATGCATGAAGGC-1': 'basophil',\n",
       " 'AACCATGGTATACCTG-1': 'lung macrophage',\n",
       " 'AACCATGGTATGCTAC-1': 'mature NK T cell',\n",
       " 'AACCATGGTCGTCGGT-1': 'lung macrophage',\n",
       " 'AACCATGGTGCAACGA-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'AACCATGGTTTAGTCG-1': 'mature NK T cell',\n",
       " 'AACCATGTCAAGGTGG-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'AACCATGTCCTCACCA-1': 'mature NK T cell',\n",
       " 'AACCATGTCGCTAAAC-1': 'T cell',\n",
       " 'AACCATGTCTGAACGT-1': 'regulatory T cell',\n",
       " 'AACCATGTCTTGCAGA-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'AACCCAAAGAGTGTGC-1': 'smooth muscle cell of the pulmonary artery',\n",
       " 'AACCCAAAGATAGCTA-1': 'intermediate monocyte',\n",
       " 'AACCCAAAGATCCAAA-1': 'plasma cell',\n",
       " 'AACCCAAAGCACTTTG-1': 'fibroblast of lung',\n",
       " 'AACCCAAAGCAGCAGT-1': 'basophil',\n",
       " 'AACCCAACACCATAAC-1': 'mature NK T cell',\n",
       " 'AACCCAACAGGTCTCG-1': 'intermediate monocyte',\n",
       " 'AACCCAAGTTGCATAC-1': 'bronchial smooth muscle cell',\n",
       " 'AACCCAATCAAGTCTG-1': 'neutrophil',\n",
       " 'AACCCAATCAGACTGT-1': 'lung macrophage',\n",
       " 'AACCCAATCTCAACCC-1': 'lung macrophage',\n",
       " 'AACCTGAAGAGGCCAT-1': 'lung macrophage',\n",
       " 'AACCTGAAGCAATAGT-1': 'lung macrophage',\n",
       " 'AACCTGAAGGGAGGGT-1': 'mature NK T cell',\n",
       " 'AACCTGAAGGTGCAGT-1': 'basophil',\n",
       " 'AACCTGAAGTTTGAGA-1': 'pulmonary interstitial fibroblast',\n",
       " 'AACCTGACAACACTAC-1': 'NK cell',\n",
       " 'AACCTGACACCTTCCA-1': 'bronchial smooth muscle cell',\n",
       " 'AACCTGACATCAGTCA-1': 'myeloid dendritic cell',\n",
       " 'AACCTGACATGAATAG-1': 'basophil',\n",
       " 'AACCTGAGTCGCATTA-1': 'lung macrophage',\n",
       " 'AACCTGAGTGTAAACA-1': 'neutrophil',\n",
       " 'AACCTGATCCCATTTA-1': 'neutrophil',\n",
       " 'AACCTGATCCCTCTAG-1': 'basophil',\n",
       " 'AACCTGATCGACATAC-1': 'neutrophil',\n",
       " 'AACCTGATCGTTCCCA-1': 'lung macrophage',\n",
       " 'AACCTTTAGAGTCAAT-1': 'regulatory T cell',\n",
       " 'AACCTTTAGATCCGAG-1': 'lung macrophage',\n",
       " 'AACCTTTAGCACTCCG-1': 'B cell',\n",
       " 'AACCTTTAGCGAAACC-1': 'neutrophil',\n",
       " 'AACCTTTAGGAATGTT-1': 'T cell',\n",
       " 'AACCTTTAGTCAGAGC-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'AACCTTTCAGAGATGC-1': 'lung macrophage',\n",
       " 'AACCTTTCAGCGTGAA-1': 'lung macrophage',\n",
       " 'AACCTTTCATGTTACG-1': 'intermediate monocyte',\n",
       " 'AACCTTTGTCACGTGC-1': 'classical monocyte',\n",
       " 'AACCTTTGTCTGATAC-1': 'lung macrophage',\n",
       " 'AACCTTTGTGTTCGTA-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'AACCTTTTCAGCCTCT-1': 'B cell',\n",
       " 'AACCTTTTCATAGACC-1': 'lung macrophage',\n",
       " 'AACCTTTTCATTCGTT-1': 'bronchial smooth muscle cell',\n",
       " 'AACCTTTTCCAAGAGG-1': 'lung macrophage',\n",
       " 'AACCTTTTCGATACTG-1': 'lung macrophage',\n",
       " 'AACGAAAAGGATGGCT-1': 'classical monocyte',\n",
       " 'AACGAAACAACACACT-1': 'type II pneumocyte',\n",
       " 'AACGAAAGTTTCGGCG-1': 'T cell',\n",
       " 'AACGAAATCCATGAGT-1': 'neutrophil',\n",
       " 'AACGAAATCCCGAGGT-1': 'basophil',\n",
       " 'AACGAAATCGATACTG-1': 'bronchial smooth muscle cell',\n",
       " 'AACGAAATCGGCTGAC-1': 'lung macrophage',\n",
       " 'AACGAAATCGTTCTGC-1': 'neutrophil',\n",
       " 'AACGAAATCTCCTGAC-1': 'lung macrophage',\n",
       " 'AACGGGAAGCTGACCC-1': 'neutrophil',\n",
       " 'AACGGGAAGGACAAGA-1': 'lung macrophage',\n",
       " 'AACGGGACAAACAGGC-1': 'neutrophil',\n",
       " 'AACGGGACATCGGCCA-1': 'NK cell',\n",
       " 'AACGGGAGTACGAGCA-1': 'neutrophil',\n",
       " 'AACGGGAGTCCACTCT-1': 'T cell',\n",
       " 'AACGGGAGTTAAGAAC-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'AACGGGATCGCTACGG-1': 'NK cell',\n",
       " 'AACGTCAAGCTGTACT-1': 'lung macrophage',\n",
       " 'AACGTCACAAGGTCAG-1': 'B cell',\n",
       " 'AACGTCACACATTACG-1': 'bronchial smooth muscle cell',\n",
       " 'AACGTCACAGAGCTAG-1': 'bronchial smooth muscle cell',\n",
       " 'AACGTCACAGTGGCTC-1': 'regulatory T cell',\n",
       " 'AACGTCACAGTTCCAA-1': 'regulatory T cell',\n",
       " 'AACGTCAGTACAGTAA-1': 'lung macrophage',\n",
       " 'AACGTCAGTACATACC-1': 'mature NK T cell',\n",
       " 'AACGTCAGTCCTGGGT-1': 'lung macrophage',\n",
       " 'AACGTCAGTGACCGTC-1': 'lung macrophage',\n",
       " 'AACGTCAGTTTCGTGA-1': 'T cell',\n",
       " 'AACGTCATCAGACCGC-1': 'bronchial smooth muscle cell',\n",
       " 'AACGTCATCGACGCGT-1': 'neutrophil',\n",
       " 'AACGTCATCTCCGAAA-1': 'bronchial smooth muscle cell',\n",
       " 'AACTTCTAGACCATTC-1': 'lung macrophage',\n",
       " 'AACTTCTAGCAAGTGC-1': 'lung macrophage',\n",
       " 'AACTTCTAGGCCCACT-1': 'myeloid dendritic cell',\n",
       " 'AACTTCTCAATGCTCA-1': 'classical monocyte',\n",
       " 'AACTTCTCACATATCG-1': 'T cell',\n",
       " 'AACTTCTGTAGCGTCC-1': 'B cell',\n",
       " 'AACTTCTGTATGGAAT-1': 'non-classical monocyte',\n",
       " 'AACTTCTGTCGCATGC-1': 'lung macrophage',\n",
       " 'AACTTCTGTGTCTTCC-1': 'neutrophil',\n",
       " 'AACTTCTGTTAAACCC-1': 'lung macrophage',\n",
       " 'AACTTCTTCAAACCTG-1': 'mature NK T cell',\n",
       " 'AACTTCTTCATGAGAA-1': 'NK cell',\n",
       " 'AAGAACAAGCAATTCC-1': 'T cell',\n",
       " 'AAGAACAAGTCACGAG-1': 'basophil',\n",
       " 'AAGAACAAGTTCCATG-1': 'bronchial smooth muscle cell',\n",
       " 'AAGAACATCAGAGCGA-1': 'bronchial smooth muscle cell',\n",
       " 'AAGAACATCAGGACGA-1': 'T cell',\n",
       " 'AAGAACATCGAGTACT-1': 'basophil',\n",
       " 'AAGAACATCTTTGCAT-1': 'lung macrophage',\n",
       " 'AAGACAAAGAGAGGTA-1': 'myeloid dendritic cell',\n",
       " 'AAGACAAAGTGTAGAT-1': 'B cell',\n",
       " 'AAGACAAAGTTCCGGC-1': 'classical monocyte',\n",
       " 'AAGACAAAGTTGCTCA-1': 'lung macrophage',\n",
       " 'AAGACAACATAACAGA-1': 'basophil',\n",
       " 'AAGACAAGTAGTCGGA-1': 'lung macrophage',\n",
       " 'AAGACAAGTTAGCGGA-1': 'myeloid dendritic cell',\n",
       " 'AAGACAAGTTCTAACG-1': 'lung macrophage',\n",
       " 'AAGACAAGTTTAGACC-1': 'basophil',\n",
       " 'AAGACAATCTGTCGCT-1': 'lung macrophage',\n",
       " 'AAGACTCAGAACTGAT-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'AAGACTCAGATCCGAG-1': 'intermediate monocyte',\n",
