{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: MASS\n",
      "Loading required package: mclust\n",
      "Package 'mclust' version 5.4.3\n",
      "Type 'citation(\"mclust\")' for citing this R package in publications.\n"
     ]
    }
   ],
   "source": [
    "library(prabclus)\n",
    "library(Matrix)\n",
    "library(Rtsne)\n",
    "library(ggplot2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocess"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`bsub < count_reads_peaks.sh`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "5335"
      ],
      "text/latex": [
       "5335"
      ],
      "text/markdown": [
       "5335"
      ],
      "text/plain": [
       "[1] 5335"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "path = './count_reads_peaks_output/'\n",
    "files <- list.files(path,pattern = \"\\\\.txt$\")\n",
    "length(files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#assuming tab separated values with a header    \n",
    "datalist = lapply(files, function(x)fread(paste0(path,x))$V4) \n",
    "#assuming the same header/columns for all files\n",
    "datafr = do.call(\"cbind\", datalist) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>97998</li>\n",
       "\t<li>5335</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 97998\n",
       "\\item 5335\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 97998\n",
       "2. 5335\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 97998  5335"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(datafr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_regions = read.csv(\"../../input/atac_v1_pbmc_5k_peaks.bed\",\n",
    "                      sep = '\\t',header=FALSE,stringsAsFactors=FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>97998</li>\n",
       "\t<li>3</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 97998\n",
       "\\item 3\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 97998\n",
       "2. 3\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 97998     3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(df_regions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "peaknames = paste(df_regions$V1,df_regions$V2,df_regions$V3,sep = \"_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'chr1_10244_10510'</li>\n",
       "\t<li>'chr1_237575_237942'</li>\n",
       "\t<li>'chr1_565098_565554'</li>\n",
       "\t<li>'chr1_569172_569645'</li>\n",
       "\t<li>'chr1_713421_715095'</li>\n",
       "\t<li>'chr1_752386_753061'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'chr1\\_10244\\_10510'\n",
       "\\item 'chr1\\_237575\\_237942'\n",
       "\\item 'chr1\\_565098\\_565554'\n",
       "\\item 'chr1\\_569172\\_569645'\n",
       "\\item 'chr1\\_713421\\_715095'\n",
       "\\item 'chr1\\_752386\\_753061'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'chr1_10244_10510'\n",
       "2. 'chr1_237575_237942'\n",
       "3. 'chr1_565098_565554'\n",
       "4. 'chr1_569172_569645'\n",
       "5. 'chr1_713421_715095'\n",
       "6. 'chr1_752386_753061'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"chr1_10244_10510\"   \"chr1_237575_237942\" \"chr1_565098_565554\"\n",
       "[4] \"chr1_569172_569645\" \"chr1_713421_715095\" \"chr1_752386_753061\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(peaknames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'AAACGAAAGCGCAATG-1'</li>\n",
       "\t<li>'AAACGAAAGGGTATCG-1'</li>\n",
