200309 Doublet Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# doublet cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Attaching package: ‘dplyr’\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style> .container { width:95% !important; } </style>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(Seurat)\n",
    "library(dplyr)\n",
    "library(magrittr)\n",
    "IRdisplay::display_html(\"<style> .container { width:95% !important; } </style>\")\n",
    "library(\"xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Regressing out nCount_RNA, percent.mito\n",
      "Centering and scaling data matrix\n",
      "Regressing out nCount_RNA, percent.mito\n",
      "Centering and scaling data matrix\n",
      "Computing nearest neighbor graph\n",
      "Computing SNN\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck\n",
      "\n",
      "Number of nodes: 6200\n",
      "Number of edges: 240314\n",
      "\n",
      "Running Louvain algorithm...\n",
      "Maximum modularity in 10 random starts: 0.9070\n",
      "Number of communities: 24\n",
      "Elapsed time: 0 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing nearest neighbor graph\n",
      "Computing SNN\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck\n",
      "\n",
      "Number of nodes: 6168\n",
      "Number of edges: 227894\n",
      "\n",
      "Running Louvain algorithm...\n",
      "Maximum modularity in 10 random starts: 0.9166\n",
      "Number of communities: 27\n",
      "Elapsed time: 0 seconds\n"
     ]
    }
   ],
   "source": [
    "cntl.data <-Read10X(data.dir = \"/media/garret/New Volume/scRNAseq_data/Yoshiko/180725LHb/raw_gene_bc_matrices/mm10\")\n",
    "colnames(cntl.data) = paste0(colnames(cntl.data),\"cntl\")\n",
    "cntl<- CreateSeuratObject(counts = cntl.data, min.cells = 3, min.features = 200, project = \"10X_LHb\")\n",
    "cntl@meta.data$stim <- \"cntl\"\n",
    "stim.data <-Read10X(data.dir = \"/media/garret/New Volume/scRNAseq_data/Yoshiko/180905LHb/raw_gene_bc_matrices/mm10\")\n",
    "colnames(stim.data) = paste0(colnames(stim.data),\"stim\")\n",
    "stim<- CreateSeuratObject(counts = stim.data, min.cells = 3, min.features = 200, project = \"10X_LHb\")\n",
    "stim@meta.data$stim <- \"stim\"\n",
    "\n",
    "\n",
    "mito.features <- grep(pattern = \"^mt-\", x = rownames(x = cntl), value = TRUE)\n",
    "percent.mito <- Matrix::colSums(x = GetAssayData(object = cntl, slot = 'counts')[mito.features, ]) / Matrix::colSums(x = GetAssayData(object = cntl, slot = 'counts'))\n",
    "cntl[['percent.mito']] <- percent.mito\n",
    "mito.features <- grep(pattern = \"^mt-\", x = rownames(x = stim), value = TRUE)\n",
    "percent.mito <- Matrix::colSums(x = GetAssayData(object = stim, slot = 'counts')[mito.features, ]) / Matrix::colSums(x = GetAssayData(object = stim, slot = 'counts'))\n",
    "stim[['percent.mito']] <- percent.mito\n",
    "\n",
    "cntl <- subset(x = cntl, subset = nCount_RNA > 700 & nCount_RNA < 15000 & percent.mito < 0.20)\n",
    "stim <- subset(x = stim, subset = nCount_RNA > 700 & nCount_RNA < 15000 & percent.mito < 0.20)\n",
    "\n",
    "cntl<- NormalizeData(object = cntl,verbose = FALSE) \n",
    "stim<- NormalizeData(object = stim,verbose = FALSE) \n",
    "\n",