       " 'AAGACTCAGTCCGCCA-1': 'B cell',\n",
       " 'AAGACTCAGTGGACTG-1': 'neutrophil',\n",
       " 'AAGACTCCAACCCTAA-1': 'myeloid dendritic cell',\n",
       " 'AAGACTCCACAGGATG-1': 'NK cell',\n",
       " 'AAGACTCCACTGGATT-1': 'lung macrophage',\n",
       " 'AAGACTCCATACGCAT-1': 'fibroblast of lung',\n",
       " 'AAGACTCTCCCACAGG-1': 'mature NK T cell',\n",
       " 'AAGACTCTCCCAGCGA-1': 'B cell',\n",
       " 'AAGACTCTCGCCGTGA-1': 'mature NK T cell',\n",
       " 'AAGACTCTCGCTGACG-1': 'T cell',\n",
       " 'AAGACTCTCTTACGGA-1': 'B cell',\n",
       " 'AAGACTCTCTTCTGGC-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'AAGATAGAGACCATGG-1': 'type II pneumocyte',\n",
       " 'AAGATAGAGACTTCCA-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'AAGATAGAGATGTAGT-1': 'adventitial cell',\n",
       " 'AAGATAGAGCATGCAG-1': 'myeloid dendritic cell',\n",
       " 'AAGATAGAGCCTTCTC-1': 'lung macrophage',\n",
       " 'AAGATAGAGGATATGT-1': 'lung macrophage',\n",
       " 'AAGATAGAGGCTAAAT-1': 'lung macrophage',\n",
       " 'AAGATAGAGTGTTGTC-1': 'B cell',\n",
       " 'AAGATAGCAAGTGACG-1': 'mature NK T cell',\n",
       " 'AAGATAGCACTAGGCC-1': 'basophil',\n",
       " 'AAGATAGCACTGAGTT-1': 'regulatory T cell',\n",
       " 'AAGATAGCAGACAAGC-1': 'lung macrophage',\n",
       " 'AAGATAGCAGGCTTGC-1': 'lung macrophage',\n",
       " 'AAGATAGCAGGTTCGC-1': 'myeloid dendritic cell',\n",
       " 'AAGATAGCATGCGTGC-1': 'regulatory T cell',\n",
       " 'AAGATAGTCATTACTC-1': 'lung macrophage',\n",
       " 'AAGATAGTCTACACTT-1': 'lung macrophage',\n",
       " 'AAGATAGTCTCAAAGC-1': 'mature NK T cell',\n",
       " 'AAGATAGTCTGTTGGA-1': 'regulatory T cell',\n",
       " 'AAGCATCAGGTTGGAC-1': 'T cell',\n",
       " 'AAGCATCCACAAATAG-1': 'mature NK T cell',\n",
       " 'AAGCATCCACGCGTCA-1': 'B cell',\n",
       " 'AAGCATCCATCCAATG-1': 'T cell',\n",
       " 'AAGCATCCATGTGTCA-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'AAGCATCGTACCGTGC-1': 'lung macrophage',\n",
       " 'AAGCATCGTAGACACG-1': 'myeloid dendritic cell',\n",
       " 'AAGCATCGTCGCGTTG-1': 'T cell',\n",
       " 'AAGCATCGTGCATCTA-1': 'lung macrophage',\n",
       " 'AAGCATCGTGCCCAGT-1': 'B cell',\n",
       " 'AAGCATCGTGGGCTCT-1': 'B cell',\n",
       " 'AAGCATCGTGTCTTCC-1': 'B cell',\n",
       " 'AAGCATCTCATCCTGC-1': 'neutrophil',\n",
       " 'AAGCATCTCTGCTGAA-1': 'B cell',\n",
       " 'AAGCATCTCTTTGCGC-1': 'bronchial smooth muscle cell',\n",
       " 'AAGCCATAGAAACCCG-1': 'B cell',\n",
       " 'AAGCCATAGGAGTCTG-1': 'regulatory T cell',\n",
       " 'AAGCCATCACAGAAGC-1': 'neutrophil',\n",
       " 'AAGCCATCATGAGATA-1': 'intermediate monocyte',\n",
       " 'AAGCCATGTCTTGGTA-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'AAGCCATGTGCGTCGT-1': 'myeloid dendritic cell',\n",
       " 'AAGCCATGTTCCAAAC-1': 'lung macrophage',\n",
       " 'AAGCCATTCCGTATAG-1': 'basophil',\n",
       " 'AAGCCATTCCGTGTCT-1': 'bronchial smooth muscle cell',\n",
       " 'AAGCCATTCGGCATCG-1': 'lung macrophage',\n",
       " 'AAGCGAGAGACCAAGC-1': 'pericyte cell',\n",
       " 'AAGCGAGAGCATGATA-1': 'neutrophil',\n",
       " 'AAGCGAGAGCGCCTAC-1': 'basophil',\n",
       " 'AAGCGAGAGTTGCTCA-1': 'lung macrophage',\n",
       " 'AAGCGAGCAAGTCCCG-1': 'B cell',\n",
       " 'AAGCGAGCACTGCGTG-1': 'lung macrophage',\n",
       " 'AAGCGAGGTCATAACC-1': 'classical monocyte',\n",
       " 'AAGCGAGGTCATAGTC-1': 'intermediate monocyte',\n",
       " 'AAGCGAGGTCCGTACG-1': 'mature NK T cell',\n",
       " 'AAGCGAGGTGCCTGAC-1': 'T cell',\n",
       " 'AAGCGAGGTGCTAGCC-1': 'regulatory T cell',\n",
       " 'AAGCGAGGTGTTACTG-1': 'lung macrophage',\n",
       " 'AAGCGAGTCATTATCC-1': 'neutrophil',\n",
       " 'AAGCGTTAGACCGCCT-1': 'T cell',\n",
       " 'AAGCGTTAGGGTCACA-1': 'mature NK T cell',\n",
       " 'AAGCGTTAGTGCCGAA-1': 'B cell',\n",
       " 'AAGCGTTAGTTCCGGC-1': 'lung macrophage',\n",
       " 'AAGCGTTGTCTACACA-1': 'intermediate monocyte',\n",
       " 'AAGCGTTGTCTGCGCA-1': 'T cell',\n",
       " 'AAGCGTTTCGCGTGAC-1': 'bronchial smooth muscle cell',\n",
       " 'AAGGAATAGACTTGTC-1': 'regulatory T cell',\n",
       " 'AAGGAATAGCACTTTG-1': 'lung macrophage',\n",
       " 'AAGGAATAGCGTGTTT-1': 'bronchial smooth muscle cell',\n",
       " 'AAGGAATAGCTTAAGA-1': 'lung macrophage',\n",
       " 'AAGGAATAGGGCCTCT-1': 'basophil',\n",
       " 'AAGGAATCAATCCTTT-1': 'neutrophil',\n",
       " 'AAGGAATCACCCTCTA-1': 'T cell',\n",
       " 'AAGGAATCATGGATCT-1': 'regulatory T cell',\n",
       " 'AAGGAATCATGTAACC-1': 'lung macrophage',\n",
       " 'AAGGAATGTCCAAATC-1': 'neutrophil',\n",
       " 'AAGGAATGTCGGAACA-1': 'mature NK T cell',\n",
       " 'AAGGAATGTTCGGCCA-1': 'B cell',\n",
       " 'AAGGAATGTTTCGTGA-1': 'myeloid dendritic cell',\n",
       " 'AAGGAATTCCCAGTGG-1': 'B cell',\n",
       " 'AAGGAATTCGCCATAA-1': 'vein endothelial cell',\n",
       " 'AAGGAATTCGTCGATA-1': 'lung macrophage',\n",
       " 'AAGGAATTCTCGTGAA-1': 'neutrophil',\n",
       " 'AAGGTAAAGCACTAGG-1': 'lung macrophage',\n",
       " 'AAGGTAAAGCCTATTG-1': 'lung macrophage',\n",
       " 'AAGGTAAAGGAGTCTG-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'AAGGTAAAGTAGGCCA-1': 'classical monocyte',\n",
       " 'AAGGTAAAGTGCTAGG-1': 'adventitial cell',\n",
       " 'AAGGTAACAGACCGCT-1': 'neutrophil',\n",
       " 'AAGGTAACAGCACAGA-1': 'bronchial smooth muscle cell',\n",
       " 'AAGGTAACAGCGGTCT-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'AAGGTAACAGGCCCTA-1': 'mature NK T cell',\n",
       " 'AAGGTAACATTCAGCA-1': 'neutrophil',\n",
       " 'AAGGTAAGTAGACTGG-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'AAGGTAAGTGTCTAAC-1': 'lung macrophage',\n",
       " 'AAGGTAAGTTAGAGTA-1': 'myeloid dendritic cell',\n",
       " 'AAGGTAAGTTCGCGTG-1': 'basophil',\n",
       " 'AAGGTAAGTTGTGTAC-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'AAGGTAATCCAAAGGG-1': 'lung macrophage',\n",
       " 'AAGGTAATCCACATAG-1': 'lung macrophage',\n",
       " 'AAGGTAATCGTCGCTT-1': 'classical monocyte',\n",
       " 'AAGTACCAGAATGTTG-1': 'lung macrophage',\n",
       " 'AAGTACCAGCAGGTCA-1': 'neutrophil',\n",
       " 'AAGTACCAGCCGATCC-1': 'lung macrophage',\n",
       " 'AAGTACCAGGACGCTA-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'AAGTACCCAACGAGGT-1': 'B cell',\n",