       "\t<li>'AAACGAAAGTAACATG-1'</li>\n",
       "\t<li>'AAACGAAAGTTACACC-1'</li>\n",
       "\t<li>'AAACGAACAGAGATGC-1'</li>\n",
       "\t<li>'AAACGAACATGCTATG-1'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'AAACGAAAGCGCAATG-1'\n",
       "\\item 'AAACGAAAGGGTATCG-1'\n",
       "\\item 'AAACGAAAGTAACATG-1'\n",
       "\\item 'AAACGAAAGTTACACC-1'\n",
       "\\item 'AAACGAACAGAGATGC-1'\n",
       "\\item 'AAACGAACATGCTATG-1'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'AAACGAAAGCGCAATG-1'\n",
       "2. 'AAACGAAAGGGTATCG-1'\n",
       "3. 'AAACGAAAGTAACATG-1'\n",
       "4. 'AAACGAAAGTTACACC-1'\n",
       "5. 'AAACGAACAGAGATGC-1'\n",
       "6. 'AAACGAACATGCTATG-1'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"AAACGAAAGCGCAATG-1\" \"AAACGAAAGGGTATCG-1\" \"AAACGAAAGTAACATG-1\"\n",
       "[4] \"AAACGAAAGTTACACC-1\" \"AAACGAACAGAGATGC-1\" \"AAACGAACATGCTATG-1\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(sapply(strsplit(files,'\\\\.'),'[', 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(datafr) = sapply(strsplit(files,'\\\\.'),'[', 2)\n",
    "rownames(datafr) = peaknames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>AAACGAAAGCGCAATG-1</th><th scope=col>AAACGAAAGGGTATCG-1</th><th scope=col>AAACGAAAGTAACATG-1</th><th scope=col>AAACGAAAGTTACACC-1</th><th scope=col>AAACGAACAGAGATGC-1</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>chr1_10244_10510</th><td>1 </td><td>0 </td><td> 1</td><td>6 </td><td>0 </td></tr>\n",
       "\t<tr><th scope=row>chr1_237575_237942</th><td>0 </td><td>0 </td><td> 0</td><td>0 </td><td>0 </td></tr>\n",
       "\t<tr><th scope=row>chr1_565098_565554</th><td>0 </td><td>0 </td><td> 6</td><td>0 </td><td>0 </td></tr>\n",
       "\t<tr><th scope=row>chr1_569172_569645</th><td>0 </td><td>0 </td><td> 8</td><td>6 </td><td>0 </td></tr>\n",
       "\t<tr><th scope=row>chr1_713421_715095</th><td>4 </td><td>4 </td><td>10</td><td>4 </td><td>0 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       "  & AAACGAAAGCGCAATG-1 & AAACGAAAGGGTATCG-1 & AAACGAAAGTAACATG-1 & AAACGAAAGTTACACC-1 & AAACGAACAGAGATGC-1\\\\\n",
       "\\hline\n",
       "\tchr1\\_10244\\_10510 & 1  & 0  &  1 & 6  & 0 \\\\\n",
       "\tchr1\\_237575\\_237942 & 0  & 0  &  0 & 0  & 0 \\\\\n",
       "\tchr1\\_565098\\_565554 & 0  & 0  &  6 & 0  & 0 \\\\\n",
       "\tchr1\\_569172\\_569645 & 0  & 0  &  8 & 6  & 0 \\\\\n",
       "\tchr1\\_713421\\_715095 & 4  & 4  & 10 & 4  & 0 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | AAACGAAAGCGCAATG-1 | AAACGAAAGGGTATCG-1 | AAACGAAAGTAACATG-1 | AAACGAAAGTTACACC-1 | AAACGAACAGAGATGC-1 | \n",
       "|---|---|---|---|---|\n",
       "| chr1_10244_10510 | 1  | 0  |  1 | 6  | 0  | \n",
       "| chr1_237575_237942 | 0  | 0  |  0 | 0  | 0  | \n",
       "| chr1_565098_565554 | 0  | 0  |  6 | 0  | 0  | \n",
       "| chr1_569172_569645 | 0  | 0  |  8 | 6  | 0  | \n",
       "| chr1_713421_715095 | 4  | 4  | 10 | 4  | 0  | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "                   AAACGAAAGCGCAATG-1 AAACGAAAGGGTATCG-1 AAACGAAAGTAACATG-1\n",
       "chr1_10244_10510   1                  0                   1                \n",
       "chr1_237575_237942 0                  0                   0                \n",
       "chr1_565098_565554 0                  0                   6                \n",
       "chr1_569172_569645 0                  0                   8                \n",
       "chr1_713421_715095 4                  4                  10                \n",