    "cntl<- FindVariableFeatures(object =cntl,selection.method = \"vst\", nfeatures = 2000, verbose = FALSE)\n",
    "stim<- FindVariableFeatures(object =stim,selection.method = \"vst\", nfeatures = 2000, verbose = FALSE)\n",
    "\n",
    "cntl<- ScaleData(object = cntl, features = rownames(x =cntl), vars.to.regress = c(\"nCount_RNA\", \"percent.mito\"))\n",
    "stim<- ScaleData(object = stim, features = rownames(x =stim), vars.to.regress = c(\"nCount_RNA\", \"percent.mito\"))\n",
    "\n",
    "cntl<- RunPCA(object = cntl, features = VariableFeatures(object =cntl), verbose = FALSE)\n",
    "stim<- RunPCA(object = stim, features = VariableFeatures(object =stim), verbose = FALSE)\n",
    "\n",
    "cntl <- JackStraw(object =cntl, num.replicate = 100)\n",
    "stim<- JackStraw(object =stim, num.replicate = 100)\n",
    "\n",
    "cntl<- ScoreJackStraw(object = cntl, dims = 1:20)\n",
    "stim<- ScoreJackStraw(object = stim, dims = 1:20)\n",
    "\n",
    "\n",
    "cntl<- FindNeighbors(object =cntl, dims = 1:30)\n",
    "cntl<- FindClusters(object = cntl, resolution = 0.8)\n",
    "stim<- FindNeighbors(object =stim, dims = 1:30)\n",
    "stim <- FindClusters(object = stim, resolution = 0.8)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "saveRDS(cntl,file = \"/media/garret/New Volume/scRNAseq_data/Yoshiko/180725LHb/all_cells/gene2000_30/cntl.rds\")\n",
    "saveRDS(stim,file = \"/media/garret/New Volume/scRNAseq_data/Yoshiko/180725LHb/all_cells/gene2000_30/stim.rds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "cntl<-readRDS(file = \"/media/garret/New Volume/scRNAseq_data/Yoshiko/180725LHb/all_cells/gene2000_30/cntl.rds\")\n",
    "stim<-readRDS(file = \"/media/garret/New Volume/scRNAseq_data/Yoshiko/180725LHb/all_cells/gene2000_30/stim.rds\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts<-as.matrix(cntl@assays$RNA@data)\n",
    "write.table(data.frame(\"GENE\"=rownames(counts),counts),file=\"/media/garret/New Volume/scRNAseq_data/Yoshiko/180725LHb/all_cells/gene2000_30/doublet/counts.txt\",row.names=FALSE,sep=\"\\t\")\n",
    "\n",
    "counts<-as.matrix(stim@assays$RNA@data)\n",
    "write.table(data.frame(\"GENE\"=rownames(counts),counts),file=\"/media/garret/New Volume/scRNAseq_data/Yoshiko/180905LHb/all_cells/gene2000_30/doublet/counts.txt\",row.names=FALSE,sep=\"\\t\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calculating cluster 0\n",
      "Calculating cluster 1\n",
      "Calculating cluster 2\n",
      "Calculating cluster 3\n",
      "Calculating cluster 4\n",
      "Calculating cluster 5\n",
      "Calculating cluster 6\n",
      "Calculating cluster 7\n",
      "Calculating cluster 8\n",
      "Calculating cluster 9\n",
      "Calculating cluster 10\n",
      "Calculating cluster 11\n",
      "Calculating cluster 12\n",
      "Calculating cluster 13\n",
      "Calculating cluster 14\n",
      "Calculating cluster 15\n",
      "Calculating cluster 16\n",
      "Calculating cluster 17\n",
      "Calculating cluster 18\n",
      "Calculating cluster 19\n",
      "Calculating cluster 20\n",
      "Calculating cluster 21\n",
      "Calculating cluster 22\n",
      "Calculating cluster 23\n",
      "Selecting by gene\n",
      "Calculating cluster 0\n",
      "Calculating cluster 1\n",
      "Calculating cluster 2\n",
      "Calculating cluster 3\n",
      "Calculating cluster 4\n",
