scNorm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Has part for normal preprocessing, then for tumor samples for PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scanpy as sc\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "import anndata2ri\n",
    "import logging\n",
    "from scipy.sparse import issparse\n",
    "\n",
    "import rpy2.rinterface_lib.callbacks as rcb\n",
    "import rpy2.robjects as ro\n",
    "\n",
    "sc.settings.verbosity = 0\n",
    "sc.settings.set_figure_params(\n",
    "    dpi=80,\n",
    "    facecolor=\"white\",\n",
    "    # color_map=\"YlGnBu\",\n",
    "    frameon=False,\n",
    ")\n",
    "\n",
    "rcb.logger.setLevel(logging.ERROR)\n",
    "ro.pandas2ri.activate()\n",
    "#anndata2ri.activate()\n",
    "\n",
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.environ['PYTHONHASHSEED'] = '0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def shift_log(adata):\n",
    "    \n",
    "    np.random.seed(0)\n",
    "    \n",
    "    #p1 = sns.histplot(adata.obs[\"total_counts\"], bins=100, kde=False)\n",
    "    \n",
    "    scales_counts = sc.pp.normalize_total(adata, target_sum=None, inplace=False)\n",
    "    # log1p transform\n",
    "    adata.layers[\"log1p_norm\"] = sc.pp.log1p(scales_counts[\"X\"], copy=True)\n",
    "    \n",
    "    fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n",
    "    p1 = sns.histplot(adata.obs[\"total_counts\"], bins=100, kde=False, ax=axes[0])\n",
    "    axes[0].set_title(\"Total counts\")\n",
    "    p2 = sns.histplot(adata.layers[\"log1p_norm\"].sum(1), bins=100, kde=False, ax=axes[1])\n",
    "    axes[1].set_title(\"Shifted logarithm\")\n",
    "    plt.show()\n",
    "    \n",
    "    return adata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata3B = sc.read_h5ad(\n",
    "    filename=\"sample_3B/sample3B_quality_control.h5ad\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata26 = sc.read_h5ad(\n",
    "    filename=\"sample_26/sample26_quality_control.h5ad\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>n_genes_by_counts</th>\n",
       "      <th>log1p_n_genes_by_counts</th>\n",
       "      <th>total_counts</th>\n",
       "      <th>log1p_total_counts</th>\n",
       "      <th>pct_counts_in_top_20_genes</th>\n",
       "      <th>total_counts_mt</th>\n",
       "      <th>log1p_total_counts_mt</th>\n",
       "      <th>pct_counts_mt</th>\n",
       "      <th>total_counts_ribo</th>\n",
       "      <th>log1p_total_counts_ribo</th>\n",
       "      <th>pct_counts_ribo</th>\n",
       "      <th>total_counts_hb</th>\n",
       "      <th>log1p_total_counts_hb</th>\n",
       "      <th>pct_counts_hb</th>\n",
       "      <th>outlier</th>\n",
       "      <th>mt_outlier</th>\n",
       "      <th>is_outlier</th>\n",
       "      <th>sample</th>\n",
       "      <th>scDblFinder_score</th>\n",
       "      <th>scDblFinder_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAACCCAAGACGCAGT-1</th>\n",
       "      <td>416</td>\n",
       "      <td>6.033086</td>\n",
       "      <td>724.0</td>\n",
       "      <td>6.586172</td>\n",
       "      <td>26.795580</td>\n",
       "      <td>67.0</td>\n",
       "      <td>4.219508</td>\n",
       "      <td>9.254144</td>\n",
       "      <td>182.0</td>\n",
       "      <td>5.209486</td>\n",
       "      <td>25.138123</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>26</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>singlet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCCAAGACTACGG-1</th>\n",
       "      <td>3569</td>\n",
       "      <td>8.180321</td>\n",
       "      <td>16680.0</td>\n",
       "      <td>9.722026</td>\n",
       "      <td>17.883693</td>\n",
       "      <td>553.0</td>\n",
       "      <td>6.317165</td>\n",
       "      <td>3.315348</td>\n",
       "      <td>5576.0</td>\n",
       "      <td>8.626407</td>\n",
       "      <td>33.429256</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>26</td>\n",
       "      <td>0.010236</td>\n",
       "      <td>singlet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCCAAGTCCCGGT-1</th>\n",
       "      <td>7548</td>\n",
       "      <td>8.929170</td>\n",
       "      <td>69740.0</td>\n",
       "      <td>11.152544</td>\n",
       "      <td>18.187554</td>\n",
       "      <td>1357.0</td>\n",
       "      <td>7.213768</td>\n",
       "      <td>1.945799</td>\n",
       "      <td>17457.0</td>\n",
       "      <td>9.767553</td>\n",
       "      <td>25.031546</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.397895</td>\n",
