{
 "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": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 6334 × 4000\n",
       "    obs: 'n_genes', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'total_counts_mt', 'log1p_total_counts_mt', 'pct_counts_mt', 'cell_type', 'batch'\n",
       "    var: 'gene_ids', 'feature_types', 'genome', 'mt', 'n_cells-0', 'n_cells_by_counts-0', 'mean_counts-0', 'log1p_mean_counts-0', 'pct_dropout_by_counts-0', 'total_counts-0', 'log1p_total_counts-0', 'highly_variable-0', 'means-0', 'dispersions-0', 'dispersions_norm-0', 'n_cells-1', 'n_cells_by_counts-1', 'mean_counts-1', 'log1p_mean_counts-1', 'pct_dropout_by_counts-1', 'total_counts-1', 'log1p_total_counts-1', 'highly_variable-1', 'means-1', 'dispersions-1', 'dispersions_norm-1', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'\n",
       "    uns: 'hvg'\n",
       "    obsm: 'X_pca'\n",
       "    layers: 'count'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata = sc.read('../data/sagner-2021/expressions_hvg.h5ad')\n",
    "adata"
   ]
  },
  {
   "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']\n",
    "adata_.obs['batch'] = adata.obs['batch']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "An object of class Seurat \n",
      "4000 features across 6334 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": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 6334 × 38313\n",
       "    obs: 'nb_features', 'cell_type', 'batch'\n",
       "    var: 'feature_types', 'genome', 'gene_ids-0', 'n_cells-0', 'commonness-0', 'prop_shared_cells-0', 'variability_score-0', 'gene_ids-1', 'n_cells-1', 'commonness-1', 'prop_shared_cells-1', 'variability_score-1'\n",
       "    layers: 'binary'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata = sc.read('../data/sagner-2021/peaks.h5ad')\n",
    "adata"
   ]
  },
  {
   "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']\n",
    "adata_.obs['batch'] = adata.obs['batch']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "An object of class Seurat \n",
      "38313 features across 6334 samples within 1 assay \n",
      "Active assay: RNA (38313 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": [
    "# Integrate RNA batches\n",
    "Following https://satijalab.org/seurat/articles/integration_introduction.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Calculating cell attributes from input UMI matrix: log_umi\n",
      "\n",
      "R[write to console]: Variance stabilizing transformation of count matrix of size 3483 by 2859\n",
      "\n",
      "R[write to console]: Model formula is y ~ log_umi\n",
      "\n",
      "R[write to console]: Get Negative Binomial regression parameters per gene\n",
      "\n",
      "R[write to console]: Using 2000 genes, 2859 cells\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  |======================================================================| 100%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Found 1 outliers - those will be ignored in fitting/regularization step\n",
      "\n",
      "\n",
      "R[write to console]: Second step: Get residuals using fitted parameters for 3483 genes\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  |======================================================================| 100%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Computing corrected count matrix for 3483 genes\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  |======================================================================| 100%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Calculating gene attributes\n",
      "\n",
      "R[write to console]: Wall clock passed: Time difference of 1.704511 mins\n",
      "\n",
      "R[write to console]: Determine variable features\n",
      "\n",
      "R[write to console]: Place corrected count matrix in counts slot\n",
      "\n",
      "R[write to console]: Centering data matrix\n",
      "\n",
      "  |                                                                            \n",
      "  |                                                                      |   0%\n",
      "  |                                                                            \n",
      "  |==============                                                        |  20%\n",
      "  |                                                                            \n",
      "  |============================                                          |  40%\n",
      "  |                                                                            \n",
      "  |==========================================                            |  60%\n",
      "  |                                                                            \n",
      "  |========================================================              |  80%\n",