      "Calculating cluster 5\n",
      "Calculating cluster 6\n",
      "Calculating cluster 7\n",
      "Calculating cluster 8\n",
      "Calculating cluster 9\n",
      "Calculating cluster 10\n",
      "Calculating cluster 11\n",
      "Calculating cluster 12\n",
      "Calculating cluster 13\n",
      "Calculating cluster 14\n",
      "Calculating cluster 15\n",
      "Calculating cluster 16\n",
      "Calculating cluster 17\n",
      "Calculating cluster 18\n",
      "Calculating cluster 19\n",
      "Calculating cluster 20\n",
      "Calculating cluster 21\n",
      "Calculating cluster 22\n",
      "Calculating cluster 23\n",
      "Calculating cluster 24\n",
      "Calculating cluster 25\n",
      "Calculating cluster 26\n",
      "Selecting by gene\n"
     ]
    }
   ],
   "source": [
    "markers <- FindAllMarkers(object = cntl, only.pos = TRUE, min.pct = 0.25)\n",
    "top_50<-markers %>% group_by(cluster) %>% top_n(50)\n",
    "write.table(data.frame(\"test\"=as.character(rownames(top_50)),top_50),file=\"/media/garret/New Volume/scRNAseq_data/Yoshiko/180725LHb/all_cells/gene2000_30/doublet/Top50Genes.txt\",row.names=FALSE,col.names=c(\"\",colnames(top_50)),sep=\"\\t\",eol=\"\\n\")\n",
    "\n",
    "markers <- FindAllMarkers(object = stim, only.pos = TRUE, min.pct = 0.25)\n",
    "top_50<-markers %>% group_by(cluster) %>% top_n(50)\n",
    "write.table(data.frame(\"test\"=as.character(rownames(top_50)),top_50),file=\"/media/garret/New Volume/scRNAseq_data/Yoshiko/180905LHb/all_cells/gene2000_30/doublet/Top50Genes.txt\",row.names=FALSE,col.names=c(\"\",colnames(top_50)),sep=\"\\t\",eol=\"\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster<-Idents(object=cntl)\n",
    "cluster<-as.matrix(cluster)\n",
    "cluster[,1]<-as.character(cluster[,1])\n",
    "cluster[,0]<-as.character(cluster[,0])\n",
    "cluster<-data.frame(\"x\"=rownames(cluster),cluster)\n",
    "write.table(cluster,file=\"/media/garret/New Volume/scRNAseq_data/Yoshiko/180725LHb/all_cells/gene2000_30/doublet/Cluster.txt\",row.names=FALSE,col.names=c(\"\",\"x\"),sep=\"\\t\",eol=\"\\n\")\n",
    "\n",
    "cluster<-Idents(object=stim)\n",
    "cluster<-as.matrix(cluster)\n",
    "cluster[,1]<-as.character(cluster[,1])\n",
    "cluster[,0]<-as.character(cluster[,0])\n",
    "cluster<-data.frame(\"x\"=rownames(cluster),cluster)\n",
    "write.table(cluster,file=\"/media/garret/New Volume/scRNAseq_data/Yoshiko/180905LHb/all_cells/gene2000_30/doublet/Cluster.txt\",row.names=FALSE,col.names=c(\"\",\"x\"),sep=\"\\t\",eol=\"\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading packages...\n",
      "Reading data...\n",
      "Processing raw data...\n",
      "Combining similar clusters...\n",
      "Creating synthetic doublet profiles...\n",
      "Step 1: Removing possible doublets...\n",
      "Step 2: Re-clustering possible doublets...\n",
      "Step 3: Rescuing cells with unique gene expression...\n",
      "Finished!\n"
     ]
    },
    {
     "data": {