       "      <td>0.014339</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>26</td>\n",
       "      <td>0.062422</td>\n",
       "      <td>singlet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCCAAGTTGCCTA-1</th>\n",
       "      <td>1187</td>\n",
       "      <td>7.080026</td>\n",
       "      <td>2492.0</td>\n",
       "      <td>7.821242</td>\n",
       "      <td>19.181380</td>\n",
       "      <td>91.0</td>\n",
       "      <td>4.521789</td>\n",
       "      <td>3.651685</td>\n",
       "      <td>382.0</td>\n",
       "      <td>5.948035</td>\n",
       "      <td>15.329053</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.040128</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>26</td>\n",
       "      <td>0.001254</td>\n",
       "      <td>singlet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCCAGTGAGAGGG-1</th>\n",
       "      <td>2214</td>\n",
       "      <td>7.703008</td>\n",
       "      <td>7381.0</td>\n",
       "      <td>8.906800</td>\n",
       "      <td>20.891478</td>\n",
       "      <td>261.0</td>\n",
       "      <td>5.568345</td>\n",
       "      <td>3.536106</td>\n",
       "      <td>1964.0</td>\n",
       "      <td>7.583248</td>\n",
       "      <td>26.608860</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>26</td>\n",
       "      <td>0.001474</td>\n",
       "      <td>singlet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGTTGAGTAAACGT-1</th>\n",
       "      <td>1537</td>\n",
       "      <td>7.338238</td>\n",
       "      <td>3601.0</td>\n",
       "      <td>8.189244</td>\n",
       "      <td>22.216051</td>\n",
       "      <td>456.0</td>\n",
       "      <td>6.124683</td>\n",
       "      <td>12.663150</td>\n",
       "      <td>621.0</td>\n",
       "      <td>6.432940</td>\n",
       "      <td>17.245209</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>26</td>\n",
       "      <td>0.106057</td>\n",
       "      <td>singlet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGTTGCATTCAGGT-1</th>\n",
       "      <td>4619</td>\n",
       "      <td>8.438150</td>\n",
       "      <td>23538.0</td>\n",
       "      <td>10.066414</td>\n",
       "      <td>14.992778</td>\n",
       "      <td>483.0</td>\n",
       "      <td>6.182085</td>\n",
       "      <td>2.052001</td>\n",
       "      <td>5462.0</td>\n",
       "      <td>8.605753</td>\n",
       "      <td>23.205030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>26</td>\n",
       "      <td>0.240245</td>\n",
       "      <td>singlet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGTTGGTCAGGAGT-1</th>\n",
       "      <td>4171</td>\n",
       "      <td>8.336151</td>\n",
       "      <td>18478.0</td>\n",
       "      <td>9.824390</td>\n",
       "      <td>22.405022</td>\n",
       "      <td>687.0</td>\n",
       "      <td>6.533789</td>\n",
       "      <td>3.717935</td>\n",
       "      <td>4384.0</td>\n",
       "      <td>8.385945</td>\n",
       "      <td>23.725512</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>26</td>\n",
       "      <td>0.057561</td>\n",
       "      <td>singlet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGTTGTCAGACCTA-1</th>\n",
       "      <td>4357</td>\n",
       "      <td>8.379769</td>\n",
       "      <td>20113.0</td>\n",
       "      <td>9.909171</td>\n",
       "      <td>15.666484</td>\n",
       "      <td>881.0</td>\n",
       "      <td>6.782192</td>\n",
       "      <td>4.380251</td>\n",
       "      <td>4260.0</td>\n",
       "      <td>8.357259</td>\n",
       "      <td>21.180332</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>26</td>\n",
       "      <td>0.354901</td>\n",
       "      <td>singlet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGTTGTCCCTATTA-1</th>\n",
       "      <td>687</td>\n",
       "      <td>6.533789</td>\n",
       "      <td>1340.0</td>\n",
       "      <td>7.201171</td>\n",
       "      <td>23.432836</td>\n",
       "      <td>41.0</td>\n",
       "      <td>3.737670</td>\n",
       "      <td>3.059701</td>\n",
       "      <td>353.0</td>\n",
       "      <td>5.869297</td>\n",
       "      <td>26.343283</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>26</td>\n",
       "      <td>0.000347</td>\n",
       "      <td>singlet</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5432 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    n_genes_by_counts  log1p_n_genes_by_counts  total_counts  \\\n",
       "AAACCCAAGACGCAGT-1                416                 6.033086         724.0   \n",
       "AAACCCAAGACTACGG-1               3569                 8.180321       16680.0   \n",
