{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "\n",
    "import scanpy as sc\n",
    "import anndata as ad\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import anndata2ri\n",
    "import gdown\n",
    "import scipy\n",
    "import scipy.io\n",
    "from rpy2.robjects import r\n",
    "\n",
    "anndata2ri.activate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%R\n",
    "suppressPackageStartupMessages({\n",
    "    library(SingleCellExperiment)\n",
    "    library(Seurat)\n",
    "    library(SeuratDisk)\n",
    "    library(Signac)\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Expressions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1i5Yj7Hh6ZYn9MpJ5hikOqA-SvnYK4zvQ\n",
      "To: /mnt/home/icb/anastasia.litinetskaya/multigrate/example/cao2018-expressions.h5ad\n",
      "19.5MB [00:00, 78.6MB/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'cao2018-expressions.h5ad'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdown.download(\n",
    "    'https://drive.google.com/uc?id=1i5Yj7Hh6ZYn9MpJ5hikOqA-SvnYK4zvQ',\n",
    "    'cao2018-expressions.h5ad'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata = sc.read('cao2018-expressions.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_ = ad.AnnData(adata.layers['count'])\n",
    "adata_.obs_names = adata.obs_names\n",
    "adata_.var_names = adata.var_names\n",
    "adata_.obs['cell_type'] = adata.obs['cell_type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "An object of class Seurat \n",
      "4000 features across 7362 samples within 1 assay \n",
      "Active assay: RNA (4000 features, 0 variable features)\n"
     ]
    }
   ],
   "source": [
    "%%R -i adata_\n",
    "rna = as.Seurat(adata_, counts='X', data=NULL)\n",
    "rna"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Peaks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1BujbHCgZxjS2K7lxxQ1XXLQJ2lPDnpZN\n",
      "To: /mnt/home/icb/anastasia.litinetskaya/multigrate/example/cao2018-peaks.h5ad\n",
      "34.2MB [00:00, 105MB/s] \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'cao2018-peaks.h5ad'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdown.download(\n",
    "    'https://drive.google.com/uc?id=1BujbHCgZxjS2K7lxxQ1XXLQJ2lPDnpZN',\n",
    "    'cao2018-peaks.h5ad'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata = sc.read('cao2018-peaks.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_ = ad.AnnData(adata.layers['binary'])\n",
    "adata_.obs_names = adata.obs_names\n",
    "adata_.var_names = adata.var_names\n",
    "adata_.obs['cell_type'] = adata.obs['cell_type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Warning:\n",
      "R[write to console]:  Feature names cannot have underscores ('_'), replacing with dashes ('-')\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "An object of class Seurat \n",
      "41524 features across 7362 samples within 1 assay \n",
      "Active assay: RNA (41524 features, 0 variable features)\n"
     ]
    }
   ],
   "source": [
    "%%R -i adata_\n",
    "peaks = as.Seurat(adata_, counts='X', data=NULL)\n",
    "peaks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run Seurat v4 WNN\n",
    "Following https://satijalab.org/seurat/v4.0/weighted_nearest_neighbor_analysis.html."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Preprocessing RNA...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Warning:\n",
      "R[write to console]:  The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric\n",
      "To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'\n",
      "This message will be shown once per session\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Preprocessing ATAC...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Calculating cell-specific modality weights\n",
      "\n",
      "R[write to console]: Finding 20 nearest neighbors for each modality.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  |                                                  | 0 % ~calculating   |+++++++++++++++++++++++++                         | 50% ~03s           |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=06s  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Calculating kernel bandwidths\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  |                                                  | 0 % ~calculating   |+++++++++++++++++++++++++                         | 50% ~00s           |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Finding multimodal neighbors\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  |                                                  | 0 % ~calculating   |+++++++++++++++++++++++++                         | 50% ~09s           |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=20s  \n",