       " 'AAGTACCCAATAGTCC-1': 'fibroblast of lung',\n",
       " 'AAGTACCCAATGCAGG-1': 'basophil',\n",
       " 'AAGTACCCACTACACA-1': 'lung macrophage',\n",
       " 'AAGTACCCATGTTCAG-1': 'lung macrophage',\n",
       " 'AAGTACCGTAGAGCTG-1': 'neutrophil',\n",
       " 'AAGTACCGTGCTCCGA-1': 'NK cell',\n",
       " 'AAGTACCGTTCCACAA-1': 'bronchial smooth muscle cell',\n",
       " 'AAGTACCTCAAGTCGT-1': 'regulatory T cell',\n",
       " 'AAGTACCTCACCGCTT-1': 'fibroblast of lung',\n",
       " 'AAGTACCTCACTGGGC-1': 'lung macrophage',\n",
       " 'AAGTACCTCGAGTACT-1': 'plasmacytoid dendritic cell',\n",
       " 'AAGTACCTCGTTACCC-1': 'lung macrophage',\n",
       " 'AAGTCGTAGCTGGCTC-1': 'lung macrophage',\n",
       " 'AAGTCGTAGCTTTCTT-1': 'lung macrophage',\n",
       " 'AAGTCGTAGGCAGGTT-1': 'vein endothelial cell',\n",
       " 'AAGTCGTAGGGCTAAC-1': 'lung macrophage',\n",
       " 'AAGTCGTAGTACTGTC-1': 'neutrophil',\n",
       " 'AAGTCGTCACAGGATG-1': 'plasmacytoid dendritic cell',\n",
       " 'AAGTCGTCAGGATGAC-1': 'T cell',\n",
       " 'AAGTCGTCATAGATCC-1': 'T cell',\n",
       " 'AAGTCGTCATCGCTAA-1': 'fibroblast of lung',\n",
       " 'AAGTCGTGTCACTGAT-1': 'neutrophil',\n",
       " 'AAGTCGTGTCGGCCTA-1': 'neutrophil',\n",
       " 'AAGTCGTGTGGCTGAA-1': 'adventitial cell',\n",
       " 'AAGTCGTTCCCTCGAT-1': 'basophil',\n",
       " 'AAGTCGTTCCGTAATG-1': 'bronchial smooth muscle cell',\n",
       " 'AAGTCGTTCCTAGCTC-1': 'lung macrophage',\n",
       " 'AAGTCGTTCGGAAACG-1': 'bronchial smooth muscle cell',\n",
       " 'AAGTCGTTCTCAATCT-1': 'lung macrophage',\n",
       " 'AAGTCGTTCTGGCCGA-1': 'lung macrophage',\n",
       " 'AAGTCGTTCTTCCGTG-1': 'lung macrophage',\n",
       " 'AAGTCGTTCTTTCGAT-1': 'bronchial smooth muscle cell',\n",
       " 'AAGTGAAAGGCTAGCA-1': 'neutrophil',\n",
       " 'AAGTGAAAGTGAATAC-1': 'lung macrophage',\n",
       " 'AAGTGAACAATCTAGC-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'AAGTGAACACAGTATC-1': 'classical monocyte',\n",
       " 'AAGTGAACAGGATTCT-1': 'regulatory T cell',\n",
       " 'AAGTGAACAGGTCAAG-1': 'lung macrophage',\n",
       " 'AAGTGAAGTAGTCTTG-1': 'T cell',\n",
       " 'AAGTGAAGTCTACTGA-1': 'non-classical monocyte',\n",
       " 'AAGTGAAGTCTGCCTT-1': 'vein endothelial cell',\n",
       " 'AAGTGAAGTGCATACT-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'AAGTGAATCCCTCATG-1': 'lung macrophage',\n",
       " 'AAGTGAATCGCTGCGA-1': 'lung macrophage',\n",
       " 'AAGTGAATCGTTCCTG-1': 'B cell',\n",
       " 'AAGTGAATCTAGCCAA-1': 'alveolar macrophage',\n",
       " 'AAGTGAATCTGCTTAT-1': 'vein endothelial cell',\n",
       " 'AAGTGAATCTTGATTC-1': 'lung macrophage',\n",
       " 'AAGTTCGAGACCTGGA-1': 'myeloid dendritic cell',\n",
       " 'AAGTTCGAGAGCACTG-1': 'myeloid dendritic cell',\n",
       " 'AAGTTCGAGGCCCACT-1': 'T cell',\n",
       " 'AAGTTCGCACAATGTC-1': 'lung macrophage',\n",
       " 'AAGTTCGGTAGCTGAG-1': 'B cell',\n",
       " 'AAGTTCGGTATGATCC-1': 'basophil',\n",
       " 'AAGTTCGGTGAATTGA-1': 'lung macrophage',\n",
       " 'AAGTTCGGTGTCTTCC-1': 'lung macrophage',\n",
       " 'AAGTTCGGTTGCTCCT-1': 'lung macrophage',\n",
       " 'AAGTTCGTCAGCTAGT-1': 'bronchial smooth muscle cell',\n",
       " 'AAGTTCGTCCAAACCA-1': 'basophil',\n",
       " 'AAGTTCGTCCTCTAGC-1': 'lung macrophage',\n",
       " 'AAGTTCGTCGACCCAG-1': 'fibroblast of lung',\n",
       " 'AATAGAGAGAACTTCC-1': 'bronchial smooth muscle cell',\n",
       " 'AATAGAGAGACCAAAT-1': 'mature NK T cell',\n",
       " 'AATAGAGAGCTAAATG-1': 'basophil',\n",
       " 'AATAGAGAGCTGAGCA-1': 'mature NK T cell',\n",
       " 'AATAGAGAGTACAGAT-1': 'neutrophil',\n",
       " 'AATAGAGCAAGAGGCT-1': 'lung macrophage',\n",
       " 'AATAGAGCAAGCGCTC-1': 'neutrophil',\n",
       " 'AATAGAGCATGCCGAC-1': 'regulatory T cell',\n",
       " 'AATAGAGCATTCTTCA-1': 'alveolar macrophage',\n",
       " 'AATAGAGGTAGACACG-1': 'mature NK T cell',\n",
       " 'AATAGAGGTCGCTTAA-1': 'myeloid dendritic cell',\n",
       " 'AATAGAGGTGTCTAAC-1': 'non-classical monocyte',\n",
       " 'AATAGAGTCAACCTCC-1': 'lung macrophage',\n",
       " 'AATAGAGTCACAATGC-1': 'myeloid dendritic cell',\n",
       " 'AATAGAGTCACTGCTC-1': 'NK cell',\n",
       " 'AATAGAGTCATGGATC-1': 'regulatory T cell',\n",
       " 'AATAGAGTCCTACCAC-1': 'basophil',\n",
       " 'AATAGAGTCGGCTGGT-1': 'lung macrophage',\n",
       " 'AATAGAGTCTGAATCG-1': 'classical monocyte',\n",
       " 'AATCACGAGAAATCCA-1': 'lung macrophage',\n",
       " 'AATCACGAGATTGACA-1': 'lung macrophage',\n",
       " 'AATCACGAGCCTCATA-1': 'intermediate monocyte',\n",
       " 'AATCACGAGGCACCAA-1': 'bronchial smooth muscle cell',\n",
       " 'AATCACGAGGGTACAC-1': 'lung macrophage',\n",
       " 'AATCACGAGGTAAAGG-1': 'mature NK T cell',\n",
       " 'AATCACGCAATGTCAC-1': 'lung macrophage',\n",
       " 'AATCACGCACATACTG-1': 'fibroblast of lung',\n",
       " 'AATCACGCACTGTCCT-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'AATCACGCATGACTCA-1': 'regulatory T cell',\n",
       " 'AATCACGGTAGGTGCA-1': 'vein endothelial cell',\n",
       " 'AATCACGGTCCAGGTC-1': 'lung macrophage',\n",
       " 'AATCACGGTTACGATC-1': 'T cell',\n",
       " 'AATCACGTCCAAACCA-1': 'bronchial smooth muscle cell',\n",
       " 'AATCACGTCGATACGT-1': 'bronchial smooth muscle cell',\n",
       " 'AATCGACCAAGTATAG-1': 'lung macrophage',\n",
       " 'AATCGACCACATGTTG-1': 'lung macrophage',\n",
       " 'AATCGACCAGCTACCG-1': 'non-classical monocyte',\n",
       " 'AATCGACCATGAAGGC-1': 'type II pneumocyte',\n",
       " 'AATCGACGTACTGACT-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'AATCGACGTCGCATCG-1': 'regulatory T cell',\n",
       " 'AATCGACGTTGACGGA-1': 'regulatory T cell',\n",
       " 'AATCGACTCCGGACTG-1': 'adventitial cell',\n",
       " 'AATCGTGAGACTCATC-1': 'regulatory T cell',\n",
       " 'AATCGTGAGTAACAGT-1': 'lung macrophage',\n",
       " 'AATCGTGAGTATTAGG-1': 'bronchial smooth muscle cell',\n",
       " 'AATCGTGAGTCGCTAT-1': 'non-classical monocyte',\n",
       " 'AATCGTGCAGACCCGT-1': 'lung macrophage',\n",
       " 'AATCGTGCATCATCTT-1': 'lung macrophage',\n",
       " 'AATCGTGGTCATGGCC-1': 'regulatory T cell',\n",
       " 'AATCGTGGTCCAGCGT-1': 'lung macrophage',\n",
       " 'AATCGTGGTCCTGAAT-1': 'regulatory T cell',\n",
