tutorial/3.mouse_brain.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5c3136f8-761d-4e9a-b0ad-3e6d2d74bd5d",
   "metadata": {},
   "source": [
    "# Using SCOTCH to integarte MERFISH and singl-cell ATAC-seq data of mouse brain cortex"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "228a9e02-28c2-40cf-ac1a-74ac7900b698",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-01T05:49:52.201683Z",
     "iopub.status.busy": "2024-01-01T05:49:52.201162Z",
     "iopub.status.idle": "2024-01-01T05:49:52.206417Z",
     "shell.execute_reply": "2024-01-01T05:49:52.205386Z",
     "shell.execute_reply.started": "2024-01-01T05:49:52.201640Z"
    },
    "tags": []
   },
   "source": [
    "## Import required python packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "94ee1ca8-154e-4b91-9067-b88ab8767635",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-01T09:15:41.507735Z",
     "iopub.status.busy": "2024-01-01T09:15:41.507019Z",
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import anndata as ad\n",
    "import scanpy as sc\n",
    "import numpy as np\n",
    "from scotch import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1db482c8-8f3e-4f06-93e3-81bb3de4d2a6",
   "metadata": {},
   "source": [
    "## Load datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a4b6f3ca-944d-4300-955a-6871c254184f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-01T09:15:42.062819Z",
     "iopub.status.busy": "2024-01-01T09:15:42.062396Z",
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     "shell.execute_reply": "2024-01-01T09:15:51.041272Z",
     "shell.execute_reply.started": "2024-01-01T09:15:42.062797Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "rna = load_data(\"/slurm/home/yrd/fanlab/yangpenghui/SCOTCH/data/mouse-brain/rna.h5ad\")\n",
    "gam = load_data(\"/slurm/home/yrd/fanlab/yangpenghui/SCOTCH/data/mouse-brain/gam.h5ad\")\n",
    "atac = load_data(\"/slurm/home/yrd/fanlab/yangpenghui/SCOTCH/data/mouse-brain/atac.h5ad\")\n",
    "gam = gam[atac.obs.index]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10fba101-a8bf-4356-b247-97ef67a1ff70",
   "metadata": {},
   "source": [
    "## Process datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c67ff818-0325-47f9-981b-9a0e208a6bf8",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Process scRNA-seq dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "49473140-4ea2-4080-941b-332a23f68214",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-01T09:15:51.043937Z",
     "iopub.status.busy": "2024-01-01T09:15:51.043747Z",
     "iopub.status.idle": "2024-01-01T09:16:26.148589Z",
     "shell.execute_reply": "2024-01-01T09:16:26.147899Z",
     "shell.execute_reply.started": "2024-01-01T09:15:51.043910Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing RNA data...\n",
      "Identifying highly variable genes...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/preprocessing/_highly_variable_genes.py:62: UserWarning: `flavor='seurat_v3'` expects raw count data, but non-integers were found.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalizing total counts...\n",
      "Applying log1p transformation...\n",
      "Saving pre-log1p counts to a layer...\n",
      "Scaling the data...\n",
      "Performing PCA...\n",
      "Calculating neighbors based on cosine metric...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing UMAP...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "... storing 'cell_type' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing completed.\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Processing\n",
    "rna = process_anndata(rna)\n",
    "sc.pl.umap(rna, color=\"cell_type\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "33f0c2bb-5cbe-4457-a502-33c1f6d36d6e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-01T09:17:05.675547Z",
     "iopub.status.busy": "2024-01-01T09:17:05.675016Z",
     "iopub.status.idle": "2024-01-01T09:17:07.757774Z",
     "shell.execute_reply": "2024-01-01T09:17:07.756913Z",
     "shell.execute_reply.started": "2024-01-01T09:17:05.675522Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.tl.leiden(rna,resolution = 1)\n",
    "sc.pl.umap(rna, color=\"leiden\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2647b673-bf8e-433c-9fa8-506883e4d321",
   "metadata": {},
   "source": [
    "### Process scATAC-seq dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cadb85c8-4102-4a06-a7ee-ea9246c918b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-01T09:17:07.760418Z",
     "iopub.status.busy": "2024-01-01T09:17:07.760033Z",
     "iopub.status.idle": "2024-01-01T09:22:08.151637Z",
     "shell.execute_reply": "2024-01-01T09:22:08.150836Z",
     "shell.execute_reply.started": "2024-01-01T09:17:07.760398Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing ATAC data...\n",
      "Running LSI...\n",
      "Calculating neighbors based on cosine metric using X_lsi...\n",
      "Performing UMAP...\n",
      "Processing completed.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "atac = process_anndata(atac,mode='atac')\n",
    "sc.tl.leiden(atac,resolution = 1)\n",
    "sc.pl.umap(atac, color=\"leiden\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "878873b5-bc1b-47c0-a93b-acfbddb3020a",
   "metadata": {},
   "source": [
    "### Process scATAC-seq dataset (gene activity score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d7931271-2548-41fe-86cb-4ccef2613624",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-01T09:22:08.152973Z",
     "iopub.status.busy": "2024-01-01T09:22:08.152582Z",
     "iopub.status.idle": "2024-01-01T09:22:12.732610Z",
     "shell.execute_reply": "2024-01-01T09:22:12.731812Z",
     "shell.execute_reply.started": "2024-01-01T09:22:08.152930Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing RNA data...\n",
      "Identifying highly variable genes...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trying to set attribute `.uns` of view, copying.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalizing total counts...\n",