      "  |                                                                            \n",
      "  |======================================================================| 100%\n",
      "R[write to console]: \n",
      "\n",
      "R[write to console]: Set default assay to SCT\n",
      "\n",
      "R[write to console]: Calculating cell attributes from input UMI matrix: log_umi\n",
      "\n",
      "R[write to console]: Variance stabilizing transformation of count matrix of size 3548 by 3475\n",
      "\n",
      "R[write to console]: Model formula is y ~ log_umi\n",
      "\n",
      "R[write to console]: Get Negative Binomial regression parameters per gene\n",
      "\n",
      "R[write to console]: Using 2000 genes, 3475 cells\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  |======================================================================| 100%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Found 2 outliers - those will be ignored in fitting/regularization step\n",
      "\n",
      "\n",
      "R[write to console]: Second step: Get residuals using fitted parameters for 3548 genes\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  |======================================================================| 100%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Computing corrected count matrix for 3548 genes\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  |======================================================================| 100%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Calculating gene attributes\n",
      "\n",
      "R[write to console]: Wall clock passed: Time difference of 1.74686 mins\n",
      "\n",
      "R[write to console]: Determine variable features\n",
      "\n",
      "R[write to console]: Place corrected count matrix in counts slot\n",
      "\n",
      "R[write to console]: Centering data matrix\n",
      "\n",
      "  |                                                                            \n",
      "  |                                                                      |   0%\n",
      "  |                                                                            \n",
      "  |==============                                                        |  20%\n",
      "  |                                                                            \n",
      "  |============================                                          |  40%\n",
      "  |                                                                            \n",
      "  |==========================================                            |  60%\n",
      "  |                                                                            \n",
      "  |========================================================              |  80%\n",
      "  |                                                                            \n",
      "  |======================================================================| 100%\n",
      "R[write to console]: \n",
      "\n",
      "R[write to console]: Set default assay to SCT\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  |                                                  | 0 % ~calculating   |+++++++++++++++++++++++++                         | 50% ~03s           |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=07s  \n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "rna.list <- SplitObject(rna, split.by = \"batch\")\n",
    "rna.list <- lapply(X = rna.list, FUN = SCTransform, variable.features.n = 4000)\n",
    "features <- SelectIntegrationFeatures(object.list = rna.list, nfeatures = 4000)\n",
    "rna.list <- PrepSCTIntegration(object.list = rna.list, anchor.features = features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Finding all pairwise anchors\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  |                                                  | 0 % ~calculating  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Running CCA\n",
      "\n",
      "R[write to console]: Merging objects\n",
      "\n",
      "R[write to console]: Finding neighborhoods\n",
      "\n",
      "R[write to console]: Finding anchors\n",
      "\n",
      "R[write to console]: \tFound 8197 anchors\n",
      "\n",
      "R[write to console]: Filtering anchors\n",
      "\n",
      "R[write to console]: \tRetained 6134 anchors\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 13s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Merging dataset 1 into 2\n",
      "\n",
      "R[write to console]: Extracting anchors for merged samples\n",
      "\n",
      "R[write to console]: Finding integration vectors\n",
      "\n",
      "R[write to console]: Finding integration vector weights\n",
      "\n",
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      "R[write to console]: |\n",
      "\n",
      "R[write to console]: Integrating data\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "anchors <- FindIntegrationAnchors(object.list = rna.list, normalization.method = \"SCT\", \n",
    "    anchor.features = features)\n",
    "integrated <- IntegrateData(anchorset = anchors, normalization.method = \"SCT\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: PC_ 1 \n",
      "Positive:  PLXDC2, VCAN, SLC8A1, FCN1, LRMDA, RBM47, LYN, MCTP1, AC020916.1, DMXL2 \n",
      "\t   CSF3R, IRAK3, PSAP, SAT1, NEAT1, CD83, HDAC9, PLAUR, SLC11A1, MARCH1 \n",
      "\t   RRP12, CLEC12A, FGD4, HLA-DRA, PID1, LRRK2, CLEC7A, CREB5, GRK3, CST3 \n",
      "Negative:  SKAP1, CD247, PRKCH, BCL11B, CAMK4, RORA, IL7R, INPP4B, TC2N, SYNE2 \n",
      "\t   LEF1, CD96, THEMIS, STAT4, ANK3, TXK, GNLY, PRKCQ, ITK, BACH2 \n",
      "\t   TRAC, SLFN12L, BCL2, PRF1, CD69, ETS1, SCML4, LINC01934, NCALD, TCF7 \n",
      "PC_ 2 \n",
      "Positive:  BANK1, AFF3, CD79A, MS4A1, IGHM, EBF1, RALGPS2, PAX5, FCRL1, OSBPL10 \n",
      "\t   IGHD, BACH2, COL19A1, BLK, LINC00926, CD74, HLA-DQA1, LIX1-AS1, CD79B, ADAM28 \n",
      "\t   HLA-DQB1, HLA-DRA, HLA-DRB1, GNG7, COBLL1, NIBAN3, PLEKHG1, LARGE1, IGKC, KHDRBS2 \n",
      "Negative:  GNLY, PRF1, NKG7, AOAH, PLCB1, FGFBP2, ARHGAP26, SPON2, CCL5, KLRD1 \n",
      "\t   GZMA, CD247, GZMB, CST7, GZMH, TGFBR3, CTSW, BNC2, NEAT1, PRKCH \n",
      "\t   IL2RB, ADGRG1, NCALD, KLRF1, MYOM2, PPP2R2B, PLXDC2, FCGR3A, CCL4, PDGFD \n",
      "PC_ 3 \n",
      "Positive:  GNLY, PRF1, NKG7, FGFBP2, SPON2, GZMB, KLRD1, GZMH, BNC2, GZMA \n",
      "\t   CST7, ADGRG1, KLRF1, MYOM2, IL2RB, MCTP2, PDGFD, CCL5, AUTS2, BANK1 \n",
      "\t   CTSW, FCGR3A, NCAM1, TGFBR3, CCL4, C1orf21, PRSS23, GRIK4, PTGDS, MS4A1 \n",
      "Negative:  LEF1, CAMK4, INPP4B, IL7R, PRKCA, SERINC5, MAML2, PDE3B, NELL2, THEMIS \n",
      "\t   BCL11B, PLCL1, ANK3, TSHZ2, TRAC, TCF7, TRABD2A, CDC14A, CCR7, RCAN3 \n",
      "\t   ITK, OXNAD1, NR3C2, MLLT3, AC139720.1, FHIT, FKBP5, TC2N, MAL, RETREG1 \n",
      "PC_ 4 \n",
      "Positive:  LEF1, APBA2, TXK, FGFBP2, FCGR3A, BNC2, MYOM2, SPON2, FHIT, BACH2 \n",
      "\t   AK5, CCR7, IGF1R, GRIK4, GZMB, KLRF1, NELL2, ATP10A, CD7, PTGDS \n",
      "\t   SERINC5, LINC01505, TRABD2A, FCER1G, PRSS23, CX3CR1, IGFBP7, MAML2, TSHZ2, LDB2 \n",
      "Negative:  SLC4A10, AL136456.1, GZMK, A2M, ADAM12, PHACTR2, KLRG1, PZP, IFNG-AS1, A2M-AS1 \n",
      "\t   IL4I1, IL23R, SCART1, LINC01644, AGAP1, LTK, LINC00612, TRGC2, TRGC1, NIBAN1 \n",
      "\t   PLCB1, RORA, IL7R, CXCR6, CCL5, LINC00944, ME1, SYTL2, AC093865.1, IKZF2 \n",
      "PC_ 5 \n",
      "Positive:  VCAN, DYSF, PLCB1, CSF3R, CXCL8, LINC00937, CD36, NCF1, FCAR, LUCAT1 \n",
      "\t   CREB5, ARHGAP24, GLT1D1, EREG, LIN7A, CRISPLD2, NRG1, CD14, NLRP12, AC020916.1 \n",
      "\t   PADI4, ACSL1, TREM1, NLRP3, PROK2, ARHGAP26, CLMN, AL163541.1, OLR1, KIF13A \n",
      "Negative:  CDKN1C, FCGR3A, TCF7L2, FMNL2, PAPSS2, CSF1R, CCDC26, LYPD2, LST1, MTSS1 \n",
      "\t   MS4A7, IFITM3, C1QA, CST3, CFD, AC092546.1, SETBP1, FCER1G, PTPN13, SERPINA1 \n",
      "\t   HMOX1, WARS, SNED1, HLA-DPA1, P2RX1, IFI30, FTL, UBASH3B, PPP1R17, CX3CR1 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "integrated <- RunPCA(integrated)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Integrate ATAC-seq batches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Performing TF-IDF normalization\n",
      "\n",
      "R[write to console]: Performing TF-IDF normalization\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "peaks.list <- SplitObject(peaks, split.by = \"batch\")\n",
    "\n",
    "for (i in 1:length(peaks.list)) {\n",
    "    peaks.list[[i]] <- RunTFIDF(peaks.list[[i]])\n",
    "    peaks.list[[i]] <- FindTopFeatures(peaks.list[[i]], min.cutoff = 50)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Running SVD\n",
      "\n",
      "R[write to console]: Scaling cell embeddings\n",
      "\n",
      "R[write to console]: Running SVD\n",
      "\n",
      "R[write to console]: Scaling cell embeddings\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "for (i in 1:length(peaks.list)) {\n",
    "    peaks.list[[i]] <- RunSVD(peaks.list[[i]])\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Scaling features for provided objects\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  |                                                  | 0 % ~calculating   |+++++++++++++++++++++++++                         | 50% ~32s           |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 08s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Finding all pairwise anchors\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  |                                                  | 0 % ~calculating  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Running CCA\n",
      "\n",
      "R[write to console]: Merging objects\n",
