Reproduce-MouseBrain-CB.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "684fcb55",
   "metadata": {},
   "source": [
    "# Download\n",
    "\n",
    "Mouse brain cells from Saunders et al (Drop-seq) http://dropviz.org/ --> Data\n",
    "\n",
    "annotation R Data: annotation.BrainCellAtlas_Saunders_version_2018.04.01.rds (12K)\n",
    "\n",
    "Hippocampus DGE  F_GRCm38.81.P60Hippocampus.raw.dge.txt.gz\n",
    "\n",
    "Subcluster  F_GRCm38.81.P60Hippocampus.subcluster.assign.RDS\n",
    "\n",
    "Mouse Brain cells from Zeisel et al (10X) http://mousebrain.org/downloads.html\n",
    "\n",
    "L5_All.loom"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab93c971",
   "metadata": {},
   "source": [
    "# Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1041fc9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scanpy==1.7.2 anndata==0.7.6 umap==0.4.6 numpy==1.19.2 scipy==1.6.2 pandas==1.1.5 scikit-learn==0.24.1 statsmodels==0.12.2 python-igraph==0.9.1 louvain==0.7.0 leidenalg==0.7.0\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scanpy as sc\n",
    "import rpy2.robjects as ro\n",
    "import rpy2.robjects.numpy2ri\n",
    "import anndata2ri\n",
    "\n",
    "sc.settings.verbosity = 3\n",
    "sc.logging.print_header()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "63e73323",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Loading required package: data.table\n",
      "\n",
      "R[write to console]: data.table 1.13.6 using 22 threads (see ?getDTthreads).  Latest news: r-datatable.com\n",
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      "\n",
      "R[write to console]: \n",
      "Seurat v4 will be going to CRAN in the near future;\n",
      " for more details, please visit https://satijalab.org/seurat/v4_changes\n",
      "\n",
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      "R[write to console]: Loading required package: SummarizedExperiment\n",
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      "    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,\n",
      "    clusterExport, clusterMap, parApply, parCapply, parLapply,\n",
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      "    which\n",
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      "    IQR, mad, sd, var, xtabs\n",
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      "    anyDuplicated, append, as.data.frame, basename, cbind, colnames,\n",
      "    dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,\n",
      "    grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,\n",
      "    order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,\n",
      "    rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,\n",
      "    union, unique, unsplit, which, which.max, which.min\n",
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      "Attaching package: ‘S4Vectors’\n",
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      "R[write to console]: The following object is masked from ‘package:Matrix’:\n",
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      "    expand\n",
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      "    first, second\n",
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      "    expand.grid\n",
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      "Attaching package: ‘IRanges’\n",
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      "    shift\n",
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      "R[write to console]: Loading required package: GenomeInfoDb\n",
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      "\n",
      "R[write to console]: Welcome to Bioconductor\n",
      "\n",
      "    Vignettes contain introductory material; view with\n",
      "    'browseVignettes()'. To cite Bioconductor, see\n",
      "    'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'.\n",
      "\n",
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      "R[write to console]: Loading required package: DelayedArray\n",
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      "    colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges\n",
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      "R[write to console]: The following objects are masked from ‘package:base’:\n",
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      "    aperm, apply, rowsum\n",
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      "Attaching package: ‘SummarizedExperiment’\n",
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      "\n",
      "    Assays\n",
      "\n",
      "\n",