      "image/png": 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tfI2nnXVTMf7PBpZFnPfbDvAv/AtUH+86eP9mpas06XZZF5BROvzrjty1yLqetI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dZj8uctv45qVlhykAFDsAAFBEXV3dr9t8\nMeLPzeN9HGRnKRgUOwAAoGJpuhOHEm8LIU5fN4rbiYdjY5OEKFnFq6azMWpi8Fptt0Vdip2M\njc18vINrfU8ni+1HFhscAAAgd7rlfRp/vPfRvbkdG88VQjSaFR8blLz59wSRlDCg2cqsj2/x\n+aWooRUKPWYBodgBAAAVcwnaYwzKftbU08aphRvG5DhBMQAAgEpQ7AAAAFSCYgcAAKASFDtY\nlCtXZCcAAMB8cfAELIqTk9DpZIeQx3hU0eKa2gWUA8i/Iv7pVbj6wvJHAIWFLXYAAAAqQbED\nAABQCYodAACASlDsAAAAVIJiBwAA1C7lyNIhTctaaVqGn8/+Afd2f+RupXEZvqdwcxU4ih0A\nAFCzO/vmtKvlMznRyTXHc4Ho900euOSSXWGmMhGKHQAAULNrR+Ldp+05vGFwrRxaT9qh6f0X\nlBkyoEHh5jIJih0AAFCzqn3nL+xd2z6n2Q+OzxoQah204AO3QsxkMpyg+CGDwSCEiImJkR0E\nwtbW1s/Pz9raWnYQAIDqGU9/NnDazQ+2ja5n/YXsLAWBYvdQXFycECIsLCwsLEx2FoitW7e2\nbdtWdgoAgNolfD1wQsJ7P//WtLjI4agKC0Oxeyg4ONhgMHh6elpZsXtaMltbW19fX9kpAACq\nd+nbD0cd6/zd+jY57qe1OBS7h7Ra7YQJE2SnAAAAheTyymFBe1t/eaxLadlJChBbpwAAQFG0\nb/PmG9d+etNZk6FK4G5xYZ6PJudz3VkCttgBAICiqG3YqcSphsy7SYu7Nf7Sa8P+Sc1KV5SY\nSiGKHQAAULE03YlDibeFEKevG8XtxMOxsUlClKziVdO5TCWXMlke6KgV1vZOLi5OspIWBIod\nAABQMd3yPo0/3vvo3tyOjecKIRrNio8NcpMXymQodgAAQMVcgvYYg/LyuOFRxuEmT2NqHDwB\nAACgEhQ7AAAAlaDYAQAAqATFDpblgewAAACYLw6egEVJ2i1EuuwQ8mhqy04APK8i/ukt4quP\nQsQWOwAAAJWg2AEAAKhE9rtiDQaDECImJqZww+RbdHS0ECI9vQjvmwMAAHgk+2IXFxcnhAgL\nCwsLCyvcPM8jIiIiICBAdgoAAGCW0s/+MnHEpGV/HLv8oOxLrft9On9Sx6rWskOZSPbFLjg4\n2GAweHp6WlmZ9b7a6Ojo8PBwPz8/2UEAAIB5St8/rkPXz4sNDF+/qIHVoUWBw7q0FzGHpngX\nkx3MJLIvdlqtdsKECYUc5fmEh4fb2HBsLwAAyM699dPDT9Qdc/TLAbWEEN7e6y7Hvjh15vqQ\nNW+WkB3NFMx6gxwAAIAip48eTS338iu1Ht2v8cYbte5GRpr7YQTPi2IH86LX66dPn67X62UH\nAQCoQsYBoVmUK1dOJJ85c11OHFOj2MG8hIaGjhkzJjQ0VHYQAIAquNeqpb0aFXXi0f07e/Yc\nESIlJUVmKNOh2MG8eHh4ZN4CAKBUye4jB7kdnNlr+PfRx04d2DChU+AWTTGh2u/nU+xgXjIO\nxDbzw7EBAJZD+/LMjd8GlPxlqG/DJt1mnOv87bjmaVbly5eVncs0+PMJAABUza72+1/tjE9O\nvZ8cH/3dcMekE8Kjbl1b2alMg2IHAABUTBez/LPv99x8eC9977IVJ927dqkjNZPpqHQPMwAA\ngBBC2J3fOOm9PduvfT2+Tflr20OHLLjrvyLQW3YqU2GLHQAAULFS/l//NveVK5/1atWo5dtf\nXOuwZPt33cvLDmUybLEDAACq5tj0f8ui/ic7ReFgix0AAIBKUOwAAABUgmIH85JxMTEuKQYA\nwHOg2MG8LF68OPM2Ow8KMwwAAJaFYgfz4uXllXmbHevCDAMAgGWh2MG8cEkxAACeG38+AQAA\nVIJiBwAAoBIUOwAAoGr6s5umBDRzr+BQwrGyZ8ves/9MMsiOZDIUOwAAoGK3IwPbvBF6qlHI\nsm1RW74cXHHXqA495p0yyo5lIlxSDAAAqJd+54p1F73Hbl7Qz0MIIRouuxtd7q0f1icEjnaT\nnMwkKHYAAEC9tH6Lk+5luW/n6uok9uh0QrjJimRK7IoFAABFhiEucvs5hxYt6soOYiJssYN5\nMRqNmbcAABSo1EPTe0875v3J8q4lZEcxEYodzMulS5cybwEAKDgp+2Z28Zus67liZ1At1V7H\niF2xMC+dO3fOvAUAoGAYkyL+90rryZffWh+1pLuLRnYc06HYwbzY2Nhk3gIAUBBu7Qh6resy\nu1GRu+Z3qqTu6sOfTwAAoGZXV/frNl+M+HPzeB8H2VlMjmIH86LX6zNvAQBQKjVqYvBabbdF\nXYqdjI3NnOrgWt/TSY0lSI3rBEu2ePHijNtevXrJzgIAsHzHN/+eIJISBjRbmXVqi88vRQ2t\nICuTCal7RzMsj5eXV+YtAABK1Z962vg0dbY6QbGDubGyssq8BQAA+cKfTwAAAJWg2AEAAKgE\nxQ6W5YHsAAAAmC+KHSyLai8CAwCAchQ7AAAAlaDYAQAAqATFDgAAQCUodgAAQO1Sjiwd0rSs\nlaZl+PnHpt+LWze2u3d1Z/sSjpU9W70X+scFCz9Ij2IHAADU7M6+Oe1q+UxOdHJ98kKqtyKG\ntvFfbuy3dPeZhOOb57aKm9LB75O/0qWkLCAUOwAAoGbXjsS7T9tzeMPgWk+0HsOuFasSG34Y\n+kErd2enSnU7Tpvwpv3hFeuOyYlZMJ7srgAAAGpSte/8hUIIEf/kDI21tUYYjcbM+xqNxV/U\n0qLDAwAAPC9N64FDah75YvTX+y7euZ+SsGv6zLXp/zfsvTqycynBFjuYl4x/OWX59xMAACai\nbf7pxm/Pte/btPIgIYQo1Wj4yk1DPGSnUoQtdjAvly5dyrwFAMCUkjcPbd8/ps7sLUfPXTh7\n8NfRZVf6tx8fkyo7lhIUO5iXzp07Z94CAGBCZxeP//LCG6HLh71aq0ol13qdQpZPaHYwdNqa\nW7KDKWDZu2INBoMQIiYmRnYQFJh9+/bJjgAAKBr+PXPGWPE1j5KZE8q7uZXQ74m/IEQpibEU\nsexiFxcXJ4QICwsLCwuTnQUFKSIiIiAgQHYKAICqVatWTVw4cSJF1HfImHDh1Km7Gjc3V7mx\nFLHsYhccHGwwGDw9PS382GT8Jzo6Ojw83M/PT3YQAIA6pOlOHEq8LYQ4fd0obicejo1NEqJk\nFa+aztV6Denw6YCQPvPLTelc2z750KrgmVFV+v7WpYTsyApoOPwQZmXNmjU9e/ZcvXq1v79/\ndvNPCWHRpwRXRlNbdgIAkOT568r52c2qfLz3iYmNZsXHBrkJcfPwssljw3/Zd+rS/VIuL7V4\nK2RGSJcadsqiSmXZW+wAAACeySVojzEop5mOXr3n/tp7bmHmMS32YAIAAKgExQ4AAEAlKHYA\nAAAqQbEDAABQCYodLMsD2QEAADBfFDtYFmvZAQAAMF8UOwAAAJWg2AEAAKgExQ4AAEAlKHYA\nAEDV9Gc3TQlo5l7BoYRjZc+WvWf/mWTInHcjZl7fFu5lS9iWdK7Z9qPlx+5JzFkQKHYAAEDF\nbkcGtnkj9FSjkGXborZ8ObjirlEdesw7lXHp2fgverQbtefFj1dHx+76bqDjL++3HbIpWXJe\nZbhWLAAAUC/9zhXrLnqP3bygn4cQQjRcdje63Fs/rE8IHO0m9sz7dFuFj/Z+M6iJtRD16yyf\n95dL96mLJ3ccWUV26ufGFjsAAKBeWr/FSff2jvZ4dN/O1dVJ6HQ6IcTJP/44X659xyaPzqRV\nol2HVzR7t/5xS07SAkGxg3kxGAyZtwAAFDBDXOT2cw4tWtQVQsTFxYnq1av/N7NE9erOxlOn\nTktLpxy7YmFe4uLiMm8BAChQqYem9552zPuT5V1LCJF682aqKFWqVJb5Dg4OIjnZkr9lR7GD\neQkODra2th45cqTsIAAAlUnZN7OL32RdzxU7g2rlcB0jo9EoNBpN4eYqUOyKhXnRarUhISFa\nrVZ2EACAihiTIv73SuvJl99aH7Wku0tGcytepoyduHUr6zfqbt26JcqUKSMnY4Gg2AEAAHW7\ntSPota7L7EZF7prfqdJ/1cfT01Oczvrdn1unTumsX3rJXULEgkKxAwAAanZ1db9u88WIiM3j\nm5d+bEaN115zvx6xISr94f3kX37eYfOy36v2hZ+xwGiMRqPsDEDenRIiPfdHqZWmtuwEACDJ\nc9eV1Kghnq3W+yz6eWT9LF+sc3Ct7+lkIy4u71yn33G/sM8DWzqe+2XC4E9OB/xxJPyVEgUS\nWQqKHSwLxQ4AiqTnrisHx7k3mHbmyaktPr8UNbSCEOLWX18GDZ+1NvZ8quOL/9fnk8+ndK1e\nTFlSuSh2sCwUOwAokqgrecN37AAAAFSCYgcAAKASFDsAAACVoNgBAACoBAdPAAAAqARb7AAA\nAFSCYgcAAKASFDsAAACVoNgBAACoBMUOAABAJSh2AAAAKkGxAwAAUAmKHQAAgEpQ7AAAAFSC\nYgcAAKASFDsAAACVoNgBAACoBMUOAABAJSh2AAAAKkGxAwAAUAmKHQAAgEpQ7AAAAFSCYgcA\nAKAS1pMmTZKdAciftFsX4o4eOXb8ZFz8+cs39TYOpe21z/lPlLSb50/HX35Q0tG+mLJ/5KTf\nvnL5ro29rU2eHv3g7vXLustXk+/ohbaErY1GwQuf3hS+9U6dOhW1Cp5D5Dd/wQQw3L929vix\nM5dTbV8oY2ed9+UKcPQe0d84n3jbpnTJYjk+IjU56UZa8ZLF85EzV8b71xNOJ9y0dnC0U7gW\nz/X2Pe/4Zyf3AcyG4p8+RQNo1N+6eObMpbs2DqWeb/jz9eqm+PxkkefxL8Bfngp/+Sgef+TM\nCFiM1DMbpwQ0q1Lyid8C1qU9Wg+Yt/PSg2cvnX5+0/0HKScAABdVSURBVPhuTTxcXDxb9Zm/\n96bx2pYxL1fM+EWoKd1owLLj9569eNKfs/u2afRiFRfPFm+OXXU05bG5+0e5iu5rcslvuLJ/\ncVAPn2qls/witLJzeunlgJBv911Oz8MIPG1Nd9E0LDEPDyyA/EoC/P3VoDE/JTy6l3Z2Q8hr\nVR8Ng8axZs9Zu6/n8gymGL1Hcl/9Nd2FKOHe+ZMNcXef7yXSEn4e071FXc86rXrN3n3VaEhY\nPahB6YxPsnX5lh+tikt95uJK3z7l4/9MuQZQ+NNnVDyA+rjVQV0nbk03Go3Geyd+DGzjUvzR\nh6hMvXdCt+Xy60Phqyv//Dxb7h8AZb88s5fnXz5GxeOP/KDYwWIkLn2jrLVTk3fHfvb9hm3R\nsYf+OXr0n4P7o7au/zb0f2/UtLeu8t7Pl3Ne2nD800bW9i/59Rs2rP/rdRzLvv2/dytU+L+g\nhas3Rfz8Q/j/WlUoVm/SwZzbgeH4jCbFrMs17vHBiJGDuzVwsrL17L824b/fRXkoRlc39nez\nsSnfqOfwSaGfzZ879p0GZZ1aBn62KGzMey9Xs7eu2OHrE2k5L71pYOVsvWAntI4VMv5/4CYT\n5lcYYE134Tpqf8b/p+8LqWldrHKbwM9Wbdqyef3SGQN9ylvbt/0y/hkvr2z0cpOnYlf+tf69\najtoXf5v2KKYpHy+mOHwxLoabZWX3xnU7406ji8ELJ7fqYRTi2Hzfvz5l5VfjuvuYWfTaOo/\nOf9pU/72KR3/XOQSQOFPn1HxAOp3Dq9mVdZ7/PabRuO9Pz6oKuxf6jH+qzWbtm7ZsGJeUAe3\n4sXqTz7wjPFX9upGxZ+f3OT2AVD2y1Phz75R8fgjf/K/4wWQ48iXn26uPfv49uHVn9hz4N2i\nbdf3/9f3k+YNx38xpvOEF7Nf/J9lSxLeXXl2SceSQojUd4a5/9+K9hsTZnUsKYQQHTp38bjl\nOmLN8Yn16uS0+OK/m8w6uSuwukYIYZj859S3egS0Fut3L+rknLf8pxaEfKPtH/n3F23KZEwY\nMqjr+Jdf/6vCme++Gz556sp3ffp8tKTr7wPKZb94mTK21y5cqthhwIB2bnZZph/6LnB7tcHD\nX3EUQnjksO4Fkl9hgKz2Lv/uZMNJR38f81LGjqnXugb4VWxSL3zx0UFTame/iMLRO70pfGPc\nMxIlxt4SpXOLbVd/0PcbPxn85cTgsa2qTW359sAB77/TpXnVEnnZjXToh+9P+ob/+8cHlYW4\n9+Ygr57jUt/84fhnfiWFEOKNNzuUe7n6l6uOj62Tw+orf/uyeo7xVziACn/6hOIB3LtqxfWA\nb/755P9KCrFt5crrnb+IXfOOU8a8V19/s1u1ttWnL90/oX5Tk7x6BiWfH4Xjr/CXp/KffYXj\nj3yS3SyBPNrwtvalSUdynn9gbI3ivX7NcfbGd2xf+eLKo3t3l/mJ5p9dzDL/wjwfu3dz/kfn\nxndsq4X8lXXKnX1TmtuXaDhu902jMS+bTDa8rXUbHfv4tJPT6jr225rx/3+FVCvRO+IZT3A3\nbu2o1hXt3V6ftOns/cypedwbojy/wgBZtxhtetfuqaGIHe2mfXtDjosrHL11/hphVax4jrTW\nIh9bvIy3436bM9DXtYQQxZ1q+/YYMHLSnIXfLP0hKudh2PiObY2xBx7eMfzyrlbT6fusOx/3\nflzFtB8/heOvcAAV/vQZC2IAPcYfyvj/iN4l6s84/fj8/aNcc/ntoeTVjYo/PwrHX+EvT4U/\n+0bF44/84ahYWAoXF5ez0dGXcphrTIjanVilSpUcF69QocLp48fTMu6k//PPcXH66NHUzNmp\nR4+eqVSpUo6LV65c+eKBA5ezTCnReNzG9X3vz+zoNyUmOQ/5nZycLh08qMs66ereff9qNBn/\nYNdfvnzTzs4u20Uz2Ll3n/HH8aiJlTa+W8ere+i2C+l5eNWCy68wQFY1a9W6cu5c6mPTUlJu\nlypVKsdFFI7ea4MGu5Vp//XZ+znYHeSanxUo6d5+xFfb/r0Ut+3bkI6ud/5eO2/M0P7v9Qrb\nk+MSzs7Ol06eTMm4o6naqH3bZtVt/5udcubM5fLly+e4eEG8ff95jvFXOIAKf/qE4gFs2KRJ\n/KqvtiULIUSzNm0ubNt2Icvca78vWn+udu1apnr1J+X/86Nw/BX+8lT+s69w/JFPspslkFcn\nw1va2dZ4fdTC9TsOnErUXUu+eTP5WlLCidjIlXOHta5czKHtF2dyXjptX7CntsqrH02fN+/T\nYW2qOLYP6Ozk9vr01X/GHtj3x4qpndxs3Efs1ue4uOGfyfWsHOr1nv57wmOTk34LbGSvdev4\n1stlc9lkkr5/TE0r+1pvfvLdpj9jYv7ctHRqQG0H4TJwy12jMTlmboeq1mV7b7iTl4FI1+2a\nG/CSvUOdXmFRupV5+0ez8vwKA6zpLuzr9xg2YtS4yZ/OGt/Fzabhp6cyZ6adW9a9QnG/b3P+\n/r7i0bv5e1+XMp2XX8x+bv62eGUb8N71i9dyDpAWM9LdquKr43746+oTcx7cjNsa3tXVusqQ\n7Tl//17526dw/BUOoMKfPqPiATTe/3tWi9LaCt49P5773eov/uddoVr7YdMXLPp63qdBb3qX\ntylWY8jvN0z36oo/P0o/wMp+eWbN+Rw/+0aj0vFH/lDsYEGS9y7s38LF7qnvpFg5uL86fPk/\nubSiO0e+6dO4Ygkra/tqbUdtvPTg4voPvEo9fK5irn4zY5KfuXjqvys/9HGuMCDyyRm3j/z4\n8atVi+e2L89o1P/706h2VR8dCya0lX2HLT92z2g0Gm8ufadez9l7buay/lndPxsxoYOrbdmy\nZfL4u1V5fkUBomd0bOfbskmDOp7Vqzi/4GBrU6b/1syZiWFNi3uNjXnmG6h49K7vWTZr8a4r\n2c47vqBL05Btz1o6tz/MuUo5MK9bDbsKH+58Yvq1r3yFKOUd9Ef2yR5R+vYpHn+FA6jwp8+o\neACNxvsJkWEfdqxfuUTWHVUau4oNXh++aN9Vg2lfXfHnR+kHWNkvz8fl+5dPxlJKxh/5oTEa\njUo3+gGF6v7lk4ePxCVeTr6bptGWLFPBzbNu3Rov5PUEWoYHDzTW1g9/vRlunj1w8N+7jtVq\n16r2Ql5OxmTU69O12mxe677uxIl7Feu7Oeb6+veuJMTFXzGWdXV3cy6p8KxWd46tDA3fbNMj\nbHy7MnlaQHl+hQH+YzAYrawevg/pF89cKFPD9Vk7oh8uVJCjly+3Eo8kad1fdLbN/aHPcO/G\nDU2ZMo8/R+JfO1PdW7g75mFtCvLte67xV0rZT59QPIBCCCGM969fuHD55t10ja1Ducou5Uvk\n+XOk4NUL5vOjmLJfno957p/95x9/5BHFDmqRfvvK1fRSFUoXz/2h2dHfOH8x/QU3pxLP+/oP\ndLpkZ+eyz7u40mdQmF/x6isdf7mrr/ztUxhA4egpHnzJ+aWPv/TPj4X/+EtefTyOgydgOR7o\ndszp19bbs2qVl1q+NW71sduPzT04tXHF/r8+95Mfntmy2gcROc7eOLrbzB1JD3KYe/ufZUN9\nag3b/qwXUP4Mz5RL/oJYXNH4y119E7967gEUf3pN+uEXps5vBuOvaPEi/uNv9quPx1HsYCmM\nJ2a/8eqoDbeqtfF/09f51NcBjbwHrDtnKKRXd9THTvCt7TP0+yOP/0ET+nObJ3ao3ajP9+lv\ndG1o2meQS+H4y1196YOvcPTkfviVB5A+/goV8R//Ir76locTFMNSKDxHq8IzfLaae+yg94QP\nRvRptGnV+G++Gt2mko0wXt/7RWC/0csSXd+etWPu0FbOz/yuiMJnUJhf+Rl6FY6/3NVX/vYp\nPkOvotFTfoJiufmlj7/0z49F//hLX33kk+yjN4A8UniOVuWnqDUajcbkg0s+aFrOunSDgWGf\nB7Zyti7p2XPmHxfyc3mg530GhfmVr35BnOJY2uorfHXlARSOnvLBl5v/IXnjL/3zo/AZivjq\nI38odrAUB0bVKN7+G93jE69vGVqrWOkWn0TfyO1Py+3ID9zKvr70Uk7z8/aXyWg0Gh9c3fzh\nS9ZCiPKvLzmdy6W/C+4ZFOZXvvoKxz8LCauv8NWVB1A4esoHX27+LOSMv/TPj8JnKOKrj/yh\n2MFSKD5Hq9JT1BqNRqPxzslVI19xtrav06PPqy7FStV9d0HMlfxdu/q5n0FhfqWrX0CnOJa1\n+gpfXWkAhaNXEIMvM/8j0sbfDD4/Cp+hiK8+8oNiB8uh+BS7ys7wqU/YPKWjW3Gbir5jNvyb\najQakw981cfLwapssw+XHMrTuYWVPoOy/EoXVzz+cldf+dunLIDC0SuI80vLzC99/OV/fiz6\nx1/+6iPvKHawMIbU1OwvPXQv6fiB+FxPX/+84pa9XdtelKzd6/P917OeI11/duO4NpVsbCr5\njlp78q5pn8EsPO/4y119cxl8hZ9eSR9+5QHMZfyfVxH/8S/iq295KHZQj/SkpCev5Fhgi695\ns8Iro385cz+7eYbrsQveqV0yt4t1Kn6GXJhy9ZU/g9zVN/mr5xbA3Bc3cQBLH/8i/uNvAauP\nrCh2sBS/juo6489L6TnMTTm8dEjjF/xz/tWgcPHr1649+2KGqf/+svmfZz1A4TPIXX3lzyB3\n9ZW/fXLHX/rbV8THv4j/+EtffeQPxQ6WYmdgFa3mhcZDlv+T8viM1ITfJrSvWkzj2KDPj2dM\ntbh00ldf7gBKf/vkjr/0t6+Ij790RXz1kT8UO1iOlGM/BL7ibK116zQlMuPcR4Zrexb0ru0g\nStV5O3xnUk7/IC2YxaWTvvpyB1D62yd3/KW/fUV8/KUr4quP/KDYwcIoPMWlpZ8hU/rqyx1A\n6W+f3PGX/vYV8fGXroivPvKIYgcLpPAUl5Z+hkzpqy93AKW/fXLHX/rbV8THX7oivvrIA4od\nLI3CU1xa+hkypa++3AGU/vbJHX/pb18RH3/pivjqI28odrAgCk9xaelnyJS++nIHUPrbJ3f8\npb99RXz8pSviq498oNjBUig8xaWlnyFT+uoX8TMMyx1/6W9fER9/6Yr46iN/KHawFApPcWnp\nZ8iUvvpyB1D62yd3/KW/fUV8/KUr4quP/KHYwVIoPMWlpZ8hU/rqyx1A6W+f3PGX/vYV8fGX\nroivPvJHYzQaBQAAACyflewAAAAAKBgUOwAAAJWg2AEAAKgExQ4AAEAlKHYAAAAqQbEDAABQ\nCYodAACASlDsAAAAVIJiBwAAoBIUOwAAAJWg2AEAAKgExQ4AAEAlKHYAAAAqQbEDAABQCYod\nAACASlDsAAAAVIJiBwAAoBIUOwAAAJWg2AEAAKgExQ4AAEAlKHYAAAAqQbEDAABQCYodAACA\nSlDsAAAAVIJiBwAAoBIUOwAAAJWg2AEAAKgExQ4AAEAlKHYAYHJ/Dq2g0ZQeHJn9XQAoKBQ7\nAMhJ2qXo78a937HJi5XK2hcvbl/OxcPr5R4jPt9wJNlowleNHe2uyU2P/2/nTqOivM44gD/D\n8g7MyKayyWIcZdEAKntCgKDECUYFzKFEgbAzEUxrtCKYGhVprMhyLCYlREKRZXBDUYtYTwRF\nEmPhIAEBEYnWVMVKkcOww0w/GJYZPHViOwHs//ft3vvMef/vl5n7zr3vLRxSYAIAmLZUJjsA\nAMCUNHC7MMY3/FBdD6kZ2Lzu7m2qrdT1sPX6laK0ihPpyauTivI+stNUyJX1nNcLBI9Gmw+/\nySuu6zZxD11pyYx22i7AYzkAPAMmdgAAE3Wci1kRcKhVZYH/5wXpUQ66yiMD3a2ndweG7j+z\n2SvSuOmI30wFXNvUJyHDZ6x5ZdPZ4rruRYGpGRHaCrgaALxU8MwHACBr6NrejYdaxTpemReF\nG8bN6oiIy1uTdL4wysrsVdV7TV0jvV0NhTvWuy2co6XGqGkZLnQP2H3yVq9iMz5Id1dlsebF\nVcmsCvce89dgsfRjygaJqDRCm8ValHCjvSIt1N3SQFONzdU1eyNob9kDsdSHnptf3FaZudXX\n2dJUV4OtrmUwbwk/6g+ldwYVe4sA8PPhHzsAABmSS9l/biWy+nVSsAnrGeMab31R1zzW7K6M\nc+Xvq+2f6xm1ZYO1TldTaXbGrrUlZWkVX2+yUn7G5/8nDIMEq+Mun8zJuphov3zsq1x0RnhW\nRLwN4W+qEhGbzSbq+O6T1WnVHL/ATavU/3XjXH5u3nZ+Vcfl60nObDnzD1TGurmn3DZ0DQ2J\ntTHkDLbVn8/NiX/nbO3xWqGvrqLuEABehAQAAKQ0J9gQkWnsNXmKxVXxFkRkFHSqXTzS13kp\negGR2rLM+z91lMXoE2kJLjy7+Z9V/MaIiPhfdsgO9JWGzCbSWlfcM9b3JNebTbQksXHchYgM\n1p16PFIx9PfPV8wgYlbndMid/3K0HpFhTMXw2IU6Tka+am678dSEVAAwqbAUCwAgo62tjYhM\nTU3lKa4WFtwksv9oj/fM0X/3NN3iY1yoryz/xH2FhSRirxAEz6POoqzjHSNdT4qEpf3KLuEh\nluMLzcO2es8aaSibhIR7MTRQXlYpd36GYYgeV5fXj649k7ZPZv3N6nRvbPsDmFowsQMAkMEw\nDBENDw/LUSuqr79LNMvObq5Ut7GT0xySVFfXKCTgT1jOUZE2rP6SrPwHTzvai4QX+tXejggw\nkipbvNhmfFvd3NyEqOvOnXZ58ztGf+KlO3R1hz3Phv/+1qTskuofexR54AsAvDBM7AAAZBgZ\nGRHR7ZYWOWpFIhERtX/hIXPQ3Oup94lEbW09Ck1qHiZ4U3XoUtbhFiKitmMFFwc11ob76UgV\ncbW0pHf6cTgcIurt7ZU3P8ss8sz3lVnxv7IaqCpM3hb2jr2pHm/5h9m1IoXeHQD8fHh5AgBA\nhpGrK4+uthafqEh1c33Ot6SmpiYRzVoeu8vHZOKosYOCn571AwXesWXHD+fWbdutd1RYPqwX\nFr6KK13T39srIRr3Fkh3dzcRcblckj+/ssFrYZ++FvapuPve9StfnzuacSDnYJjnY81G4buz\nFXNrAPAiMLEDAJDl+H7wwuSdjZmbk8Irt9swE8bbioL5CV1r9h1M4M+xtubRuX/MdBJsXKs6\nCVE1fQXv6R7PEgq/j9DLrxjmbQn3kI0x2NjYQj5mo+2+5uZ7RDrz5+sQEUe+/EPdPWIuhyFS\n4prY8kNs+YFe+ovs9hYdLRt61w8/JABTB5ZiAQAmsIr9apu1Sl/V7zz5O//SOv5Et4GHlQf8\nXQMO197p1DczIKIl/u9ZUH9xSmrDwFhVb81u91eW+h6slWeb3n+HWS4IXUC3ChO35H0nsQoL\nd5p4QEtNTnrl6GsP4gd5OecHicvnuxDJlV989WOLGZr227+VWlaWSIhIWUUFvyIAUwoetAAA\nJlJz3lNycsg/OKU8YZX5gfmOLrbzZzO9j+/e+PZqU8cQw/NNEWbF8JSIiGUb/+Vvz/CT4xyX\nfBPq52qhy2pvuFiQW9Ks+kayh+KOsRvDcoiKXLp/27FjPyi7pEm/DvuUtsucipUOa8ICPC10\nRA2nM/90oUvdbs92n6crts/Pr+QU+IHDZ5tTVixtWO/nZqnPEXfeqz2bV3CLsYyNfhsTO4Cp\nZbLPWwEAmLIGH/7tyL7oNc6L5hloqTGcmcZmi5etj8sobe6SKeysy/94nauloSZbla2hN9fm\nrYjfF7f0jY0r5By7MY8yPBkiZuVX/5QZKIvRJ9L+4K+PylNC3Sz0ZzAMZ7aZW2hqpUzh8/JL\nuuoLdwYvs37FQFudUdMy5Fl7BO0S1rTLdQMA8AtiSSR4Zx0AYDqTNCXaWe34Maj0bjZfXWqk\nfKOBx2d9ggtPMjwnKRsA/LLwJzoAwLTWX7c/al+NkuPWOJlZHQD8H8IeOwCA6enm6f2nau9f\nO5JVdEPimJi5yWKyAwHA5MPEDgBgevqhNCnhkIgxtgv9Y1rKh4sn47AVAJhqsMcOAAAA4CWB\nPXYAAAAALwlM7AAAAABeEv8GRbxcONqL+MwAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “Color Key\n",
       "and Histogram”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(\"DoubletDecon\")\n",