       "                   AAACGAAAGTTACACC-1 AAACGAACAGAGATGC-1\n",
       "chr1_10244_10510   6                  0                 \n",
       "chr1_237575_237942 0                  0                 \n",
       "chr1_565098_565554 0                  0                 \n",
       "chr1_569172_569645 6                  0                 \n",
       "chr1_713421_715095 4                  0                 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datafr[1:5,1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>97998</li>\n",
       "\t<li>5335</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 97998\n",
       "\\item 5335\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 97998\n",
       "2. 5335\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 97998  5335"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(datafr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# saveRDS(datafr, file = './datafr.rds')\n",
    "# datafr = readRDS('./datafr.rds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "filter_peaks <- function (datafr,cutoff = 0.01){\n",
    "    binary_mat = as.matrix((datafr > 0) + 0)\n",
    "    binary_mat = Matrix(binary_mat, sparse = TRUE) \n",
    "    num_cells_ncounted = Matrix::rowSums(binary_mat)\n",
    "    ncounts = binary_mat[num_cells_ncounted >= dim(binary_mat)[2]*cutoff,]\n",
    "    ncounts = ncounts[rowSums(ncounts) > 0,]    \n",
    "    \n",
    "    options(repr.plot.width=4, repr.plot.height=4)\n",
    "    hist(log10(num_cells_ncounted),main=\"No. of Cells Each Site is Observed In\",breaks=50)\n",
    "    abline(v=log10(min(num_cells_ncounted[num_cells_ncounted >= dim(binary_mat)[2]*cutoff])),lwd=2,col=\"indianred\")\n",
    "#     hist(log10(new_counts),main=\"Number of Sites Each Cell Uses\",breaks=50)\n",
    "    datafr_filtered = datafr[rownames(ncounts),]\n",
    "    return(datafr_filtered)\n",
    "}\n",
    "\n",
    "\n",
    "getJaccardDist <- function(cdBinary){\n",
    "        \n",
    "    if(colnames(cdBinary[,2:3])[1] == 'start' && colnames(cdBinary[,2:3])[2] == 'end'){\n",
    "        SingleCell.Binary <- cdBinary[,4:(dim(cdBinary)[2])]\n",
    "    }\n",
    "    else\n",
    "        SingleCell.Binary <- cdBinary\n",
    "    \n",
    "    \n",
    "    SingleCell.Binary.Jaccard <- jaccard(as.matrix(SingleCell.Binary))\n",
    "    \n",
    "    return(SingleCell.Binary.Jaccard)\n",
    "}\n",
    "\n",
    "\n",
    "plot_tSNE <- function(cdBinary, nDimToUSE, groups=NULL, cellName, perplexity_division, ret.val=FALSE , text.label=FALSE, title=\"\"){\n",
    "\n",
    "    if(missing(nDimToUSE)){\n",
    "        stop(\"ERROR: Number of PCA's \\\"nDimToUSE\\\" to use is missing\")\n",
    "    }\n",
    "    if(missing(ret.val)){\n",
    "        ret.val = FALSE\n",
    "    }\n",
    "    \n",
    "    if(text.label==TRUE & missing(cellName)){\n",
    "        stop(\"ERROR: Please give cellName\")\n",
    "    }\n",
    "    \n",
    "    if(missing(perplexity_division)){\n",
    "        stop(\"ERROR: Please enter the number with which you want to divide the dimension of your data for perplexity setting\")\n",
    "    }\n",
    "        \n",
    "    \n",
    "    #SingleCell.Binary.Jaccard <- getJaccardDist(cdBinary)\n",
    "    #FinalPCAData <- t(SingleCell.Binary.Jaccard)\n",
    "    \n",
    "    SingleCell.Binary.Jaccard <- getJaccardDist(cdBinary)\n",