      "/home/jzhaoaz/.conda/envs/scUNIT/lib/python3.7/site-packages/numba/np/ufunc/parallel.py:355: NumbaWarning: \u001b[1mThe TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 9107. The TBB threading layer is disabled.\u001b[0m\n",
      "  warnings.warn(problem)\n",
      "Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "/home/jzhaoaz/.conda/envs/scUNIT/lib/python3.7/site-packages/ipykernel_launcher.py:66: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "/home/jzhaoaz/.conda/envs/scUNIT/lib/python3.7/site-packages/anndata/_core/anndata.py:1229: ImplicitModificationWarning: Initializing view as actual.\n",
      "  \"Initializing view as actual.\", ImplicitModificationWarning\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "... storing 'Age' as categorical\n",
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      "... storing 'Bucket' as categorical\n",
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      "... storing 'CellConc' as categorical\n",
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      "... storing 'Cell_Conc' as categorical\n",
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      "... storing 'ChipID' as categorical\n",
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      "... storing 'Class' as categorical\n",
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      "... storing 'ClusterName' as categorical\n",
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      "... storing 'Comment' as categorical\n",
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      "... storing 'Comments' as categorical\n",
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      "... storing 'DateCaptured' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "... storing 'Date_Captured' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "... storing 'Description' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "... storing 'Developmental_compartment' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "... storing 'DonorID' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "... storing 'Estimated Number of Cells' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "... storing 'Flowcell' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "... storing 'Fraction Reads in Cells' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "... storing 'Label' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "... storing 'Location_based_on' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "... storing 'Mean Reads per Cell' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "... storing 'Median Genes per Cell' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "... storing 'Median UMI Counts per Cell' as categorical\n",
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      "... storing 'NGI_PlateWell' as categorical\n",
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      "... storing 'Neurotransmitter' as categorical\n",
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      "... storing 'NumPooledAnimals' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "... storing 'Num_Pooled_Animals' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "... storing 'Number of Reads' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "... storing 'PCRCycles' as categorical\n",
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      "... storing 'PCR_Cycles' as categorical\n",
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      "... storing 'PassedQC' as categorical\n",
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      "... storing 'PlugDate' as categorical\n",
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      "... storing 'Plug_Date' as categorical\n",
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      "... storing 'Probable_location' as categorical\n",
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      "... storing 'Project' as categorical\n",
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      "... storing 'Q30 Bases in Barcode' as categorical\n",
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      "... storing 'Q30 Bases in RNA Read' as categorical\n",
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      "... storing 'Q30 Bases in Sample Index' as categorical\n",
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      "... storing 'Q30 Bases in UMI' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "... storing 'Reads Mapped Confidently to Exonic Regions' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "... storing 'Reads Mapped Confidently to Intergenic Regions' as categorical\n",
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      "... storing 'Reads Mapped Confidently to Intronic Regions' as categorical\n",