    "location=\"/media/garret/New Volume/scRNAseq_data/Yoshiko/180725LHb/all_cells/gene2000_30/doublet/\" #Update as needed \n",
    "expressionFile=paste0(location, \"counts.txt\")\n",
    "genesFile=paste0(location, \"Top50Genes.txt\")\n",
    "clustersFile=paste0(location, \"Cluster.txt\")\n",
    "\n",
    "newFiles=Seurat_Pre_Process(expressionFile, genesFile, clustersFile)\n",
    "\n",
    "results=Main_Doublet_Decon(rawDataFile=newFiles$newExpressionFile, \n",
    "                           groupsFile=newFiles$newGroupsFile, \n",
    "                           filename=\"cntl\", \n",
    "                           location=location,\n",
    "                           fullDataFile=NULL, \n",
    "                           removeCC=FALSE, \n",
    "                           species=\"mmu\", \n",
    "                           rhop=1.1, \n",
    "                           write=TRUE, \n",
    "                           PMF=TRUE, \n",
    "                           useFull=FALSE, \n",
    "                           heatmap=FALSE,\n",
    "                           centroids=TRUE,\n",
    "                           num_doubs=100, \n",
    "                           only50=TRUE,\n",
    "                           min_uniq=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading packages...\n",
      "Reading data...\n",
      "Processing raw data...\n",
      "Combining similar clusters...\n",
      "Creating synthetic doublet profiles...\n",
      "Step 1: Removing possible doublets...\n",
      "Step 2: Re-clustering possible doublets...\n",
      "Step 3: Rescuing cells with unique gene expression...\n",
      "Finished!\n"
     ]
    },
    {
     "data": {
      "image/png": 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BAAAAqDCmgl3DIcOcTwV1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\nOkHfcvmRYg4AAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “Color Key\n",
       "and Histogram”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(\"DoubletDecon\")\n",
    "location=\"/media/garret/New Volume/scRNAseq_data/Yoshiko/180905LHb/all_cells/gene2000_30/doublet/\" #Update as needed \n",
    "expressionFile=paste0(location, \"counts.txt\")\n",
    "genesFile=paste0(location, \"Top50Genes.txt\")\n",
    "clustersFile=paste0(location, \"Cluster.txt\")\n",
    "\n",
    "newFiles=Seurat_Pre_Process(expressionFile, genesFile, clustersFile)\n",
    "\n",
    "results=Main_Doublet_Decon(rawDataFile=newFiles$newExpressionFile, \n",
    "                           groupsFile=newFiles$newGroupsFile, \n",
    "                           filename=\"cntl\", \n",
    "                           location=location,\n",
    "                           fullDataFile=NULL, \n",
    "                           removeCC=FALSE, \n",
    "                           species=\"mmu\", \n",
    "                           rhop=1.1, \n",
    "                           write=TRUE, \n",
    "                           PMF=TRUE, \n",
    "                           useFull=FALSE, \n",
    "                           heatmap=FALSE,\n",
    "                           centroids=TRUE,\n",
    "                           num_doubs=100, \n",
    "                           only50=TRUE,\n",
    "                           min_uniq=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}