       " 'AATCGTGGTTAATGAG-1': 'myeloid dendritic cell',\n",
       " 'AATCGTGTCATAAGGA-1': 'type II pneumocyte',\n",
       " 'AATCGTGTCATTCCTA-1': 'non-classical monocyte',\n",
       " 'AATCGTGTCGTCGCTT-1': 'lung macrophage',\n",
       " 'AATCGTGTCTCAACGA-1': 'myeloid dendritic cell',\n",
       " 'AATCGTGTCTTCGTGC-1': 'mature NK T cell',\n",
       " 'AATGAAGAGAGTGAAG-1': 'plasma cell',\n",
       " 'AATGAAGAGGATGGCT-1': 'bronchial smooth muscle cell',\n",
       " 'AATGAAGAGTCATACC-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'AATGAAGCAAGGCTTT-1': 'type II pneumocyte',\n",
       " 'AATGAAGCATCGGCCA-1': 'lung macrophage',\n",
       " 'AATGAAGGTAGTCTTG-1': 'T cell',\n",
       " 'AATGAAGGTATGGAGC-1': 'basophil',\n",
       " 'AATGAAGGTCCAATCA-1': 'bronchial smooth muscle cell',\n",
       " 'AATGAAGGTGGAGGTT-1': 'B cell',\n",
       " 'AATGAAGTCAGCGCGT-1': 'alveolar macrophage',\n",
       " 'AATGAAGTCCCGGTAG-1': 'mature NK T cell',\n",
       " 'AATGAAGTCGGCGATC-1': 'basophil',\n",
       " 'AATGAAGTCGTAGGGA-1': 'vein endothelial cell',\n",
       " 'AATGACCAGCTTTCCC-1': 'NK cell',\n",
       " 'AATGACCAGTGAGTTA-1': 'mature NK T cell',\n",
       " 'AATGACCCATACAGGG-1': 'B cell',\n",
       " 'AATGACCTCAGGGATG-1': 'fibroblast of lung',\n",
       " 'AATGACCTCGAAACAA-1': 'T cell',\n",
       " 'AATGCCAAGATAACAC-1': 'classical monocyte',\n",
       " 'AATGCCAAGCCTCAGC-1': 'mature NK T cell',\n",
       " 'AATGCCAAGCGAGTAC-1': 'bronchial smooth muscle cell',\n",
       " 'AATGCCAAGTCTACCA-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'AATGCCAAGTGCTAGG-1': 'lung macrophage',\n",
       " 'AATGCCACACGTAGTT-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'AATGCCACATGAGTAA-1': 'lung macrophage',\n",
       " 'AATGCCAGTAGAGTTA-1': 'mature NK T cell',\n",
       " 'AATGCCAGTCTCCTGT-1': 'B cell',\n",
       " 'AATGCCATCCTTGAAG-1': 'basophil',\n",
       " 'AATGCCATCGAAGAAT-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'AATGCCATCTACGGTA-1': 'B cell',\n",
       " 'AATGGAAAGGCAGGGA-1': 'B cell',\n",
       " 'AATGGAAAGGTTGAGC-1': 'myeloid dendritic cell',\n",
       " 'AATGGAACAACTGATC-1': 'lung macrophage',\n",
       " 'AATGGAACAAGCGCAA-1': 'mature NK T cell',\n",
       " 'AATGGAACAGTAGTTC-1': 'lung macrophage',\n",
       " 'AATGGAAGTGACGTCC-1': 'lung macrophage',\n",
       " 'AATGGAAGTGTAGGAC-1': 'lung macrophage',\n",
       " 'AATGGAAGTGTATTGC-1': 'lung macrophage',\n",
       " 'AATGGAATCAAAGGAT-1': 'neutrophil',\n",
       " 'AATGGAATCACCCTCA-1': 'T cell',\n",
       " 'AATGGAATCACTCACC-1': 'neutrophil',\n",
       " 'AATGGAATCATTCTTG-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'AATGGAATCGAGATAA-1': 'lung macrophage',\n",
       " 'AATGGAATCGCAGATT-1': 'NK cell',\n",
       " 'AATGGCTAGTGCAGGT-1': 'lung macrophage',\n",
       " 'AATGGCTCAGTTGTCA-1': 'lung macrophage',\n",
       " 'AATGGCTGTAAGATTG-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'AATGGCTGTACCCGAC-1': 'neutrophil',\n",
       " 'AATGGCTGTACGCTTA-1': 'mature NK T cell',\n",
       " 'AATGGCTGTCACTGAT-1': 'lung macrophage',\n",
       " 'AATGGCTGTCGTTATG-1': 'basophil',\n",
       " 'AATGGCTGTGCGTCGT-1': 'bronchial smooth muscle cell',\n",
       " 'AATGGCTGTTCCTAAG-1': 'NK cell',\n",
       " 'AATGGCTTCATGAGTC-1': 'B cell',\n",
       " 'AATGGCTTCCCATTTA-1': 'pericyte cell',\n",
       " 'AATGGCTTCTAAACGC-1': 'classical monocyte',\n",
       " 'AATGGCTTCTCCTGTG-1': 'T cell',\n",
       " 'AATGGCTTCTGGGAGA-1': 'classical monocyte',\n",
       " 'AATTCCTAGCCATTGT-1': 'lung macrophage',\n",
       " 'AATTCCTCAATGAACA-1': 'bronchial smooth muscle cell',\n",
       " 'AATTCCTCAGAACGCA-1': 'lung macrophage',\n",
       " 'AATTCCTCATAAGCGG-1': 'myeloid dendritic cell',\n",
       " 'AATTCCTCATCCGAGC-1': 'basophil',\n",
       " 'AATTCCTCATGAATAG-1': 'basophil',\n",
       " 'AATTCCTCATGGCTGC-1': 'T cell',\n",
       " 'AATTCCTGTCATCGCG-1': 'type II pneumocyte',\n",
       " 'AATTCCTGTCTACGAT-1': 'neutrophil',\n",
       " 'AATTCCTGTGGCTAGA-1': 'basophil',\n",
       " 'AATTCCTGTTTCCAAG-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'AATTCCTTCGTTCTAT-1': 'fibroblast of lung',\n",
       " 'AATTCCTTCTGTCAGA-1': 'T cell',\n",
       " 'AATTTCCAGAAGTCAT-1': 'lung macrophage',\n",
       " 'AATTTCCAGCAAGTGC-1': 'fibroblast of lung',\n",
       " 'AATTTCCAGGTCATAA-1': 'lung macrophage',\n",
       " 'AATTTCCAGTAGCCAG-1': 'lung macrophage',\n",
       " 'AATTTCCCATCAGCGC-1': 'B cell',\n",
       " 'AATTTCCCATCGTCCT-1': 'regulatory T cell',\n",
       " 'AATTTCCGTACGTAGG-1': 'mature NK T cell',\n",
       " 'AATTTCCGTAGTCGTT-1': 'type II pneumocyte',\n",
       " 'AATTTCCGTGAGGCAT-1': 'classical monocyte',\n",
       " 'AATTTCCGTGATTAGA-1': 'lung macrophage',\n",
       " 'AATTTCCTCAAAGGTA-1': 'classical monocyte',\n",
       " 'AATTTCCTCGCTGATA-1': 'plasmacytoid dendritic cell',\n",
       " 'AATTTCCTCTTGGAAC-1': 'neutrophil',\n",
       " 'ACAAAGAAGCAAATCA-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'ACAAAGAAGGCAGGGA-1': 'neutrophil',\n",
       " 'ACAAAGAAGTTGCTGT-1': 'basophil',\n",
       " 'ACAAAGACAAAGGATT-1': 'fibroblast of lung',\n",
       " 'ACAAAGACAGGATCTT-1': 'lung macrophage',\n",
       " 'ACAAAGACAGGCATGA-1': 'NK cell',\n",
       " 'ACAAAGACAGTCAGAG-1': 'lung macrophage',\n",
       " 'ACAAAGACATCCGTGG-1': 'lung macrophage',\n",
       " 'ACAAAGATCAAGTGGG-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'ACAAAGATCGTCGCTT-1': 'bronchial smooth muscle cell',\n",
       " 'ACAACCAAGGATGTTA-1': 'neutrophil',\n",
       " 'ACAACCACATGCACTA-1': 'basophil',\n",
       " 'ACAACCAGTAGCACAG-1': 'non-classical monocyte',\n",
       " 'ACAACCAGTATCGATC-1': 'lung macrophage',\n",
       " 'ACAACCAGTGTCGATT-1': 'B cell',\n",
       " 'ACAACCAGTTACCTTT-1': 'lung macrophage',\n",
       " 'ACAACCATCTAGTGTG-1': 'lung macrophage',\n",
       " 'ACAAGCTAGGCCGCTT-1': 'lung macrophage',\n",
       " 'ACAAGCTAGGTAAACT-1': 'neutrophil',\n",
       " 'ACAAGCTAGGTACATA-1': 'bronchial smooth muscle cell',\n",
       " 'ACAAGCTAGGTTGAGC-1': 'intermediate monocyte',\n",
       " 'ACAAGCTAGTACCGGA-1': 'classical monocyte',\n",
       " 'ACAAGCTAGTTCCGGC-1': 'regulatory T cell',\n",
       " 'ACAAGCTCAAGCTCTA-1': 'lung macrophage',\n",
       " 'ACAAGCTCACATTGTG-1': 'bronchial smooth muscle cell',\n",