      "  |                                                  | 0 % ~calculating   |+++++++++++++++++++++++++                         | 50% ~01s           |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Constructing multimodal KNN graph\n",
      "\n",
      "R[write to console]: Constructing multimodal SNN graph\n",
      "\n",
      "R[write to console]: Computing sPCA transformation\n",
      "\n",
      "R[write to console]: Creating h5Seurat file for version 3.1.5.9900\n",
      "\n",
      "R[write to console]: Adding counts for RNA\n",
      "\n",
      "R[write to console]: Adding data for RNA\n",
      "\n",
      "R[write to console]: No variable features found for RNA\n",
      "\n",
      "R[write to console]: No feature-level metadata found for RNA\n",
      "\n",
      "R[write to console]: Adding counts for ATAC\n",
      "\n",
      "R[write to console]: Adding data for ATAC\n",
      "\n",
      "R[write to console]: Adding variable features for ATAC\n",
      "\n",
      "R[write to console]: Adding feature-level metadata for ATAC\n",
      "\n",
      "R[write to console]: Adding counts for SCT\n",
      "\n",
      "R[write to console]: Adding data for SCT\n",
      "\n",
      "R[write to console]: Adding scale.data for SCT\n",
      "\n",
      "R[write to console]: Adding variable features for SCT\n",
      "\n",
      "R[write to console]: No feature-level metadata found for SCT\n",
      "\n",
      "R[write to console]: Writing out SCTModel.list for SCT\n",
      "\n",
      "R[write to console]: Adding cell embeddings for pca\n",
      "\n",
      "R[write to console]: Adding loadings for pca\n",
      "\n",
      "R[write to console]: No projected loadings for pca\n",
      "\n",
      "R[write to console]: Adding standard deviations for pca\n",
      "\n",
      "R[write to console]: No JackStraw data for pca\n",
      "\n",
      "R[write to console]: Adding cell embeddings for umap.rna\n",
      "\n",
      "R[write to console]: No loadings for umap.rna\n",
      "\n",
      "R[write to console]: No projected loadings for umap.rna\n",
      "\n",
      "R[write to console]: No standard deviations for umap.rna\n",
      "\n",
      "R[write to console]: No JackStraw data for umap.rna\n",
      "\n",
      "R[write to console]: Adding cell embeddings for lsi\n",
      "\n",
      "R[write to console]: Adding loadings for lsi\n",
      "\n",
      "R[write to console]: No projected loadings for lsi\n",
      "\n",
      "R[write to console]: Adding standard deviations for lsi\n",
      "\n",
      "R[write to console]: No JackStraw data for lsi\n",
      "\n",
      "R[write to console]: Adding cell embeddings for umap.atac\n",
      "\n",
      "R[write to console]: No loadings for umap.atac\n",
      "\n",
      "R[write to console]: No projected loadings for umap.atac\n",
      "\n",
      "R[write to console]: No standard deviations for umap.atac\n",
      "\n",
      "R[write to console]: No JackStraw data for umap.atac\n",
      "\n",
      "R[write to console]: Adding cell embeddings for spca\n",
      "\n",
      "R[write to console]: Adding loadings for spca\n",
      "\n",
      "R[write to console]: No projected loadings for spca\n",
      "\n",
      "R[write to console]: Adding standard deviations for spca\n",
      "\n",
      "R[write to console]: No JackStraw data for spca\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "bm <- rna\n",
    "bm[[\"ATAC\"]] <- CreateAssayObject(counts = peaks@assays$RNA@counts)\n",
    "\n",
    "rm(rna)\n",
    "rm(peaks)\n",
    "\n",
    "print('Preprocessing RNA...')\n",
    "DefaultAssay(bm) <- \"RNA\"\n",
    "bm <- SCTransform(bm, variable.features.n = 4000, verbose=FALSE)\n",
    "bm <- RunPCA(bm, verbose=FALSE)\n",
    "bm <- RunUMAP(bm, dims = 1:50, reduction.name = 'umap.rna', reduction.key = 'rnaUMAP_', verbose=FALSE)\n",
    "\n",
    "print('Preprocessing ATAC...')\n",
    "DefaultAssay(bm) <- \"ATAC\"\n",
    "bm <- RunTFIDF(bm, verbose=FALSE)\n",
    "bm <- FindTopFeatures(bm, min.cutoff = 'q0', verbose=FALSE)\n",
    "bm <- RunSVD(bm, verbose=FALSE)\n",
    "bm <- RunUMAP(bm, reduction = 'lsi', dims = 2:50, reduction.name = \"umap.atac\", \n",
    "              reduction.key = \"atacUMAP_\", verbose=FALSE)\n",
    "\n",
    "bm <- FindMultiModalNeighbors(bm, reduction.list = list(\"pca\", \"lsi\"), \n",
    "                              dims.list = list(1:50, 2:50), modality.weight.name = \"RNA.weight\")\n",
    "\n",
    "bm <- RunSPCA(bm, assay = 'SCT', graph = 'wsnn', npcs = 20)\n",