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      "... storing 'Reads Mapped Confidently to Transcriptome' as categorical\n",
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      "... storing 'Region' as categorical\n",
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      "... storing 'Sample_Index' as categorical\n",
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      "... storing 'SeqComment' as categorical\n",
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      "... storing 'SeqLibDate' as categorical\n",
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      "... storing 'SeqLibOk' as categorical\n",
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      "... storing 'Seq_Comment' as categorical\n",
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      "... storing 'Seq_Lib_Date' as categorical\n",
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      "... storing 'Seq_Lib_Ok' as categorical\n",
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      "... storing 'Sequencing Saturation' as categorical\n",
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      "... storing 'Serial_Number' as categorical\n",
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      "... storing 'Sex' as categorical\n",
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      "... storing 'Species' as categorical\n",
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      "... storing 'Strain' as categorical\n",
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      "... storing 'Subclass' as categorical\n",
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      "... storing 'TargetNumCells' as categorical\n",
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      "... storing 'Target_Num_Cells' as categorical\n",
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      "... storing 'TaxonomyRank1' as categorical\n",
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      "... storing 'TaxonomyRank2' as categorical\n",
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      "... storing 'TaxonomyRank4' as categorical\n",
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      "... storing 'Taxonomy_group' as categorical\n",
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      "... storing 'TimepointPool' as categorical\n",
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      "... storing 'Tissue' as categorical\n",
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      "... storing 'Total Genes Detected' as categorical\n",
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      "... storing 'Transcriptome' as categorical\n",
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      "... storing 'Valid Barcodes' as categorical\n",
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      "... storing 'cDNAConcNanogramPerMicroliter' as categorical\n",
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      "... storing 'cDNALibOk' as categorical\n",
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      "... storing 'cDNA_Lib_Ok' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "... storing 'ngperul_cDNA' as categorical\n"
     ]
    }
   ],
   "source": [
    "tissue = \"CB\"\n",
    "\n",
    "data_path = \"data_MB/\" + tissue\n",
    "\n",
    "if not os.path.exists(data_path):\n",
    "    os.makedirs(data_path)\n",
    "\n",
    "# Drop-seq\n",
    "\n",
    "datapath_Drop = \"./Raw_data_Saunders\"\n",
    "    \n",
    "dropviz_RDSname_dict = {\"TH\": \"F_GRCm38.81.P60Thalamus.subcluster.assign.RDS\",\n",
    "                        \"HC\": \"F_GRCm38.81.P60Hippocampus.subcluster.assign.RDS\",\n",
    "                        \"CB\": \"F_GRCm38.81.P60Cerebellum_ALT.subcluster.assign.RDS\"}\n",
    "dropviz_dgename_dict = {\"TH\": \"F_GRCm38.81.P60Thalamus.raw.dge.txt.gz\",\n",
    "                        \"HC\": \"F_GRCm38.81.P60Hippocampus.raw.dge.txt.gz\",\n",
    "                        \"CB\": \"F_GRCm38.81.P60Cerebellum_ALT.raw.dge.txt.gz\"}\n",
    "\n",
    "dropviz_anno = ro.r['readRDS'](datapath_Drop + \"/annotation.BrainCellAtlas_Saunders_version_2018.04.01.RDS\")\n",
    "ro.r.assign(\"dropviz_anno\", dropviz_anno)\n",
    "ro.r.assign(\"tissue\", tissue)\n",
    "ro.r('''dropviz_anno_class <- dropviz_anno[which(dropviz_anno$tissue == tissue), c(\"class\")]''')\n",
    "dropviz_anno_class = list(ro.r(\"dropviz_anno_class\"))\n",
    "ro.r('''dropviz_anno_class_marker <- dropviz_anno[which(dropviz_anno$tissue == tissue), c(\"class_marker\")]''')\n",
    "dropviz_anno_class_marker = list(ro.r(\"dropviz_anno_class_marker\"))\n",