       " 'ACAAGCTCAGACACAG-1': 'neutrophil',\n",
       " 'ACAAGCTCAGACGCTC-1': 'lung macrophage',\n",
       " 'ACAAGCTCAGGTGTGA-1': 'classical monocyte',\n",
       " 'ACAAGCTGTCGGAAAC-1': 'mature NK T cell',\n",
       " 'ACAAGCTGTGTCCAAT-1': 'T cell',\n",
       " 'ACAAGCTGTTAGTCGT-1': 'classical monocyte',\n",
       " 'ACAAGCTGTTATCTGG-1': 'neutrophil',\n",
       " 'ACAAGCTGTTTAGTCG-1': 'basophil',\n",
       " 'ACAAGCTTCCATTCGC-1': 'non-classical monocyte',\n",
       " 'ACAAGCTTCCGATAAC-1': 'bronchial smooth muscle cell',\n",
       " 'ACAAGCTTCGTCTACC-1': 'B cell',\n",
       " 'ACAAGCTTCTGCTGAA-1': 'classical monocyte',\n",
       " 'ACACAGTAGCATCGAG-1': 'T cell',\n",
       " 'ACACAGTAGTCCGCGT-1': 'classical monocyte',\n",
       " 'ACACAGTAGTCTGGAG-1': 'bronchial smooth muscle cell',\n",
       " 'ACACAGTCACAGCTTA-1': 'T cell',\n",
       " 'ACACAGTCAGCACGAA-1': 'classical monocyte',\n",
       " 'ACACAGTCAGCAGTAG-1': 'lung macrophage',\n",
       " 'ACACAGTCATATCTGG-1': 'lung macrophage',\n",
       " 'ACACAGTCATCCTCAC-1': 'intermediate monocyte',\n",
       " 'ACACAGTCATCGTTCC-1': 'lung macrophage',\n",
       " 'ACACAGTCATCTTAGG-1': 'regulatory T cell',\n",
       " 'ACACAGTCATGTTTGG-1': 'B cell',\n",
       " 'ACACAGTGTACTCCCT-1': 'lung macrophage',\n",
       " 'ACACAGTGTACTCGAT-1': 'NK cell',\n",
       " 'ACACAGTGTGGCATCC-1': 'basophil',\n",
       " 'ACACAGTGTTATAGCC-1': 'lung macrophage',\n",
       " 'ACACAGTTCCTACTGC-1': 'NK cell',\n",
       " 'ACACCAAAGACCCGCT-1': 'T cell',\n",
       " 'ACACCAAAGAGCCGTA-1': 'intermediate monocyte',\n",
       " 'ACACCAAAGCATTGAA-1': 'NK cell',\n",
       " 'ACACCAAAGCGGTAAC-1': 'bronchial smooth muscle cell',\n",
       " 'ACACCAAAGGAAGTGA-1': 'B cell',\n",
       " 'ACACCAACAAAGCTAA-1': 'mature NK T cell',\n",
       " 'ACACCAACAAAGGATT-1': 'NK cell',\n",
       " 'ACACCAACAAGGGCAT-1': 'basophil',\n",
       " 'ACACCAAGTCGTCATA-1': 'NK cell',\n",
       " 'ACACCAAGTTTACCTT-1': 'T cell',\n",
       " 'ACACCAATCACGGACC-1': 'neutrophil',\n",
       " 'ACACCAATCCTCTTTC-1': 'bronchial smooth muscle cell',\n",
       " 'ACACCAATCTTCACAT-1': 'B cell',\n",
       " 'ACACCAATCTTGAACG-1': 'fibroblast of lung',\n",
       " 'ACACCAATCTTTGCTA-1': 'lung macrophage',\n",
       " 'ACACGCGAGCCATATC-1': 'mature NK T cell',\n",
       " 'ACACGCGAGGAACGAA-1': 'lung macrophage',\n",
       " 'ACACGCGAGGGTAGCT-1': 'B cell',\n",
       " 'ACACGCGAGGTCCCGT-1': 'neutrophil',\n",
       " 'ACACGCGAGTCGGCAA-1': 'vein endothelial cell',\n",
       " 'ACACGCGCAAGTCATC-1': 'T cell',\n",
       " 'ACACGCGCAATACCTG-1': 'myeloid dendritic cell',\n",
       " 'ACACGCGCAGCATACT-1': 'lung macrophage',\n",
       " 'ACACGCGCAGTTCCAA-1': 'bronchial smooth muscle cell',\n",
       " 'ACACGCGCATGTGGTT-1': 'mature NK T cell',\n",
       " 'ACACGCGTCACTACTT-1': 'bronchial smooth muscle cell',\n",
       " 'ACACGCGTCGGTTGTA-1': 'non-classical monocyte',\n",
       " 'ACACTGAAGCTCGTGC-1': 'myeloid dendritic cell',\n",
       " 'ACACTGAAGTATGGAT-1': 'lung macrophage',\n",
       " 'ACACTGAAGTGATGGC-1': 'myeloid dendritic cell',\n",
       " 'ACACTGACAAGTCATC-1': 'myeloid dendritic cell',\n",
       " 'ACACTGACAGCCTACG-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'ACACTGACAGCTGTAT-1': 'B cell',\n",
       " 'ACACTGAGTCTCGGGT-1': 'neutrophil',\n",
       " 'ACACTGAGTGTCTAAC-1': 'neutrophil',\n",
       " 'ACACTGATCGTAGCTA-1': 'vein endothelial cell',\n",
       " 'ACAGAAAAGAGGACTC-1': 'lung macrophage',\n",
       " 'ACAGAAACAAATCAGA-1': 'lung macrophage',\n",
       " 'ACAGAAACACTCTGCT-1': 'mature NK T cell',\n",
       " 'ACAGAAACATGTGACT-1': 'mature NK T cell',\n",
       " 'ACAGAAAGTAGTGATA-1': 'bronchial smooth muscle cell',\n",
       " 'ACAGAAAGTCAGACTT-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'ACAGAAAGTCCACATA-1': 'T cell',\n",
       " 'ACAGAAAGTCCCAAAT-1': 'lung macrophage',\n",
       " 'ACAGAAAGTTCAATCG-1': 'NK cell',\n",
       " 'ACAGAAATCGTGCTCT-1': 'lung macrophage',\n",
       " 'ACAGAAATCTATTTCG-1': 'mature NK T cell',\n",
       " 'ACAGCCGAGCTACGTT-1': 'T cell',\n",
       " 'ACAGCCGAGGTACTGG-1': 'basophil',\n",
       " 'ACAGCCGCAAATGCGG-1': 'bronchial smooth muscle cell',\n",
       " 'ACAGCCGCACCTATCC-1': 'T cell',\n",
       " 'ACAGCCGGTCACTAGT-1': 'lung macrophage',\n",
       " 'ACAGCCGGTCGGTGTC-1': 'lung macrophage',\n",
       " 'ACAGCCGGTCGTCAGC-1': 'T cell',\n",
       " 'ACAGCCGGTTGAGGAC-1': 'myeloid dendritic cell',\n",
       " 'ACAGCCGTCAACCCGG-1': 'basophil',\n",
       " 'ACAGCCGTCCATGCAA-1': 'basophil',\n",
       " 'ACAGCCGTCCGCATAA-1': 'fibroblast of lung',\n",
       " 'ACAGGGAAGGTGAGCT-1': 'alveolar macrophage',\n",
       " 'ACAGGGAAGTTACGTC-1': 'lung macrophage',\n",
       " 'ACAGGGACAACCCTAA-1': 'neutrophil',\n",
       " 'ACAGGGACACGATTCA-1': 'basophil',\n",
       " 'ACAGGGACATATACCG-1': 'T cell',\n",
       " 'ACAGGGACATGGAAGC-1': 'endothelial cell of lymphatic vessel',\n",
       " 'ACAGGGAGTAACGCGA-1': 'basophil',\n",
       " 'ACAGGGAGTAAGCGGT-1': 'lung macrophage',\n",
       " 'ACAGGGAGTCATCGGC-1': 'neutrophil',\n",
       " 'ACAGGGAGTTTCGTTT-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'ACAGGGATCGCTCATC-1': 'classical monocyte',\n",
       " 'ACATCCCAGACATCAA-1': 'bronchial smooth muscle cell',\n",
       " 'ACATCCCAGGTAAAGG-1': 'lung macrophage',\n",
       " 'ACATCCCAGTCGCGAA-1': 'lung macrophage',\n",
       " 'ACATCCCCAAACGGCA-1': 'mature NK T cell',\n",
       " 'ACATCCCCAAGAAACT-1': 'NK cell',\n",
       " 'ACATCCCCACCTTCGT-1': 'lung macrophage',\n",
       " 'ACATCCCCAGAAGTGC-1': 'lung macrophage',\n",
       " 'ACATCCCCATCCAACA-1': 'lung macrophage',\n",
       " 'ACATCCCTCGAACACT-1': 'basophil',\n",
       " 'ACATCCCTCGACACTA-1': 'intermediate monocyte',\n",
       " 'ACATCCCTCGATACAC-1': 'lung macrophage',\n",
       " 'ACATCGAAGGTTATAG-1': 'neutrophil',\n",
       " 'ACATCGAAGTACCGGA-1': 'bronchial smooth muscle cell',\n",
       " 'ACATCGACAACACAAA-1': 'basophil',\n",
       " 'ACATCGAGTACGCGTC-1': 'T cell',\n",
       " 'ACATCGAGTACTTGTG-1': 'NK cell',\n",
       " 'ACATCGAGTCAACCTA-1': 'lung macrophage',\n",
       " 'ACATCGAGTTCCTAAG-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'ACATCGATCGATAACC-1': 'B cell',\n",
       " 'ACATGCAAGACATCCT-1': 'lung macrophage',\n",