    "    fit <- cmdscale(as.dist(SingleCell.Binary.Jaccard),eig=TRUE, k=nDimToUSE)\n",
    "\n",
    "    #pcaPRComp <- prcomp(FinalPCAData)\n",
    "\n",
    "    \n",
    "    #rtsne_pca_out <- Rtsne(as.matrix(pcaPRComp$x[,1:nPCAToUSE]), perplexity = dim(pcaPRComp$x)[1]/perplexity_division, check_duplicates = FALSE)\n",
    "    rtsne_pca_out <- Rtsne(as.matrix(fit$points[,1:nDimToUSE]), perplexity = perplexity_division, \n",
    "                           check_duplicates = FALSE, pca=FALSE, theta=0.01, max_iter=3000)\n",
    "    \n",
    "    if(is.null(groups)){\n",
    "        if(ret.val==TRUE)\n",
    "            df<-data.frame(X=rtsne_pca_out$Y[,1],Y=rtsne_pca_out$Y[,2], cellName=cellName)\n",
    "        else\n",
    "            df<-data.frame(X=rtsne_pca_out$Y[,1],Y=rtsne_pca_out$Y[,2])\n",
    "        p1 <- ggplot(df, aes_string(x=\"X\",y =\"Y\"))\n",
    "    }\n",
    "    else{\n",
    "        if(ret.val==TRUE)\n",
    "            df<-data.frame(X=rtsne_pca_out$Y[,1],Y=rtsne_pca_out$Y[,2], Cell=colnames(SingleCell.Binary.Jaccard), Batch=groups, cellName=cellName)\n",
    "        else\n",
    "            df<-data.frame(X=rtsne_pca_out$Y[,1],Y=rtsne_pca_out$Y[,2], Cell=colnames(SingleCell.Binary.Jaccard), Batch=groups)\n",
    "        p1 <- ggplot(df, aes_string(x=\"X\",y =\"Y\", color=\"Batch\"))\n",
    "    }\n",
    "    \n",
    "\n",
    "    p1<-p1 + ggtitle(\"t-SNE plot\")\n",
    "    p1<-p1 + geom_point(size = 2) \n",
    "    p1<-p1 + xlab(paste(\"Dim-1\"))\n",
    "    p1<-p1 + ylab(\"Dim-2\")+\n",
    "        theme_light(base_size=24) +\n",
    "        theme(strip.background = element_blank(),\n",
    "              panel.border     = element_blank(),\n",
    "              plot.title = element_text(hjust = 0.5))\n",
    "#     if(length(levels(groups)) < 12)\n",
    "#         p1 <- p1 + scale_colour_brewer(palette=\"Set1\")\n",
    "    if(text.label==TRUE)\n",
    "        p1<-p1 + geom_text(data=df,aes(label=cellName),alpha=0.5,size=4, vjust=1,hjust=0.5,angle=45)\n",
    "    print(p1)\n",
    "    return(df)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Obtain Feature Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_time <- Sys.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata <- read.table('../../input/metadata.tsv',\n",
    "                         header = TRUE,\n",
    "                         stringsAsFactors=FALSE,quote=\"\",row.names=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OLFi0uXLkVGRj558mTs2LH169ePiIi4fPmy9kpks7Oza9y4cbNmzRwdHbOzs9PT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\nAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “No. of Cells Each Site is Observed In”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datafr_filtered <- filter_peaks(datafr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>69319</li>\n",
       "\t<li>5335</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 69319\n",
       "\\item 5335\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 69319\n",
       "2. 5335\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 69319  5335"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(datafr_filtered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "cdBinary = as.matrix((datafr_filtered > 0) + 0)\n",
    "cdBinary = Matrix(cdBinary, sparse = TRUE) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "## use the similar number of dimensions as shown in tutorial https://github.com/ManchesterBioinference/Scasat/blob/master/ScAsAT_functions_Buenrostro_All_Bam_Together.ipynb\n",
    "k = 15\n",
    "SingleCell.Binary.Jaccard <- getJaccardDist(cdBinary)\n",
    "fit <- cmdscale(as.dist(SingleCell.Binary.Jaccard),eig=TRUE, k=k)\n",
    "fm_Scasat = t(fit$points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>15</li>\n",
       "\t<li>5335</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 15\n",
       "\\item 5335\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 15\n",
       "2. 5335\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1]   15 5335"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>AAACGAAAGCGCAATG-1</th><th scope=col>AAACGAAAGGGTATCG-1</th><th scope=col>AAACGAAAGTAACATG-1</th><th scope=col>AAACGAAAGTTACACC-1</th><th scope=col>AAACGAACAGAGATGC-1</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>-0.051167899</td><td>-0.039053205</td><td>-0.24040989 </td><td>-0.22332404 </td><td> 0.094020841</td></tr>\n",
       "\t<tr><td>-0.147709843</td><td>-0.161547157</td><td> 0.04736274 </td><td> 0.04024757 </td><td>-0.100804918</td></tr>\n",
       "\t<tr><td>-0.006473506</td><td> 0.035480921</td><td> 0.06608409 </td><td> 0.07443681 </td><td>-0.021050537</td></tr>\n",
       "\t<tr><td> 0.010683836</td><td> 0.017649322</td><td>-0.01767384 </td><td>-0.06423900 </td><td>-0.013695877</td></tr>\n",
       "\t<tr><td> 0.027035383</td><td> 0.001416199</td><td> 0.00852579 </td><td>-0.01542106 </td><td> 0.001180007</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{lllll}\n",
       " AAACGAAAGCGCAATG-1 & AAACGAAAGGGTATCG-1 & AAACGAAAGTAACATG-1 & AAACGAAAGTTACACC-1 & AAACGAACAGAGATGC-1\\\\\n",
       "\\hline\n",
       "\t -0.051167899 & -0.039053205 & -0.24040989  & -0.22332404  &  0.094020841\\\\\n",
       "\t -0.147709843 & -0.161547157 &  0.04736274  &  0.04024757  & -0.100804918\\\\\n",
       "\t -0.006473506 &  0.035480921 &  0.06608409  &  0.07443681  & -0.021050537\\\\\n",
       "\t  0.010683836 &  0.017649322 & -0.01767384  & -0.06423900  & -0.013695877\\\\\n",
       "\t  0.027035383 &  0.001416199 &  0.00852579  & -0.01542106  &  0.001180007\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "AAACGAAAGCGCAATG-1 | AAACGAAAGGGTATCG-1 | AAACGAAAGTAACATG-1 | AAACGAAAGTTACACC-1 | AAACGAACAGAGATGC-1 | \n",
       "|---|---|---|---|---|\n",
       "| -0.051167899 | -0.039053205 | -0.24040989  | -0.22332404  |  0.094020841 | \n",
       "| -0.147709843 | -0.161547157 |  0.04736274  |  0.04024757  | -0.100804918 | \n",
       "| -0.006473506 |  0.035480921 |  0.06608409  |  0.07443681  | -0.021050537 | \n",
       "|  0.010683836 |  0.017649322 | -0.01767384  | -0.06423900  | -0.013695877 | \n",
       "|  0.027035383 |  0.001416199 |  0.00852579  | -0.01542106  |  0.001180007 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "     AAACGAAAGCGCAATG-1 AAACGAAAGGGTATCG-1 AAACGAAAGTAACATG-1\n",
       "[1,] -0.051167899       -0.039053205       -0.24040989       \n",
       "[2,] -0.147709843       -0.161547157        0.04736274       \n",
       "[3,] -0.006473506        0.035480921        0.06608409       \n",
       "[4,]  0.010683836        0.017649322       -0.01767384       \n",
       "[5,]  0.027035383        0.001416199        0.00852579       \n",
       "     AAACGAAAGTTACACC-1 AAACGAACAGAGATGC-1\n",
       "[1,] -0.22332404         0.094020841      \n",
       "[2,]  0.04024757        -0.100804918      \n",
       "[3,]  0.07443681        -0.021050537      \n",