       "AAACCCAAGTCCCGGT-1               7548                 8.929170       69740.0   \n",
       "AAACCCAAGTTGCCTA-1               1187                 7.080026        2492.0   \n",
       "AAACCCAGTGAGAGGG-1               2214                 7.703008        7381.0   \n",
       "...                               ...                      ...           ...   \n",
       "TTTGTTGAGTAAACGT-1               1537                 7.338238        3601.0   \n",
       "TTTGTTGCATTCAGGT-1               4619                 8.438150       23538.0   \n",
       "TTTGTTGGTCAGGAGT-1               4171                 8.336151       18478.0   \n",
       "TTTGTTGTCAGACCTA-1               4357                 8.379769       20113.0   \n",
       "TTTGTTGTCCCTATTA-1                687                 6.533789        1340.0   \n",
       "\n",
       "                    log1p_total_counts  pct_counts_in_top_20_genes  \\\n",
       "AAACCCAAGACGCAGT-1            6.586172                   26.795580   \n",
       "AAACCCAAGACTACGG-1            9.722026                   17.883693   \n",
       "AAACCCAAGTCCCGGT-1           11.152544                   18.187554   \n",
       "AAACCCAAGTTGCCTA-1            7.821242                   19.181380   \n",
       "AAACCCAGTGAGAGGG-1            8.906800                   20.891478   \n",
       "...                                ...                         ...   \n",
       "TTTGTTGAGTAAACGT-1            8.189244                   22.216051   \n",
       "TTTGTTGCATTCAGGT-1           10.066414                   14.992778   \n",
       "TTTGTTGGTCAGGAGT-1            9.824390                   22.405022   \n",
       "TTTGTTGTCAGACCTA-1            9.909171                   15.666484   \n",
       "TTTGTTGTCCCTATTA-1            7.201171                   23.432836   \n",
       "\n",
       "                    total_counts_mt  log1p_total_counts_mt  pct_counts_mt  \\\n",
       "AAACCCAAGACGCAGT-1             67.0               4.219508       9.254144   \n",
       "AAACCCAAGACTACGG-1            553.0               6.317165       3.315348   \n",
       "AAACCCAAGTCCCGGT-1           1357.0               7.213768       1.945799   \n",
       "AAACCCAAGTTGCCTA-1             91.0               4.521789       3.651685   \n",
       "AAACCCAGTGAGAGGG-1            261.0               5.568345       3.536106   \n",
       "...                             ...                    ...            ...   \n",
       "TTTGTTGAGTAAACGT-1            456.0               6.124683      12.663150   \n",
       "TTTGTTGCATTCAGGT-1            483.0               6.182085       2.052001   \n",
       "TTTGTTGGTCAGGAGT-1            687.0               6.533789       3.717935   \n",
       "TTTGTTGTCAGACCTA-1            881.0               6.782192       4.380251   \n",
       "TTTGTTGTCCCTATTA-1             41.0               3.737670       3.059701   \n",
       "\n",
       "                    total_counts_ribo  log1p_total_counts_ribo  \\\n",
       "AAACCCAAGACGCAGT-1              182.0                 5.209486   \n",
       "AAACCCAAGACTACGG-1             5576.0                 8.626407   \n",
       "AAACCCAAGTCCCGGT-1            17457.0                 9.767553   \n",
       "AAACCCAAGTTGCCTA-1              382.0                 5.948035   \n",
       "AAACCCAGTGAGAGGG-1             1964.0                 7.583248   \n",
       "...                               ...                      ...   \n",
       "TTTGTTGAGTAAACGT-1              621.0                 6.432940   \n",
       "TTTGTTGCATTCAGGT-1             5462.0                 8.605753   \n",
       "TTTGTTGGTCAGGAGT-1             4384.0                 8.385945   \n",
       "TTTGTTGTCAGACCTA-1             4260.0                 8.357259   \n",
       "TTTGTTGTCCCTATTA-1              353.0                 5.869297   \n",
       "\n",
       "                    pct_counts_ribo  total_counts_hb  log1p_total_counts_hb  \\\n",
       "AAACCCAAGACGCAGT-1        25.138123              0.0               0.000000   \n",
       "AAACCCAAGACTACGG-1        33.429256              0.0               0.000000   \n",
       "AAACCCAAGTCCCGGT-1        25.031546             10.0               2.397895   \n",
       "AAACCCAAGTTGCCTA-1        15.329053              1.0               0.693147   \n",
       "AAACCCAGTGAGAGGG-1        26.608860              0.0               0.000000   \n",
       "...                             ...              ...                    ...   \n",
       "TTTGTTGAGTAAACGT-1        17.245209              0.0               0.000000   \n",
       "TTTGTTGCATTCAGGT-1        23.205030              0.0               0.000000   \n",