      "\n",
      "R[write to console]: Finding neighborhoods\n",
      "\n",
      "R[write to console]: Finding anchors\n",
      "\n",
      "R[write to console]: \tFound 8779 anchors\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=56s  \n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "anchors <- FindIntegrationAnchors(\n",
    "  object.list = peaks.list,\n",
    "  anchor.features = rownames(peaks.list[[1]]),\n",
    "  k.filter = NA\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Merging dataset 2 into 1\n",
      "\n",
      "R[write to console]: Extracting anchors for merged samples\n",
      "\n",
      "R[write to console]: Finding integration vectors\n",
      "\n",
      "R[write to console]: Finding integration vector weights\n",
      "\n",
      "R[write to console]: 0%   10   20   30   40   50   60   70   80   90   100%\n",
      "\n",
      "R[write to console]: [----|----|----|----|----|----|----|----|----|----|\n",
      "\n",
      "R[write to console]: *\n",
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      "R[write to console]: *\n",
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      "R[write to console]: *\n",
      "R[write to console]: *\n",
      "R[write to console]: |\n",
      "\n",
      "R[write to console]: Integrating data\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "integrated_atac <- IntegrateData(\n",
    "  anchorset = anchors,\n",
    "  weight.reduction = 'lsi',\n",
    "  dims = 2:30,\n",
    "  preserve.order = TRUE\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Running SVD\n",
      "\n",
      "R[write to console]: Scaling cell embeddings\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "integrated_atac <- RunSVD(integrated_atac, n = 30, reduction.name = 'integratedLSI')"
   ]
  },
  {
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "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% ~04s           |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s  \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% ~01s           |++++++++++++++++++++++++++++++++++++++++++++++++++| 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% ~11s           |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=26s  \n",
      "  |                                                  | 0 % ~calculating   |+++++++++++++++++++++++++                         | 50% ~02s           |++++++++++++++++++++++++++++++++++++++++++++++++++| 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 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]: No variable features found 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 data for integrated\n",
      "\n",
      "R[write to console]: Adding scale.data for integrated\n",
      "\n",
      "R[write to console]: Adding variable features for integrated\n",
      "\n",
      "R[write to console]: No feature-level metadata found for integrated\n",
      "\n",
      "R[write to console]: Writing out SCTModel.list for integrated\n",
      "\n",
      "R[write to console]: Adding data for IATAC\n",
      "\n",
      "R[write to console]: Adding variable features for IATAC\n",
      "\n",
      "R[write to console]: No feature-level metadata found for IATAC\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 integratedLSI\n",
      "\n",
      "R[write to console]: Adding loadings for integratedLSI\n",
      "\n",
      "R[write to console]: No projected loadings for integratedLSI\n",
      "\n",
      "R[write to console]: Adding standard deviations for integratedLSI\n",
      "\n",
      "R[write to console]: No JackStraw data for integratedLSI\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",
    "integrated[[\"IATAC\"]] <- integrated_atac[[\"integrated\"]]\n",
    "integrated[[\"integratedLSI\"]] <- integrated_atac[[\"integratedLSI\"]]\n",
    "\n",
    "integrated <- FindMultiModalNeighbors(integrated, reduction.list = list(\"pca\", \"integratedLSI\"), \n",
    "                              dims.list = list(1:50, 2:30), modality.weight.name = \"RNA.weight\")\n",
    "\n",
    "integrated <- RunSPCA(integrated, assay = 'integrated', graph = 'wsnn', npcs = 20)\n",
    "\n",
    "SaveH5Seurat(integrated, 'sagner-seurat.h5seurat', overwrite = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# otherwise convertion to an AnnData object throws errors\n",
    "%%R\n",
    "integrated[[\"integratedLSI\"]] <- NULL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Warning:\n",
      "R[write to console]:  Overwriting previous file sagner-seurat.h5seurat\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 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]: No variable features found 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 data for integrated\n",
      "\n",
      "R[write to console]: Adding scale.data for integrated\n",