       "[4,] -0.06423900        -0.013695877      \n",
       "[5,] -0.01542106         0.001180007      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(fm_Scasat)\n",
    "fm_Scasat[1:5,1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "end_time <- Sys.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time difference of 1.80813 mins"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "end_time - start_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>AAACGAAAGCGCAATG-1</th><th scope=col>AAACGAAAGGGTATCG-1</th><th scope=col>AAACGAAAGTAACATG-1</th><th scope=col>AAACGAAAGTTACACC-1</th><th scope=col>AAACGAACAGAGATGC-1</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Dim 1</th><td>-0.051167899</td><td>-0.039053205</td><td>-0.24040989 </td><td>-0.22332404 </td><td> 0.094020841</td></tr>\n",
       "\t<tr><th scope=row>Dim 2</th><td>-0.147709843</td><td>-0.161547157</td><td> 0.04736274 </td><td> 0.04024757 </td><td>-0.100804918</td></tr>\n",
       "\t<tr><th scope=row>Dim 3</th><td>-0.006473506</td><td> 0.035480921</td><td> 0.06608409 </td><td> 0.07443681 </td><td>-0.021050537</td></tr>\n",
       "\t<tr><th scope=row>Dim 4</th><td> 0.010683836</td><td> 0.017649322</td><td>-0.01767384 </td><td>-0.06423900 </td><td>-0.013695877</td></tr>\n",
       "\t<tr><th scope=row>Dim 5</th><td> 0.027035383</td><td> 0.001416199</td><td> 0.00852579 </td><td>-0.01542106 </td><td> 0.001180007</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       "  & AAACGAAAGCGCAATG-1 & AAACGAAAGGGTATCG-1 & AAACGAAAGTAACATG-1 & AAACGAAAGTTACACC-1 & AAACGAACAGAGATGC-1\\\\\n",
       "\\hline\n",
       "\tDim 1 & -0.051167899 & -0.039053205 & -0.24040989  & -0.22332404  &  0.094020841\\\\\n",
       "\tDim 2 & -0.147709843 & -0.161547157 &  0.04736274  &  0.04024757  & -0.100804918\\\\\n",
       "\tDim 3 & -0.006473506 &  0.035480921 &  0.06608409  &  0.07443681  & -0.021050537\\\\\n",
       "\tDim 4 &  0.010683836 &  0.017649322 & -0.01767384  & -0.06423900  & -0.013695877\\\\\n",
       "\tDim 5 &  0.027035383 &  0.001416199 &  0.00852579  & -0.01542106  &  0.001180007\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | AAACGAAAGCGCAATG-1 | AAACGAAAGGGTATCG-1 | AAACGAAAGTAACATG-1 | AAACGAAAGTTACACC-1 | AAACGAACAGAGATGC-1 | \n",
       "|---|---|---|---|---|\n",
       "| Dim 1 | -0.051167899 | -0.039053205 | -0.24040989  | -0.22332404  |  0.094020841 | \n",
       "| Dim 2 | -0.147709843 | -0.161547157 |  0.04736274  |  0.04024757  | -0.100804918 | \n",
       "| Dim 3 | -0.006473506 |  0.035480921 |  0.06608409  |  0.07443681  | -0.021050537 | \n",
       "| Dim 4 |  0.010683836 |  0.017649322 | -0.01767384  | -0.06423900  | -0.013695877 | \n",
       "| Dim 5 |  0.027035383 |  0.001416199 |  0.00852579  | -0.01542106  |  0.001180007 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "      AAACGAAAGCGCAATG-1 AAACGAAAGGGTATCG-1 AAACGAAAGTAACATG-1\n",
       "Dim 1 -0.051167899       -0.039053205       -0.24040989       \n",
       "Dim 2 -0.147709843       -0.161547157        0.04736274       \n",
       "Dim 3 -0.006473506        0.035480921        0.06608409       \n",
       "Dim 4  0.010683836        0.017649322       -0.01767384       \n",
       "Dim 5  0.027035383        0.001416199        0.00852579       \n",
       "      AAACGAAAGTTACACC-1 AAACGAACAGAGATGC-1\n",
       "Dim 1 -0.22332404         0.094020841      \n",
       "Dim 2  0.04024757        -0.100804918      \n",