    "ro.r('''dropviz_anno_subcluster <- dropviz_anno[which(dropviz_anno$tissue == tissue), c(\"subcluster\")]''')\n",
    "dropviz_anno_subcluster = list(ro.r(\"dropviz_anno_subcluster\"))\n",
    "\n",
    "subcluster_assign = ro.r['readRDS'](datapath_Drop + \"/\" + dropviz_RDSname_dict[tissue])\n",
    "ro.r.assign(\"subcluster_assign\", subcluster_assign)\n",
    "ro.r(\"subcluster_assign_name <- names(subcluster_assign)\")\n",
    "ro.r(\"subcluster_assign <- as.vector(subcluster_assign)\")\n",
    "subcluster_assign_name = list(ro.r(\"subcluster_assign_name\"))\n",
    "subcluster_assign = list(ro.r(\"subcluster_assign\"))\n",
    "\n",
    "meta_dropseq_dict = pd.DataFrame({\"class\": dropviz_anno_class, \"marker\": dropviz_anno_class_marker}, index=dropviz_anno_subcluster)\n",
    "meta_dropseq_dict[\"subclass\"] = meta_dropseq_dict[\"class\"] + \" (\" + meta_dropseq_dict[\"marker\"] + \")\"\n",
    "\n",
    "meta_dropseq = pd.DataFrame({\"subcluster\": subcluster_assign}, index=subcluster_assign_name)\n",
    "meta_dropseq[\"drop_class\"] = meta_dropseq_dict.loc[list(meta_dropseq[\"subcluster\"].values)][\"class\"].values\n",
    "meta_dropseq[\"drop_subclass\"] = meta_dropseq_dict.loc[list(meta_dropseq[\"subcluster\"].values)][\"subclass\"].values\n",
    "meta_dropseq[\"drop_subcluster\"] = (meta_dropseq[\"drop_class\"] + \"-\" + meta_dropseq[\"subcluster\"]).astype(str)\n",
    "\n",
    "ro.r['library'](\"DropSeq.util\")\n",
    "ro.r['library'](\"Seurat\")\n",
    "ro.r['library'](\"SingleCellExperiment\")\n",
    "ro.r.assign(\"dge.path\", datapath_Drop + \"/\" + dropviz_dgename_dict[tissue])\n",
    "ro.r('''dge <- loadSparseDge(dge.path) ''')\n",
    "ro.r(\"sce <- CreateSeuratObject(counts = dge)\")\n",
    "ro.r(\"sce <- as.SingleCellExperiment(sce)\")\n",
    "\n",
    "anndata2ri.activate()\n",
    "adata_dropseq = ro.r('as(sce, \"SingleCellExperiment\")')\n",
    "adata_dropseq = adata_dropseq[meta_dropseq.index]\n",
    "\n",
    "\n",
    "# 10X\n",
    "\n",
    "datapath_10X = \"./Raw_data_Zeisel\"\n",
    "\n",
    "tissuename_10X_dict = {\"TH\": \"Thal\", \"HC\": \"HC\", \"CB\": \"CB\"}\n",
    "\n",
    "adata_10X = sc.read_loom(datapath_10X + \"/l5_all.loom\")\n",
    "\n",
    "meta_10X = adata_10X.obs[[\"Tissue\", \"Class\", \"Clusters\"]]\n",
    "meta_10X[\"subclass\"] = meta_10X[\"Class\"] + \"_\" + meta_10X[\"Clusters\"].astype(str)\n",
    "\n",
    "sub_meta_10X = meta_10X[meta_10X.Tissue == tissuename_10X_dict[tissue]]\n",
    "sub_adata_10X = adata_10X[meta_10X.Tissue == tissuename_10X_dict[tissue]]\n",
    "\n",
    "sub_adata_10X = sub_adata_10X[~sub_adata_10X.obs.index.duplicated(), ~sub_adata_10X.var.index.duplicated()]\n",
    "sub_meta_10X = sub_meta_10X[~sub_meta_10X.index.duplicated()]\n",
    "\n",
    "adata_10X = sub_adata_10X\n",
    "\n",
    "meta_dropseq[\"method\"] = \"Drop\"\n",
    "sub_meta_10X[\"method\"] = \"10X\"\n",
    "meta = pd.concat([meta_dropseq, sub_meta_10X])\n",
    "meta.to_pickle(os.path.join(data_path, \"meta_raw.pkl\"))\n",
    "\n",
    "adata_dropseq.obs = adata_dropseq.obs[['ident']]\n",
    "adata_dropseq.write(filename=os.path.join(data_path, \"adata_dropseq.h5ad\"))\n",
    "sub_adata_10X.write(filename=os.path.join(data_path, \"adata_10X.h5ad\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04988733",
   "metadata": {},
   "source": [
    "# Integration using Portal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "beea9d65",
   "metadata": {},
   "outputs": [],
   "source": [
    "import portal\n",
    "\n",
    "# Specify the GPU device\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "\n",
    "# Create a folder for saving results\n",
    "result_path = \"result\"\n",
    "if not os.path.exists(result_path):\n",
    "    os.makedirs(result_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "25f12b3e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "If you pass `n_top_genes`, all cutoffs are ignored.\n",
      "extracting highly variable genes\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finding highly variable genes...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "--> added\n",
      "    'highly_variable', boolean vector (adata.var)\n",
      "    'highly_variable_rank', float vector (adata.var)\n",
      "    'means', float vector (adata.var)\n",
      "    'variances', float vector (adata.var)\n",
      "    'variances_norm', float vector (adata.var)\n",
      "If you pass `n_top_genes`, all cutoffs are ignored.\n",
      "extracting highly variable genes\n",
      "--> added\n",
      "    'highly_variable', boolean vector (adata.var)\n",
      "    'highly_variable_rank', float vector (adata.var)\n",