      "Applying log1p transformation...\n",
      "Saving pre-log1p counts to a layer...\n",
      "Scaling the data...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trying to set attribute `.obs` of view, copying.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing completed.\n"
     ]
    }
   ],
   "source": [
    "gam = process_anndata(gam,pca=False,neighbors=False,umap=False)\n",
    "gam = gam[atac.obs.index]\n",
    "gam.obs['leiden'] = atac.obs['leiden']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5f0f206-6491-4e35-b36d-65eebdbc7366",
   "metadata": {},
   "outputs": [],
   "source": [
    "gam.obsm['reduction'] = atac.obsm['X_lsi']\n",
    "rna.obsm['reduction'] = rna.obsm['X_pca']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12996859-e009-423f-ab29-86f6daa381be",
   "metadata": {},
   "source": [
    "## Transfer labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a88ca784-27f2-40cc-90b3-defb11c639c7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-01T09:28:36.796783Z",
     "iopub.status.busy": "2024-01-01T09:28:36.795359Z",
     "iopub.status.idle": "2024-01-01T09:28:37.682539Z",
     "shell.execute_reply": "2024-01-01T09:28:37.681798Z",
     "shell.execute_reply.started": "2024-01-01T09:28:36.796734Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/anndata/_core/anndata.py:1095: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\n",
      "  df_sub[k].cat.remove_unused_categories(inplace=True)\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/anndata/_core/anndata.py:1095: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\n",
      "  df_sub[k].cat.remove_unused_categories(inplace=True)\n",
      "Trying to set attribute `.uns` of view, copying.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finding marker genes...\n",
      "Number of common genes: 231\n",
      "Calculating marker genes based on 'leiden' for adata1...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:394: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'names'] = self.var_names[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:396: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'scores'] = scores[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:399: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'pvals'] = pvals[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:409: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'pvals_adj'] = pvals_adj[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:394: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'names'] = self.var_names[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:396: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'scores'] = scores[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:399: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'pvals'] = pvals[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:409: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'pvals_adj'] = pvals_adj[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:394: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'names'] = self.var_names[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:396: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'scores'] = scores[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:399: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'pvals'] = pvals[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:409: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'pvals_adj'] = pvals_adj[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:394: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'names'] = self.var_names[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:396: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'scores'] = scores[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:399: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'pvals'] = pvals[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:409: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'pvals_adj'] = pvals_adj[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "Trying to set attribute `.uns` of view, copying.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating marker genes based on 'leiden' for adata2...\n",
      "Marker gene calculation completed.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:394: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'names'] = self.var_names[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:396: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'scores'] = scores[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:399: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'pvals'] = pvals[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:409: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'pvals_adj'] = pvals_adj[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:394: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'names'] = self.var_names[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:396: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'scores'] = scores[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:399: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'pvals'] = pvals[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:409: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'pvals_adj'] = pvals_adj[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:394: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'names'] = self.var_names[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:396: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'scores'] = scores[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:399: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'pvals'] = pvals[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:409: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'pvals_adj'] = pvals_adj[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