    "\n",
    "SaveH5Seurat(bm, 'cao-seurat.h5seurat', overwrite = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Validating h5Seurat file\n",
      "\n",
      "R[write to console]: Adding scale.data from SCT as X\n",
      "\n",
      "R[write to console]: Adding data from SCT as raw\n",
      "\n",
      "R[write to console]: Transfering meta.data to obs\n",
      "\n",
      "R[write to console]: Adding dimensional reduction information for pca\n",
      "\n",
      "R[write to console]: Adding feature loadings for pca\n",
      "\n",
      "R[write to console]: Adding dimensional reduction information for spca\n",
      "\n",
      "R[write to console]: Adding feature loadings for spca\n",
      "\n",
      "R[write to console]: Adding dimensional reduction information for umap.rna\n",
      "\n",
      "R[write to console]: Adding dimensional reduction information for umap.atac (global)\n",
      "\n",
      "R[write to console]: Adding wsnn as neighbors\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "# wsnn graph is stored in SCT assay\n",
    "Convert(\"cao-seurat.h5seurat\", assay=\"SCT\", \"cao-seurat.h5ad\", overwrite=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 7362 × 3810\n",
       "    obs: 'cell_type', 'nCount_ATAC', 'nFeature_ATAC', 'nCount_SCT', 'nFeature_SCT', 'SCT.weight', 'ATAC.weight'\n",
       "    var: 'features'\n",
       "    uns: 'neighbors'\n",
       "    obsm: 'X_pca', 'X_spca', 'X_umap.atac', 'X_umap.rna'\n",
       "    varm: 'PCs', 'SPCA'\n",
       "    obsp: 'distances'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "integrated = sc.read('cao-seurat.h5ad')\n",
    "integrated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "latent = sc.AnnData(integrated.obsm['X_spca'])\n",
    "latent.obs = integrated.obs\n",
    "latent.obsp['connectivities'] = integrated.uns['neighbors']['distances']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "latent.write('../data/integrated/seurat/cao-seurat.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "!rm 'cao2018-expressions.h5ad'\n",
    "!rm 'cao2018-peaks.h5ad'\n",
    "!rm 'cao-seurat.h5seurat'\n",
    "!rm 'cao-seurat.h5ad'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R version 4.0.3 (2020-10-10)\n",
      "Platform: x86_64-conda-linux-gnu (64-bit)\n",
      "Running under: CentOS Linux 7 (Core)\n",
      "\n",
      "Matrix products: default\n",
      "BLAS/LAPACK: /mnt/home/icb/anastasia.litinetskaya/miniconda3/envs/signac/lib/libopenblasp-r0.3.10.so\n",
      "\n",
      "locale:\n",
      " [1] LC_CTYPE=C                 LC_NUMERIC=C              \n",
      " [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n",
      " [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n",
      " [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n",
      " [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n",
      "[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n",
      "\n",
      "attached base packages:\n",
      " [1] parallel  stats4    tools     stats     graphics  grDevices utils    \n",
      " [8] datasets  methods   base     \n",
      "\n",
      "other attached packages:\n",
      " [1] Matrix_1.3-2                Signac_1.1.1               \n",
      " [3] SeuratDisk_0.0.0.9018       SeuratObject_4.0.0         \n",
      " [5] Seurat_4.0.0                SingleCellExperiment_1.12.0\n",
      " [7] SummarizedExperiment_1.20.0 Biobase_2.50.0             \n",
      " [9] GenomicRanges_1.42.0        GenomeInfoDb_1.26.0        \n",
      "[11] IRanges_2.24.0              S4Vectors_0.28.0           \n",
      "[13] BiocGenerics_0.36.0         MatrixGenerics_1.2.0       \n",
      "[15] matrixStats_0.58.0         \n",
      "\n",
      "loaded via a namespace (and not attached):\n",
      "  [1] utf8_1.1.4               reticulate_1.18          tidyselect_1.1.0        \n",
      "  [4] RSQLite_2.2.3            AnnotationDbi_1.52.0     htmlwidgets_1.5.3       \n",
      "  [7] grid_4.0.3               BiocParallel_1.24.0      Rtsne_0.15              \n",
      " [10] munsell_0.5.0            codetools_0.2-18         ica_1.0-2               \n",
      " [13] future_1.21.0            miniUI_0.1.1.1           withr_2.4.1             \n",
      " [16] colorspace_2.0-0         OrganismDbi_1.32.0       knitr_1.31              \n",
      " [19] rstudioapi_0.13          ROCR_1.0-11              tensor_1.5              \n",
      " [22] listenv_0.8.0            GenomeInfoDbData_1.2.4   polyclip_1.10-0         \n",
      " [25] farver_2.1.0             bit64_4.0.5              parallelly_1.23.0       \n",
      " [28] vctrs_0.3.6              generics_0.1.0           xfun_0.20               \n",