       " 'ACATGCAAGGGAGAAT-1': 'basophil',\n",
       " 'ACATGCAAGTCGAGGT-1': 'lung macrophage',\n",
       " 'ACATGCAAGTGCACAG-1': 'T cell',\n",
       " 'ACATGCAGTCTAACTG-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'ACATGCAGTTCCGCAG-1': 'myeloid dendritic cell',\n",
       " 'ACATGCATCAATCTTC-1': 'alveolar macrophage',\n",
       " 'ACATGCATCCTCTCTT-1': 'basophil',\n",
       " 'ACATGCATCTATACGG-1': 'B cell',\n",
       " 'ACATTTCCAAGAAATC-1': 'T cell',\n",
       " 'ACATTTCCAATACAGA-1': 'lung macrophage',\n",
       " 'ACATTTCCACAGAGAC-1': 'B cell',\n",
       " 'ACATTTCCATGATCTG-1': 'fibroblast of lung',\n",
       " 'ACATTTCGTGATCATC-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'ACATTTCGTGCCTTCT-1': 'classical monocyte',\n",
       " 'ACATTTCGTTCGGTAT-1': 'lung macrophage',\n",
       " 'ACATTTCTCATTTGGG-1': 'type II pneumocyte',\n",
       " 'ACATTTCTCCACTTTA-1': 'T cell',\n",
       " 'ACATTTCTCCGTGGGT-1': 'T cell',\n",
       " 'ACATTTCTCCTAGAGT-1': 'neutrophil',\n",
       " 'ACATTTCTCCTGGCTT-1': 'T cell',\n",
       " 'ACATTTCTCTGCACCT-1': 'mature NK T cell',\n",
       " 'ACCAAACCAGACAAAT-1': 'T cell',\n",
       " 'ACCAAACCAGTGGTGA-1': 'mature NK T cell',\n",
       " 'ACCAAACCATAGGTAA-1': 'lung macrophage',\n",
       " 'ACCAAACGTAAGTTAG-1': 'lung macrophage',\n",
       " 'ACCAAACGTAGGACCA-1': 'NK cell',\n",
       " 'ACCAAACGTCCACACG-1': 'lung macrophage',\n",
       " 'ACCAAACTCACGGGCT-1': 'T cell',\n",
       " 'ACCAAACTCTCGACGG-1': 'lung macrophage',\n",
       " 'ACCAAACTCTGAGCAT-1': 'regulatory T cell',\n",
       " 'ACCAACAAGACGAAGA-1': 'lung macrophage',\n",
       " 'ACCAACAAGATAACAC-1': 'lung macrophage',\n",
       " 'ACCAACAAGCAATAGT-1': 'lung macrophage',\n",
       " 'ACCAACAAGGCAGTCA-1': 'intermediate monocyte',\n",
       " 'ACCAACAAGTAGGGTC-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'ACCAACACACATACGT-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'ACCAACACACCCTTGT-1': 'bronchial smooth muscle cell',\n",
       " 'ACCAACACACGTACAT-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'ACCAACACACTGGAAG-1': 'lung macrophage',\n",
       " 'ACCAACACACTGGCCA-1': 'lung macrophage',\n",
       " 'ACCAACACACTTTATC-1': 'lung macrophage',\n",
       " 'ACCAACAGTCGGCACT-1': 'NK cell',\n",
       " 'ACCAACAGTGCACATT-1': 'mature NK T cell',\n",
       " 'ACCAACAGTGTGCCTG-1': 'mature NK T cell',\n",
       " 'ACCACAAAGAACGCGT-1': 'lung macrophage',\n",
       " 'ACCACAAAGAACTCCT-1': 'B cell',\n",
       " 'ACCACAAAGAGAGCCT-1': 'regulatory T cell',\n",
       " 'ACCACAAAGCGACATG-1': 'regulatory T cell',\n",
       " 'ACCACAAAGCTGGAGT-1': 'T cell',\n",
       " 'ACCACAAAGTCACTCA-1': 'lung macrophage',\n",
       " 'ACCACAACACCATATG-1': 'regulatory T cell',\n",
       " 'ACCACAACATCAGCGC-1': 'T cell',\n",
       " 'ACCACAACATGATAGA-1': 'T cell',\n",
       " 'ACCACAAGTCGCATTA-1': 'B cell',\n",
       " 'ACCACAAGTTATTCCT-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'ACCACAAGTTCCGTTC-1': 'mature NK T cell',\n",
       " 'ACCACAATCCAGGACC-1': 'classical monocyte',\n",
       " 'ACCACAATCCCGTGTT-1': 'myeloid dendritic cell',\n",
       " 'ACCACAATCGTCACCT-1': 'T cell',\n",
       " 'ACCACAATCTTAATCC-1': 'lung macrophage',\n",
       " 'ACCATTTAGATTTGCC-1': 'lung macrophage',\n",
       " 'ACCATTTAGGCATCAG-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'ACCATTTAGTACGTCT-1': 'non-classical monocyte',\n",
       " 'ACCATTTCAACGATTC-1': 'B cell',\n",
       " 'ACCATTTCAATGAGCG-1': 'lung macrophage',\n",
       " 'ACCATTTCAGCAGATG-1': 'B cell',\n",
       " 'ACCATTTTCCACGTCT-1': 'lung macrophage',\n",
       " 'ACCATTTTCGGACTTA-1': 'classical monocyte',\n",
       " 'ACCATTTTCTACGGTA-1': 'regulatory T cell',\n",
       " 'ACCATTTTCTTACGTT-1': 'lung macrophage',\n",
       " 'ACCCAAAAGGCACGAT-1': 'mature NK T cell',\n",
       " 'ACCCAAAAGGTTGACG-1': 'B cell',\n",
       " 'ACCCAAAAGTAGTGCG-1': 'B cell',\n",
       " 'ACCCAAACAACAAGTA-1': 'adventitial cell',\n",
       " 'ACCCAAACAAGACAAT-1': 'type II pneumocyte',\n",
       " 'ACCCAAACAATCGCCG-1': 'lung macrophage',\n",
       " 'ACCCAAACACCGTACG-1': 'T cell',\n",
       " 'ACCCAAACAGACGGAT-1': 'lung macrophage',\n",
       " 'ACCCAAAGTGGCTAGA-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'ACCCAAATCAACCGAT-1': 'lung macrophage',\n",
       " 'ACCCAAATCAGCAATC-1': 'classical monocyte',\n",
       " 'ACCCAAATCCACTTCG-1': 'B cell',\n",
       " 'ACCCAAATCCATGCAA-1': 'lung macrophage',\n",
       " 'ACCCAAATCCTTGAAG-1': 'lung macrophage',\n",
       " 'ACCCAAATCGGATAAA-1': 'NK cell',\n",
       " 'ACCCAAATCTGTAAGC-1': 'neutrophil',\n",
       " 'ACCCTCAAGAGGGTAA-1': 'T cell',\n",
       " 'ACCCTCAAGGTTGACG-1': 'bronchial smooth muscle cell',\n",
       " 'ACCCTCACAAGTCCCG-1': 'T cell',\n",
       " 'ACCCTCACACACAGAG-1': 'lung macrophage',\n",
       " 'ACCCTCACAGGTTCCG-1': 'bronchial smooth muscle cell',\n",
       " 'ACCCTCACATGCGGTC-1': 'basophil',\n",
       " 'ACCCTCAGTAACTGCT-1': 'B cell',\n",
       " 'ACCCTCAGTAGCGTCC-1': 'lung macrophage',\n",
       " 'ACCCTCAGTCAACCAT-1': 'classical monocyte',\n",
       " 'ACCCTCAGTCATATGC-1': 'myeloid dendritic cell',\n",
       " 'ACCCTCAGTTGAAGTA-1': 'lung macrophage',\n",
       " 'ACCCTCATCAAAGGTA-1': 'regulatory T cell',\n",
       " 'ACCCTCATCAAGCGTT-1': 'basophil',\n",
       " 'ACCCTCATCGAGAACG-1': 'basophil',\n",
       " 'ACCCTCATCGCGAAGA-1': 'B cell',\n",
       " 'ACCCTTGAGCAACCAG-1': 'type II pneumocyte',\n",
       " 'ACCCTTGAGCGAACTG-1': 'basophil',\n",
       " 'ACCCTTGCAGTTACCA-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'ACCCTTGCATTCGGGC-1': 'regulatory T cell',\n",
       " 'ACCCTTGGTAAGTTAG-1': 'lung macrophage',\n",
       " 'ACCCTTGGTGTCACAT-1': 'B cell',\n",
       " 'ACCCTTGGTTCAAAGA-1': 'lung macrophage',\n",
       " 'ACCCTTGGTTGGGTTT-1': 'NK cell',\n",
       " 'ACCCTTGTCAAATGCC-1': 'lung macrophage',\n",
       " 'ACCCTTGTCAACCCGG-1': 'lung macrophage',\n",
       " 'ACCCTTGTCGCCTATC-1': 'myeloid dendritic cell',\n",
       " 'ACCCTTGTCGCTTTAT-1': 'basophil',\n",
       " 'ACCGTTCAGACAGCTG-1': 'lung macrophage',\n",
       " 'ACCGTTCAGGGACTGT-1': 'T cell',\n",
       " 'ACCGTTCAGTACGTCT-1': 'mature NK T cell',\n",
       " 'ACCGTTCAGTATGGAT-1': 'basophil',\n",
       " 'ACCGTTCAGTGGAAGA-1': 'type II pneumocyte',\n",