:394: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'names'] = self.var_names[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:396: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'scores'] = scores[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:399: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'pvals'] = pvals[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:409: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'pvals_adj'] = pvals_adj[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:394: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'names'] = self.var_names[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:396: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'scores'] = scores[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:399: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'pvals'] = pvals[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:409: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'pvals_adj'] = pvals_adj[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:394: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'names'] = self.var_names[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:396: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'scores'] = scores[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:399: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'pvals'] = pvals[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:409: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'pvals_adj'] = pvals_adj[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:394: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'names'] = self.var_names[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:396: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'scores'] = scores[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:399: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'pvals'] = pvals[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:409: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'pvals_adj'] = pvals_adj[global_indices]\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: RuntimeWarning: invalid value encountered in log2\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/scanpy/tools/_rank_genes_groups.py:420: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.stats[group_name, 'logfoldchanges'] = np.log2(\n"
     ]
    }
   ],
   "source": [
    "# Calculate marker genes\n",
    "adata1,adata2 = find_marker(rna,gam,min_cells=0,top_marker_num=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7f3b5066-1e69-40ec-a55c-d5c57c822d20",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-01T09:28:44.544866Z",
     "iopub.status.busy": "2024-01-01T09:28:44.544550Z",
     "iopub.status.idle": "2024-01-01T09:28:45.478495Z",
     "shell.execute_reply": "2024-01-01T09:28:45.477792Z",
     "shell.execute_reply.started": "2024-01-01T09:28:44.544846Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing label transfer...\n",
      "Calculating average expression...\n",
      "Computing correlation matrix...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/anndata/_core/anndata.py:1095: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\n",
      "  df_sub[k].cat.remove_unused_categories(inplace=True)\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/anndata/_core/anndata.py:1095: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\n",
      "  df_sub[k].cat.remove_unused_categories(inplace=True)\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/anndata/_core/anndata.py:1095: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\n",
      "  df_sub[k].cat.remove_unused_categories(inplace=True)\n",
      "Trying to set attribute `.obs` of view, copying.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing unbalanced optimal transport...\n",
      "Applying cutoff to transfer probabilities and mapping labels...\n",
      "Label transfer completed.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adata1, adata2, transfer_df = scotch_transfer( test_data = adata1,reference_data = adata2,test_avg_by='leiden',reference_avg_by='leiden',reg=1)\n",
    "plot_heatmap(transfer_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1f5efaa-0ce4-4462-a44e-725e84551dc2",
   "metadata": {},
   "source": [
    "## Cell alignment calculates matching relationships"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1c5a9e8b-337c-4126-b801-e6b6b4577ccb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-01T09:30:50.404512Z",
     "iopub.status.busy": "2024-01-01T09:30:50.403995Z",
     "iopub.status.idle": "2024-01-01T09:30:50.707046Z",
     "shell.execute_reply": "2024-01-01T09:30:50.706269Z",
     "shell.execute_reply.started": "2024-01-01T09:30:50.404479Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/anndata/_core/anndata.py:1095: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\n",
      "  df_sub[k].cat.remove_unused_categories(inplace=True)\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/anndata/_core/anndata.py:1095: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\n",
      "  df_sub[k].cat.remove_unused_categories(inplace=True)\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "Trying to set attribute `.obs` of view, copying.\n"
     ]
    }
   ],
   "source": [
    "ann1, ann2 = intersect_datasets(data1 = adata1, data2 = adata2)\n",
    "ann1.obs['cell_type'] = ann1.obs['leiden']\n",
    "ann2.obs['cell_type'] = ann2.obs['leiden']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0ddb8092-ad62-490d-b22a-a21a4bc2f0ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-01T09:30:51.534297Z",
     "iopub.status.busy": "2024-01-01T09:30:51.533962Z",
     "iopub.status.idle": "2024-01-01T09:31:55.051403Z",
     "shell.execute_reply": "2024-01-01T09:31:55.050353Z",
     "shell.execute_reply.started": "2024-01-01T09:30:51.534276Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing SCOTCH alignment...\n",