      " [31] biovizBase_1.38.0        BiocFileCache_1.14.0     lsa_0.73.2              \n",
      " [34] ggseqlogo_0.1            R6_2.5.0                 hdf5r_1.3.3             \n",
      " [37] AnnotationFilter_1.14.0  reshape_0.8.8            bitops_1.0-6            \n",
      " [40] spatstat.utils_2.0-0     cachem_1.0.4             DelayedArray_0.16.0     \n",
      " [43] assertthat_0.2.1         promises_1.2.0.1         scales_1.1.1            \n",
      " [46] nnet_7.3-15              gtable_0.3.0             globals_0.14.0          \n",
      " [49] goftest_1.2-2            ggbio_1.38.0             ensembldb_2.14.0        \n",
      " [52] rlang_0.4.10             RcppRoll_0.3.0           splines_4.0.3           \n",
      " [55] rtracklayer_1.50.0       lazyeval_0.2.2           dichromat_2.0-0         \n",
      " [58] checkmate_2.0.0          BiocManager_1.30.10      reshape2_1.4.4          \n",
      " [61] abind_1.4-5              GenomicFeatures_1.42.0   backports_1.2.1         \n",
      " [64] httpuv_1.5.5             Hmisc_4.5-0              RBGL_1.66.0             \n",
      " [67] ggplot2_3.3.3            ellipsis_0.3.1           RColorBrewer_1.1-2      \n",
      " [70] ggridges_0.5.3           Rcpp_1.0.6               plyr_1.8.6              \n",
      " [73] base64enc_0.1-3          progress_1.2.2           zlibbioc_1.36.0         \n",
      " [76] purrr_0.3.4              RCurl_1.98-1.2           prettyunits_1.1.1       \n",
      " [79] rpart_4.1-15             openssl_1.4.3            deldir_0.2-10           \n",
      " [82] pbapply_1.4-3            cowplot_1.1.1            zoo_1.8-8               \n",
      " [85] ggrepel_0.9.1            cluster_2.1.1            magrittr_2.0.1          \n",
      " [88] RSpectra_0.16-0          data.table_1.14.0        scattermore_0.7         \n",
      " [91] lmtest_0.9-38            RANN_2.6.1               SnowballC_0.7.0         \n",
      " [94] ProtGenerics_1.22.0      fitdistrplus_1.1-3       hms_1.0.0               \n",
      " [97] patchwork_1.1.1          mime_0.10                xtable_1.8-4            \n",
      "[100] XML_3.99-0.5             jpeg_0.1-8.1             gridExtra_2.3           \n",
      "[103] compiler_4.0.3           biomaRt_2.46.0           tibble_3.1.0            \n",
      "[106] KernSmooth_2.23-18       crayon_1.4.1             htmltools_0.5.1.1       \n",
      "[109] mgcv_1.8-34              later_1.1.0.1            Formula_1.2-4           \n",
      "[112] tidyr_1.1.3              DBI_1.1.1                tweenr_1.0.1            \n",
      "[115] dbplyr_2.1.0             MASS_7.3-53.1            rappdirs_0.3.3          \n",
      "[118] cli_2.3.1                igraph_1.2.6             pkgconfig_2.0.3         \n",
      "[121] GenomicAlignments_1.26.0 foreign_0.8-81           plotly_4.9.3            \n",
      "[124] xml2_1.3.2               XVector_0.30.0           stringr_1.4.0           \n",
      "[127] VariantAnnotation_1.36.0 digest_0.6.27            sctransform_0.3.2       \n",
      "[130] RcppAnnoy_0.0.18         graph_1.68.0             spatstat.data_2.0-0     \n",
      "[133] Biostrings_2.58.0        fastmatch_1.1-0          leiden_0.3.7            \n",
      "[136] htmlTable_2.1.0          uwot_0.1.10              curl_4.3                \n",
      "[139] shiny_1.6.0              Rsamtools_2.6.0          lifecycle_1.0.0         \n",
      "[142] nlme_3.1-152             jsonlite_1.7.2           viridisLite_0.3.0       \n",
      "[145] askpass_1.1              BSgenome_1.58.0          fansi_0.4.2             \n",
      "[148] pillar_1.5.0             GGally_2.1.0             lattice_0.20-41         \n",
      "[151] fastmap_1.1.0            httr_1.4.2               survival_3.2-7          \n",
      "[154] glue_1.4.2               spatstat_1.64-1          png_0.1-7               \n",
      "[157] bit_4.0.4                ggforce_0.3.2            stringi_1.5.3           \n",
      "[160] blob_1.2.1               latticeExtra_0.6-29      memoise_2.0.0           \n",
      "[163] dplyr_1.0.4              irlba_2.3.3              future.apply_1.7.0      \n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "sessionInfo()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(latent.obs_names != adata.obs_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "latent.obs['cell_type'] = adata.obs['cell_type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pp.neighbors(latent)\n",
    "sc.tl.umap(latent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.umap(latent, color='cell_type', frameon=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}