       " 'ACCGTTCAGTTGTCAC-1': 'lung macrophage',\n",
       " 'ACCGTTCCAATACCCA-1': 'regulatory T cell',\n",
       " 'ACCGTTCCAATGGCAG-1': 'basophil',\n",
       " 'ACCGTTCCACCAGCGT-1': 'lung macrophage',\n",
       " 'ACCGTTCCACTAGAGG-1': 'intermediate monocyte',\n",
       " 'ACCGTTCCAGCCGGTT-1': 'bronchial smooth muscle cell',\n",
       " 'ACCGTTCCAGTAACGG-1': 'lung macrophage',\n",
       " 'ACCGTTCGTAAGTCAA-1': 'classical monocyte',\n",
       " 'ACCGTTCGTCCGACGT-1': 'intermediate monocyte',\n",
       " 'ACCGTTCGTTTGGAAA-1': 'neutrophil',\n",
       " 'ACCGTTCTCCACACCT-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'ACCGTTCTCCCGAAAT-1': 'B cell',\n",
       " 'ACCGTTCTCGCTGATA-1': 'lung macrophage',\n",
       " 'ACCGTTCTCGTTCGCT-1': 'lung macrophage',\n",
       " 'ACCTACCAGGATATAC-1': 'intermediate monocyte',\n",
       " 'ACCTACCAGGCTAACG-1': 'T cell',\n",
       " 'ACCTACCAGGCTTAAA-1': 'myeloid dendritic cell',\n",
       " 'ACCTACCAGTGCGTCC-1': 'mature NK T cell',\n",
       " 'ACCTACCCAACTGGTT-1': 'basophil',\n",
       " 'ACCTACCCAAGCCTGC-1': 'mature NK T cell',\n",
       " 'ACCTACCCACGGAAGT-1': 'B cell',\n",
       " 'ACCTACCCAGCTATTG-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'ACCTACCCATCGTTCC-1': 'lung macrophage',\n",
       " 'ACCTACCGTATAGGGC-1': 'regulatory T cell',\n",
       " 'ACCTACCGTCAAGTTC-1': 'lung macrophage',\n",
       " 'ACCTACCGTCATCCGG-1': 'NK cell',\n",
       " 'ACCTACCGTTCCAGGC-1': 'intermediate monocyte',\n",
       " 'ACCTACCTCCAGTGCG-1': 'mature NK T cell',\n",
       " 'ACCTACCTCGGAAACG-1': 'intermediate monocyte',\n",
       " 'ACCTACCTCTGCCCTA-1': 'vein endothelial cell',\n",
       " 'ACCTGAAAGAGCGACT-1': 'lung macrophage',\n",
       " 'ACCTGAAAGCACCCAC-1': 'regulatory T cell',\n",
       " 'ACCTGAAAGCATTGTC-1': 'pericyte cell',\n",
       " 'ACCTGAAAGCCTCTTC-1': 'basophil',\n",
       " 'ACCTGAAAGGCCTGAA-1': 'basophil',\n",
       " 'ACCTGAAAGTCAGGGT-1': 'T cell',\n",
       " 'ACCTGAACAACCTATG-1': 'lung macrophage',\n",
       " 'ACCTGAACACGTACTA-1': 'lung macrophage',\n",
       " 'ACCTGAACAGCGCGTT-1': 'B cell',\n",
       " 'ACCTGAACATAATGAG-1': 'bronchial smooth muscle cell',\n",
       " 'ACCTGAACATAGGTTC-1': 'B cell',\n",
       " 'ACCTGAAGTAGATCGG-1': 'lung macrophage',\n",
       " 'ACCTGAATCACGGGCT-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'ACCTGAATCATCCTGC-1': 'basophil',\n",
       " 'ACCTGAATCGATGGAG-1': 'myeloid dendritic cell',\n",
       " 'ACCTGAATCTGTCCGT-1': 'mature NK T cell',\n",
       " 'ACCTGAATCTTCCCGA-1': 'T cell',\n",
       " 'ACCTGAATCTTCTGGC-1': 'plasmacytoid dendritic cell',\n",
       " 'ACCTGTCAGAACCCGA-1': 'neutrophil',\n",
       " 'ACCTGTCAGCCATATC-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'ACCTGTCAGCTACAAA-1': 'plasma cell',\n",
       " 'ACCTGTCAGGATACGC-1': 'smooth muscle cell of the pulmonary artery',\n",
       " 'ACCTGTCAGGTCTGGA-1': 'bronchial smooth muscle cell',\n",
       " 'ACCTGTCCAACGTATC-1': 'neutrophil',\n",
       " 'ACCTGTCCACCCTCTA-1': 'T cell',\n",
       " 'ACCTGTCCAGGATGAC-1': 'basophil',\n",
       " 'ACCTGTCCATCATTTC-1': 'mature NK T cell',\n",
       " 'ACCTGTCGTATGAGAT-1': 'basophil',\n",
       " 'ACCTGTCGTATGTCTG-1': 'lung macrophage',\n",
       " 'ACCTGTCGTGCTCTTC-1': 'alveolar macrophage',\n",
       " 'ACCTGTCGTGTGATGG-1': 'lung macrophage',\n",
       " 'ACCTGTCTCCGAGATT-1': 'classical monocyte',\n",
       " 'ACCTGTCTCCTTCTAA-1': 'lung macrophage',\n",
       " 'ACCTGTCTCGCCTATC-1': 'basophil',\n",
       " 'ACCTGTCTCTCCAATT-1': 'T cell',\n",
       " 'ACGATCAAGAACTCCT-1': 'non-classical monocyte',\n",
       " 'ACGATCAAGGACCCAA-1': 'lung macrophage',\n",
       " 'ACGATCAAGTCTGGTT-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'ACGATCACAGTGACCC-1': 'basophil',\n",
       " 'ACGATCACATCGTCCT-1': 'regulatory T cell',\n",
       " 'ACGATCAGTACCACGC-1': 'B cell',\n",
       " 'ACGATCAGTAGGACCA-1': 'B cell',\n",
       " 'ACGATCAGTCAAAGCG-1': 'myeloid dendritic cell',\n",
       " 'ACGATCAGTCACCGCA-1': 'CD8-positive, alpha-beta T cell',\n",
       " 'ACGATCAGTTCAAGTC-1': 'lung macrophage',\n",
       " 'ACGATCAGTTGCATCA-1': 'bronchial smooth muscle cell',\n",
       " 'ACGATCATCCAGTTCC-1': 'neutrophil',\n",
       " 'ACGATCATCTGCGGAC-1': 'lung macrophage',\n",
       " 'ACGATGTAGAGATTCA-1': 'CD4-positive, alpha-beta T cell',\n",
       " 'ACGATGTAGCAATTCC-1': 'lung macrophage',\n",
       " 'ACGATGTAGCCTCAGC-1': 'neutrophil',\n",
       " 'ACGATGTAGTATGATG-1': 'lung macrophage',\n",
       " 'ACGATGTCATCGGATT-1': 'bronchial smooth muscle cell',\n",
       " 'ACGATGTGTACTGACT-1': 'non-classical monocyte',\n",
       " 'ACGATGTGTGAGCTCC-1': 'lung macrophage',\n",
       " 'ACGATGTGTTCCGGTG-1': 'lung macrophage',\n",
       " 'ACGATGTGTTGACTAC-1': 'lung macrophage',\n",
       " 'ACGATGTTCACACGAT-1': 'lung macrophage',\n",
       " 'ACGATGTTCACTGTCC-1': 'lung macrophage',\n",
       " 'ACGATGTTCCGTTGAA-1': 'fibroblast of lung',\n",
       " 'ACGCACGAGACCTCAT-1': 'lung macrophage',\n",
       " 'ACGCACGAGAGCACTG-1': 'neutrophil',\n",
       " 'ACGCACGAGCACTCTA-1': 'T cell',\n",
       " 'ACGCACGAGCCGATCC-1': 'lung macrophage',\n",
       " 'ACGCACGAGTGGACTG-1': 'lung macrophage',\n",
       " 'ACGCACGAGTTATGGA-1': 'basophil',\n",
       " 'ACGCACGAGTTTGTCG-1': 'lung macrophage',\n",
       " 'ACGCACGCAATCGAAA-1': 'myeloid dendritic cell',\n",
       " 'ACGCACGCACAGAAGC-1': 'neutrophil',\n",
       " 'ACGCACGCAGGTGGAT-1': 'regulatory T cell',\n",
       " 'ACGCACGCATGGGTTT-1': 'regulatory T cell',\n",
       " 'ACGCACGGTAACGTTC-1': 'neutrophil',\n",
       " 'ACGCACGGTAGCGCCT-1': 'lung macrophage',\n",
       " 'ACGCACGGTCGATTTG-1': 'bronchial smooth muscle cell',\n",
       " 'ACGCACGGTGCAATGG-1': 'bronchial smooth muscle cell',\n",
       " 'ACGCACGGTGGTCCGT-1': 'myeloid dendritic cell',\n",
       " 'ACGCACGGTTTGGGAG-1': 'mature NK T cell',\n",
       " 'ACGCACGTCACGTAGT-1': 'fibroblast of lung',\n",
       " 'ACGCACGTCCTAGCTC-1': 'NK cell',\n",
       " 'ACGCACGTCGCCAATA-1': 'pulmonary interstitial fibroblast',\n",
       " 'ACGGAAGAGATTACCC-1': 'regulatory T cell',\n",