       "TTTGTTGGTCAGGAGT-1        23.725512              0.0               0.000000   \n",
       "TTTGTTGTCAGACCTA-1        21.180332              0.0               0.000000   \n",
       "TTTGTTGTCCCTATTA-1        26.343283              0.0               0.000000   \n",
       "\n",
       "                    pct_counts_hb  outlier  mt_outlier  is_outlier sample  \\\n",
       "AAACCCAAGACGCAGT-1       0.000000    False       False       False     26   \n",
       "AAACCCAAGACTACGG-1       0.000000    False       False       False     26   \n",
       "AAACCCAAGTCCCGGT-1       0.014339    False       False       False     26   \n",
       "AAACCCAAGTTGCCTA-1       0.040128    False       False       False     26   \n",
       "AAACCCAGTGAGAGGG-1       0.000000    False       False       False     26   \n",
       "...                           ...      ...         ...         ...    ...   \n",
       "TTTGTTGAGTAAACGT-1       0.000000    False       False       False     26   \n",
       "TTTGTTGCATTCAGGT-1       0.000000    False       False       False     26   \n",
       "TTTGTTGGTCAGGAGT-1       0.000000    False       False       False     26   \n",
       "TTTGTTGTCAGACCTA-1       0.000000    False       False       False     26   \n",
       "TTTGTTGTCCCTATTA-1       0.000000    False       False       False     26   \n",
       "\n",
       "                    scDblFinder_score scDblFinder_class  \n",
       "AAACCCAAGACGCAGT-1           0.000124           singlet  \n",
       "AAACCCAAGACTACGG-1           0.010236           singlet  \n",
       "AAACCCAAGTCCCGGT-1           0.062422           singlet  \n",
       "AAACCCAAGTTGCCTA-1           0.001254           singlet  \n",
       "AAACCCAGTGAGAGGG-1           0.001474           singlet  \n",
       "...                               ...               ...  \n",
       "TTTGTTGAGTAAACGT-1           0.106057           singlet  \n",
       "TTTGTTGCATTCAGGT-1           0.240245           singlet  \n",
       "TTTGTTGGTCAGGAGT-1           0.057561           singlet  \n",
       "TTTGTTGTCAGACCTA-1           0.354901           singlet  \n",
       "TTTGTTGTCCCTATTA-1           0.000347           singlet  \n",
       "\n",
       "[5432 rows x 20 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata26.obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata27 = sc.read_h5ad(\n",
    "    filename=\"sample_27/sample27_quality_control.h5ad\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata35 = sc.read_h5ad(\n",
    "    filename=\"sample_35/sample35_quality_control.h5ad\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata36 = sc.read_h5ad(\n",
    "    filename=\"sample_36/sample36_quality_control.h5ad\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "adataTICP32 = sc.read_h5ad(\n",
    "    filename=\"sample_TICP32/sampleTICP32_quality_control.h5ad\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 382,
       "width": 707
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "adata3B = shift_log(adata3B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 382,
       "width": 715
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "adata26 = shift_log(adata26)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 382,
       "width": 708
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "adata27 = shift_log(adata27)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 382,
       "width": 703
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "adata36 = shift_log(adata36)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 382,
       "width": 692
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "adata35 = shift_log(adata35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 382,
       "width": 704
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "adataTICP32 = shift_log(adataTICP32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata3B.write(\"sample_3B/sample3B_normalization.h5ad\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata26.write(\"sample_26/sample26_normalization.h5ad\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata27.write(\"sample_27/sample27_normalization.h5ad\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata35.write(\"sample_35/sample35_normalization.h5ad\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata36.write(\"sample_36/sample36_normalization.h5ad\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "adataTICP32.write(\"sample_TICP32/sampleTICP32_normalization.h5ad\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "meso2_conda_env",
   "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}