      "Processing cell type 1/21: 14\n",
      "mass : 0.45722713864306785\n",
      "Constructing connectivity...\n",
      "k = 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/anndata/_core/anndata.py:1095: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\n",
      "  df_sub[k].cat.remove_unused_categories(inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running OT...\n",
      "Processing cell type 2/21: 6\n",
      "mass : 0.8704453441295547\n",
      "Constructing connectivity...\n",
      "k = 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/anndata/_core/anndata.py:1095: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\n",
      "  df_sub[k].cat.remove_unused_categories(inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running OT...\n",
      "Processing cell type 3/21: 7\n",
      "mass : 0.6147959183673469\n",
      "Constructing connectivity...\n",
      "k = 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/anndata/_core/anndata.py:1095: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\n",
      "  df_sub[k].cat.remove_unused_categories(inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running OT...\n",
      "Processing cell type 4/21: 25\n",
      "mass : 0.18021201413427562\n",
      "Constructing connectivity...\n",
      "k = 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/anndata/_core/anndata.py:1095: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\n",
      "  df_sub[k].cat.remove_unused_categories(inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running OT...\n",
      "Processing cell type 5/21: 10\n",
      "mass : 0.6767676767676768\n",
      "Constructing connectivity...\n",
      "k = 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/anndata/_core/anndata.py:1095: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\n",
      "  df_sub[k].cat.remove_unused_categories(inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running OT...\n",
      "Processing cell type 6/21: 5\n",
      "mass : 0.9800664451827242\n",
      "Constructing connectivity...\n",
      "k = 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/anndata/_core/anndata.py:1095: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\n",
      "  df_sub[k].cat.remove_unused_categories(inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running OT...\n",
      "Processing cell type 7/21: 22\n",
      "mass : 0.7375\n",
      "Constructing connectivity...\n",
      "k = 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/anndata/_core/anndata.py:1095: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\n",
      "  df_sub[k].cat.remove_unused_categories(inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running OT...\n",
      "Processing cell type 8/21: 2\n",
      "mass : 0.984375\n",
      "Constructing connectivity...\n",
      "k = 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/anndata/_core/anndata.py:1095: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\n",
      "  df_sub[k].cat.remove_unused_categories(inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running OT...\n",
      "Processing cell type 9/21: 8\n",
      "mass : 0.5545851528384279\n",
      "Constructing connectivity...\n",
      "k = 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/anndata/_core/anndata.py:1095: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\n",
      "  df_sub[k].cat.remove_unused_categories(inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running OT...\n",
      "Processing cell type 10/21: 15\n",
      "mass : 0.5461538461538461\n",
      "Constructing connectivity...\n",
      "k = 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/anndata/_core/anndata.py:1095: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\n",
      "  df_sub[k].cat.remove_unused_categories(inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running OT...\n",
      "Processing cell type 11/21: 1\n",
      "mass : 0.31026785714285715\n",
      "Constructing connectivity...\n",
      "k = 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/anndata/_core/anndata.py:1095: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\n",
      "  df_sub[k].cat.remove_unused_categories(inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running OT...\n",
      "Processing cell type 12/21: 19\n",
      "mass : 0.4105263157894737\n",
      "Constructing connectivity...\n",
      "k = 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/anndata/_core/anndata.py:1095: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\n",
      "  df_sub[k].cat.remove_unused_categories(inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running OT...\n",
      "Processing cell type 13/21: 17\n",
      "mass : 0.9752066115702479\n",
      "Constructing connectivity...\n",
      "k = 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/anndata/_core/anndata.py:1095: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\n",
      "  df_sub[k].cat.remove_unused_categories(inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running OT...\n",
      "Processing cell type 14/21: 9\n",
      "mass : 0.9307359307359307\n",
      "Constructing connectivity...\n",
      "k = 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/anndata/_core/anndata.py:1095: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\n",
      "  df_sub[k].cat.remove_unused_categories(inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running OT...\n",
      "Processing cell type 15/21: 16\n",
      "mass : 0.6829268292682927\n",
      "Constructing connectivity...\n",
      "k = 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/anndata/_core/anndata.py:1095: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\n",
      "  df_sub[k].cat.remove_unused_categories(inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running OT...\n",
      "Processing cell type 16/21: 12\n",
      "mass : 0.6827956989247311\n",
      "Constructing connectivity...\n",