       " ...}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_mapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "70b4ce12",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jrlab2019/miniconda3/envs/sc/lib/python3.9/site-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "  utils.warn_names_duplicates(\"var\")\n"
     ]
    }
   ],
   "source": [
    "#map back onto raw adata\n",
    "adata = sc.read_10x_h5('lung1.h5')\n",
    "adata.var_names_make_unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "47778b56",
   "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>cell_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAACCCAAGCAACAAT-1</th>\n",
       "      <td>alveolar macrophage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCCAAGGAGGGTG-1</th>\n",
       "      <td>lung macrophage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCCAAGGTCCCTG-1</th>\n",
       "      <td>lung macrophage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCCACAACGCATT-1</th>\n",
       "      <td>lung macrophage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCCACACCAGCGT-1</th>\n",
       "      <td>intermediate monocyte</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGTTGCAGGACTTT-1</th>\n",
       "      <td>T cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGTTGGTAGGCAAC-1</th>\n",
       "      <td>lung macrophage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGTTGGTAGTCTGT-1</th>\n",
       "      <td>lung macrophage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGTTGGTCGCTGCA-1</th>\n",
       "      <td>T cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGTTGTCAACCCGG-1</th>\n",
       "      <td>myeloid dendritic cell</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12324 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 cell_type\n",
       "AAACCCAAGCAACAAT-1     alveolar macrophage\n",
       "AAACCCAAGGAGGGTG-1         lung macrophage\n",
       "AAACCCAAGGTCCCTG-1         lung macrophage\n",
       "AAACCCACAACGCATT-1         lung macrophage\n",
       "AAACCCACACCAGCGT-1   intermediate monocyte\n",
       "...                                    ...\n",
       "TTTGTTGCAGGACTTT-1                  T cell\n",
       "TTTGTTGGTAGGCAAC-1         lung macrophage\n",
       "TTTGTTGGTAGTCTGT-1         lung macrophage\n",
       "TTTGTTGGTCGCTGCA-1                  T cell\n",
       "TTTGTTGTCAACCCGG-1  myeloid dendritic cell\n",
       "\n",
       "[12324 rows x 1 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs['cell_type'] = adata.obs.index.map(cell_mapper)\n",
    "adata.obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d60d478e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9cfc5555",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5244b73",
   "metadata": {},
   "outputs": [],
   "source": [
    "#optional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a982fe18",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def pp(adata):\n",
    "    sc.pp.filter_cells(adata, min_genes=200) #get rid of cells with fewer than 200 genes\n",
    "    sc.pp.filter_genes(adata, min_cells=3) #get rid of genes that are found in fewer than 3 cells\n",
    "    adata.var['mt'] = adata.var_names.str.startswith('MT-')  # annotate the group of mitochondrial genes as 'mt'\n",
    "    sc.pp.calculate_qc_metrics(adata, qc_vars=['mt'], percent_top=None, log1p=False, inplace=True)\n",
    "    upper_lim = np.quantile(adata.obs.n_genes_by_counts.values, .98)\n",
    "    lower_lim = np.quantile(adata.obs.n_genes_by_counts.values, .02)\n",
    "    adata = adata[(adata.obs.n_genes_by_counts < upper_lim) & (adata.obs.n_genes_by_counts > lower_lim)]\n",
    "    adata = adata[adata.obs.pct_counts_mt < 20]\n",
    "    sc.pp.normalize_total(adata, target_sum=1e4) #normalize every cell to 10,000 UMI\n",
    "    sc.pp.log1p(adata) #change to log counts\n",
    "    sc.pp.highly_variable_genes(adata, min_mean=0.0125, max_mean=3, min_disp=0.5) #these are default values\n",
    "    adata.raw = adata #save raw data before processing values and further filtering\n",
    "    adata = adata[:, adata.var.highly_variable] #filter highly variable\n",
    "    sc.pp.regress_out(adata, ['total_counts', 'pct_counts_mt']) #Regress out effects of total counts per cell and the percentage of mitochondrial genes expressed\n",
    "    sc.pp.scale(adata, max_value=10) #scale each gene to unit variance\n",
    "    sc.tl.pca(adata, svd_solver='arpack')\n",
    "    sc.pp.neighbors(adata, n_neighbors=10, n_pcs=20)\n",
    "    sc.tl.umap(adata)\n",
    "    return adata\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6190b5d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jrlab2019/miniconda3/envs/sc/lib/python3.9/site-packages/scanpy/preprocessing/_normalization.py:170: UserWarning: Received a view of an AnnData. Making a copy.\n",
      "  view_to_actual(adata)\n",
      "/home/jrlab2019/miniconda3/envs/sc/lib/python3.9/site-packages/anndata/_core/anndata.py:1235: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.\n",
      "  df[key] = c\n",
      "/home/jrlab2019/miniconda3/envs/sc/lib/python3.9/site-packages/anndata/_core/anndata.py:1235: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.\n",
      "  df[key] = c\n"
     ]
    }
   ],
   "source": [
    "adata = pp(adata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "566df0bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.umap(adata, color = 'cell_type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c2f6d513",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: Length of palette colors is smaller than the number of categories (palette length: 1, categories length: 5. Some categories will have the same color.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jrlab2019/miniconda3/envs/sc/lib/python3.9/site-packages/anndata/compat/_overloaded_dict.py:106: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.\n",
      "  self.data[key] = value\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sc.pl.umap(adata, show = False)\n",
    "\n",
    "sc.pl.umap(adata[adata.obs.cell_type.str.contains('T cell')], palette = ['red'], color = 'cell_type', ax = ax)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "fc7397b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.umap(adata, color = 'Cd3e')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9094fba7",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}