      "\n",
      "R[write to console]: Adding variable features for integrated\n",
      "\n",
      "R[write to console]: No feature-level metadata found for integrated\n",
      "\n",
      "R[write to console]: Writing out SCTModel.list for integrated\n",
      "\n",
      "R[write to console]: Adding data for IATAC\n",
      "\n",
      "R[write to console]: Adding variable features for IATAC\n",
      "\n",
      "R[write to console]: No feature-level metadata found for IATAC\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 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",
    "SaveH5Seurat(integrated, 'sagner-seurat.h5seurat', overwrite = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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 integrated as X\n",
      "\n",
      "R[write to console]: Adding data from integrated 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 wsnn as neighbors\n",
      "\n",
      "R[write to console]: Adding scale.data from SCT as a layer\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "Convert(\"sagner-seurat.h5seurat\", assay=\"integrated\", \"sagner-seurat.h5ad\", overwrite=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 6334 × 3361\n",
       "    obs: 'cell_type', 'batch', 'nCount_RNA', 'nFeature_RNA', 'nCount_SCT', 'nFeature_SCT', 'integrated.weight'\n",
       "    var: 'features', 'SCT_features'\n",
       "    uns: 'neighbors'\n",
       "    obsm: 'X_pca', 'X_spca'\n",
       "    varm: 'PCs', 'SPCA'\n",
       "    layers: 'SCT'\n",
       "    obsp: 'distances'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "integrated = sc.read('sagner-seurat.h5ad')\n",
    "integrated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "... storing 'cell_type' as categorical\n",
      "... storing 'batch' as categorical\n"
     ]
    }
   ],
   "source": [
    "latent.write('../data/integrated/seurat/sagner-seurat.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "!rm 'sagner-seurat.h5seurat'\n",
    "!rm 'sagner-seurat.h5ad'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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] data.table_1.14.0        scattermore_0.7          lmtest_0.9-38           \n",
      " [91] RANN_2.6.1               SnowballC_0.7.0          ProtGenerics_1.22.0     \n",
      " [94] fitdistrplus_1.1-3       hms_1.0.0                patchwork_1.1.1         \n",
      " [97] mime_0.10                xtable_1.8-4             XML_3.99-0.5            \n",
      "[100] jpeg_0.1-8.1             gridExtra_2.3            compiler_4.0.3          \n",
      "[103] biomaRt_2.46.0           tibble_3.1.0             KernSmooth_2.23-18      \n",
      "[106] crayon_1.4.1             htmltools_0.5.1.1        mgcv_1.8-34             \n",
      "[109] later_1.1.0.1            Formula_1.2-4            tidyr_1.1.3             \n",
      "[112] DBI_1.1.1                tweenr_1.0.1             dbplyr_2.1.0            \n",
      "[115] MASS_7.3-53.1            rappdirs_0.3.3           cli_2.3.1               \n",
      "[118] igraph_1.2.6             pkgconfig_2.0.3          GenomicAlignments_1.26.0\n",
      "[121] foreign_0.8-81           plotly_4.9.3             xml2_1.3.2              \n",
      "[124] XVector_0.30.0           stringr_1.4.0            VariantAnnotation_1.36.0\n",
      "[127] digest_0.6.27            sctransform_0.3.2        RcppAnnoy_0.0.18        \n",
      "[130] graph_1.68.0             spatstat.data_2.0-0      Biostrings_2.58.0       \n",
      "[133] fastmatch_1.1-0          leiden_0.3.7             htmlTable_2.1.0         \n",
      "[136] uwot_0.1.10              curl_4.3                 shiny_1.6.0             \n",
      "[139] Rsamtools_2.6.0          lifecycle_1.0.0          nlme_3.1-152            \n",
      "[142] jsonlite_1.7.2           viridisLite_0.3.0        askpass_1.1             \n",
      "[145] BSgenome_1.58.0          fansi_0.4.2              pillar_1.5.0            \n",
      "[148] GGally_2.1.0             lattice_0.20-41          fastmap_1.1.0           \n",
      "[151] httr_1.4.2               survival_3.2-7           glue_1.4.2              \n",
      "[154] spatstat_1.64-1          png_0.1-7                bit_4.0.4               \n",
      "[157] ggforce_0.3.2            stringi_1.5.3            blob_1.2.1              \n",
      "[160] latticeExtra_0.6-29      memoise_2.0.0            dplyr_1.0.4             \n",
      "[163] 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": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pp.neighbors(latent)\n",
    "sc.tl.umap(latent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 494.64x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.umap(latent, color=['cell_type', 'batch'], frameon=False, ncols=1)"
   ]
  },
  {
   "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
}