      "k = 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/anndata/_core/anndata.py:1095: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\n",
      "  df_sub[k].cat.remove_unused_categories(inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running OT...\n",
      "Processing cell type 17/21: 20\n",
      "mass : 0.6012658227848101\n",
      "Constructing connectivity...\n",
      "k = 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/anndata/_core/anndata.py:1095: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\n",
      "  df_sub[k].cat.remove_unused_categories(inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running OT...\n",
      "Processing cell type 18/21: 18\n",
      "mass : 0.78\n",
      "Constructing connectivity...\n",
      "k = 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/anndata/_core/anndata.py:1095: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\n",
      "  df_sub[k].cat.remove_unused_categories(inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running OT...\n",
      "Processing cell type 19/21: 11\n",
      "mass : 0.6808510638297872\n",
      "Constructing connectivity...\n",
      "k = 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/anndata/_core/anndata.py:1095: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\n",
      "  df_sub[k].cat.remove_unused_categories(inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running OT...\n",
      "Processing cell type 20/21: 21\n",
      "mass : 0.5862068965517241\n",
      "Constructing connectivity...\n",
      "k = 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/anndata/_core/anndata.py:1095: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\n",
      "  df_sub[k].cat.remove_unused_categories(inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running OT...\n",
      "Processing cell type 21/21: 24\n",
      "mass : 0.6082474226804123\n",
      "Constructing connectivity...\n",
      "k = 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/anndata/_core/anndata.py:1095: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\n",
      "  df_sub[k].cat.remove_unused_categories(inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running OT...\n",
      "SCOTCH alignment completed.\n"
     ]
    }
   ],
   "source": [
    "result = scotch_alignment(ann1, ann2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb901018-c4ef-4440-b8d8-3754d203c320",
   "metadata": {},
   "source": [
    "## Data integration using matching relationships"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "03385f87-4172-4799-85be-7216355884a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-01T09:33:53.060995Z",
     "iopub.status.busy": "2024-01-01T09:33:53.060520Z",
     "iopub.status.idle": "2024-01-01T09:34:09.879123Z",
     "shell.execute_reply": "2024-01-01T09:34:09.878359Z",
     "shell.execute_reply.started": "2024-01-01T09:33:53.060973Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating and concatenating AnnData objects...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/anndata/_core/anndata.py:1095: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\n",
      "  df_sub[k].cat.remove_unused_categories(inplace=True)\n",
      "/slurm/home/yrd/fanlab/yangpenghui/anaconda3/envs/yph/lib/python3.8/site-packages/anndata/_core/anndata.py:1095: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\n",
      "  df_sub[k].cat.remove_unused_categories(inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integration and concatenation completed.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "... storing 'leiden' as categorical\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adata1, adata2, adata_merged = integrate_adata(adata1=rna, adata2=gam, df=result, n_components = 30, max_iter = 1000,cca_components = 10)\n",
    "sc.pp.neighbors(adata_merged, use_rep=\"integrated\", metric=\"cosine\")\n",
    "sc.tl.umap(adata_merged)\n",
    "sc.pl.umap(adata_merged, color=\"batch\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "58b73660-0b3e-41fa-89d8-63e911c9ddb5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-01T09:32:15.057308Z",
     "iopub.status.busy": "2024-01-01T09:32:15.056783Z",
     "iopub.status.idle": "2024-01-01T09:32:16.260076Z",
     "shell.execute_reply": "2024-01-01T09:32:16.259480Z",
     "shell.execute_reply.started": "2024-01-01T09:32:15.057285Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.tl.leiden(adata_merged,resolution = 0.9)\n",
    "sc.pl.umap(adata_merged, color=\"leiden\",legend_loc=\"on data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6524dac8-616e-484a-b4dc-6907469d2a8f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-01T09:32:29.922145Z",
     "iopub.status.busy": "2024-01-01T09:32:29.921638Z",
     "iopub.status.idle": "2024-01-01T09:32:30.021715Z",
     "shell.execute_reply": "2024-01-01T09:32:30.021230Z",
     "shell.execute_reply.started": "2024-01-01T09:32:29.922118Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster2annotation = {\n",
    "     '0': 'L2/3 IT',\n",
    "     '1': 'L5 IT',\n",
    "     '2': 'L2/3 IT',\n",
    "     '3': 'Oligo',\n",
    "     '4': 'L4/5 IT',\n",
    "     '5': 'L6 CT',\n",
    "     '6': 'Astro',\n",
    "     '7': 'L5 ET',\n",
    "     '8': 'Pvalb',\n",
    "    '9': 'L6 IT',\n",
    "    '10': 'Micro',\n",
    "    '11': 'Lamp5/Vip',\n",
    "    '12': 'Sst',\n",
    "    '13': 'SMC/VLMC',\n",
    "    '14': 'OPC',\n",
    "    '15': 'Peri',\n",
    "    '16': 'L5/6 NP'\n",
    "}\n",
    "# add a new `.obs` column called `cell type` by mapping clusters to annotation using pandas `map` function\n",
    "adata_merged.obs['myct'] = adata_merged.obs['leiden'].map(cluster2annotation).astype('category')\n",
    "\n",
    "sc.pl.umap(adata_merged, color=\"myct\",legend_loc=\"on data\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "yph",
   "language": "python",
   "name": "yph"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}