      "    'means', float vector (adata.var)\n",
      "    'variances', float vector (adata.var)\n",
      "    'variances_norm', float vector (adata.var)\n",
      "normalizing counts per cell\n",
      "    finished (0:00:00)\n",
      "/home/jzhaoaz/.local/lib/python3.7/site-packages/scanpy/preprocessing/_simple.py:845: UserWarning: Revieved a view of an AnnData. Making a copy.\n",
      "  view_to_actual(adata)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalizing and scaling...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "... as `zero_center=True`, sparse input is densified and may lead to large memory consumption\n",
      "normalizing counts per cell\n",
      "    finished (0:00:00)\n",
      "... as `zero_center=True`, sparse input is densified and may lead to large memory consumption\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensionality reduction via PCA...\n",
      "Begining time:  Thu Mar 17 20:41:36 2022\n",
      "step 0, loss_D=9.556219, loss_GAN=2.906622, loss_AE=260.116089, loss_cos=29.515400, loss_LA=207.128677\n",
      "step 200, loss_D=2.598508, loss_GAN=3.560721, loss_AE=9.093377, loss_cos=5.291987, loss_LA=2.438880\n",
      "step 400, loss_D=2.475666, loss_GAN=3.091831, loss_AE=5.584288, loss_cos=3.221293, loss_LA=1.344176\n",
      "step 600, loss_D=2.809396, loss_GAN=2.995210, loss_AE=4.604313, loss_cos=2.587498, loss_LA=1.096710\n",
      "step 800, loss_D=2.482383, loss_GAN=3.091920, loss_AE=3.773004, loss_cos=2.127327, loss_LA=0.725290\n",
      "step 1000, loss_D=2.477369, loss_GAN=3.009437, loss_AE=3.068430, loss_cos=2.102551, loss_LA=0.495042\n",
      "step 1200, loss_D=2.446446, loss_GAN=2.612375, loss_AE=2.606275, loss_cos=1.659568, loss_LA=0.385495\n",
      "step 1400, loss_D=2.738665, loss_GAN=2.848250, loss_AE=2.812898, loss_cos=1.880576, loss_LA=0.394464\n",
      "step 1600, loss_D=2.547716, loss_GAN=2.761175, loss_AE=2.700958, loss_cos=1.876387, loss_LA=0.434538\n",
      "step 1800, loss_D=2.732641, loss_GAN=2.664747, loss_AE=2.907195, loss_cos=1.667665, loss_LA=0.376585\n",
      "Ending time:  Thu Mar 17 20:42:23 2022\n",
      "Training takes 47.05 seconds\n",
      "Begining time:  Thu Mar 17 20:42:23 2022\n",
      "Ending time:  Thu Mar 17 20:42:23 2022\n",
      "Evaluating takes 0.06 seconds\n"
     ]
    }
   ],
   "source": [
    "model = portal.model.Model(training_steps=2000, lambdacos=15.0)\n",
    "model.preprocess(adata_dropseq, adata_10X) # perform preprocess and PCA\n",
    "model.train() # train the model\n",
    "model.eval() # get integrated latent representation of cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "076fa44f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UMAP(angular_rp_forest=True, local_connectivity=1, metric='correlation',\n",
      "     min_dist=0.3, n_neighbors=30, random_state=1234, repulsion_strength=1,\n",
      "     verbose=True)\n",
      "Construct fuzzy simplicial set\n",
      "Thu Mar 17 20:42:23 2022 Finding Nearest Neighbors\n",
      "Thu Mar 17 20:42:23 2022 Building RP forest with 12 trees\n",
      "Thu Mar 17 20:42:25 2022 NN descent for 14 iterations\n",
      "\t 0  /  14\n",
      "\t 1  /  14\n",
      "\t 2  /  14\n",
      "\t 3  /  14\n",
      "\t 4  /  14\n",
      "\t 5  /  14\n",
      "\t 6  /  14\n",
      "Thu Mar 17 20:42:39 2022 Finished Nearest Neighbor Search\n",
      "Thu Mar 17 20:42:41 2022 Construct embedding\n",
      "\tcompleted  0  /  200 epochs\n",
      "\tcompleted  20  /  200 epochs\n",
      "\tcompleted  40  /  200 epochs\n",
      "\tcompleted  60  /  200 epochs\n",
      "\tcompleted  80  /  200 epochs\n",
      "\tcompleted  100  /  200 epochs\n",
      "\tcompleted  120  /  200 epochs\n",
      "\tcompleted  140  /  200 epochs\n",
      "\tcompleted  160  /  200 epochs\n",
      "\tcompleted  180  /  200 epochs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "... storing 'subcluster' as categorical\n",
      "... storing 'drop_class' as categorical\n",
      "... storing 'drop_subclass' as categorical\n",
      "... storing 'drop_subcluster' as categorical\n",
      "... storing 'method' as categorical\n",
      "... storing 'Tissue' as categorical\n",
      "... storing 'Class' as categorical\n",
      "... storing 'subclass' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thu Mar 17 20:43:06 2022 Finished embedding\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The integration result can be visualized by portal.utils.plot_UMAP\n",
    "portal.utils.plot_UMAP(model.latent, meta, colors=[\"method\", \"drop_subcluster\", \"subclass\"], save=True, result_path=result_path)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}