       "Dim 3  0.07443681        -0.021050537      \n",
       "Dim 4 -0.06423900        -0.013695877      \n",
       "Dim 5 -0.01542106         0.001180007      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rownames(fm_Scasat) = paste('Dim',1:dim(fm_Scasat)[1])\n",
    "fm_Scasat[1:5,1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "TRUE"
      ],
      "text/latex": [
       "TRUE"
      ],
      "text/markdown": [
       "TRUE"
      ],
      "text/plain": [
       "[1] TRUE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all(colnames(fm_Scasat) == rownames(metadata))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "saveRDS(fm_Scasat, file = '../../output/feature_matrices/FM_Scasat_10xpbmc5k.rds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Downstream Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+zz6llV6Jx/uKW6xv90PAKe6kZt6GeyV4thGIZhfPYXtPBUjyRJOvhwvcPk5uYu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ik/BwB4d34xdHiD+iwEQRQVFTGZzODgYGvzy3D4qxIy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/Dh06BABhYWGXLl0yPSyeeujibQ5j3bp1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S0d43rl5+e7KnPt2jV+ISEhQYyYfC0gsipoKSay4S/cvddEequcD6brCq6qJROxCvtw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\nRK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=6, repr.plot.height=4)\n",
    "set.seed(2019)\n",
    "tsNE_out_2PCs <- plot_tSNE(cdBinary, k, groups=as.character(metadata$label), perplexity=30, \n",
    "                              cellName=rownames(metadata),ret.val=TRUE, text.label=F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R version 3.5.1 (2018-07-02)\n",
       "Platform: x86_64-conda_cos6-linux-gnu (64-bit)\n",
       "Running under: CentOS Linux 7 (Core)\n",
       "\n",
       "Matrix products: default\n",
       "BLAS/LAPACK: /data/pinello/SHARED_SOFTWARE/anaconda3/envs/ATACseq_scasat/lib/R/lib/libRblas.so\n",
       "\n",
       "locale:\n",
       " [1] LC_CTYPE=en_US.UTF-8       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] stats     graphics  grDevices utils     datasets  methods   base     \n",
       "\n",
       "other attached packages:\n",
       "[1] ggplot2_3.0.0  Rtsne_0.15     Matrix_1.2-14  prabclus_2.2-7 mclust_5.4.3  \n",
       "[6] MASS_7.3-50   \n",
       "\n",
       "loaded via a namespace (and not attached):\n",
       " [1] Rcpp_1.0.1       compiler_3.5.1   pillar_1.3.1     plyr_1.8.4      \n",
       " [5] bindr_0.1.1      base64enc_0.1-3  tools_3.5.1      digest_0.6.18   \n",
       " [9] uuid_0.1-2       jsonlite_1.5     evaluate_0.11    tibble_2.1.1    \n",
       "[13] gtable_0.2.0     lattice_0.20-35  pkgconfig_2.0.2  rlang_0.3.3     \n",
       "[17] IRdisplay_0.5.0  IRkernel_0.8.12  bindrcpp_0.2.2   withr_2.1.2     \n",
       "[21] repr_0.15.0      stringr_1.3.1    dplyr_0.7.6      tidyselect_0.2.5\n",
       "[25] grid_3.5.1       glue_1.3.1       R6_2.4.0         pbdZMQ_0.3-3    \n",
       "[29] purrr_0.3.2      magrittr_1.5     scales_0.5.0     htmltools_0.3.6 \n",
       "[33] assertthat_0.2.1 colorspace_1.3-2 labeling_0.3     stringi_1.2.4   \n",
       "[37] lazyeval_0.2.1   munsell_0.5.0    crayon_1.3.4    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sessionInfo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "save.image(file = 'Scasat_10xpbmc5k.RData')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "name": "conda-env-ATACseq_scasat-r"
  },
  "language_info": {
   "codemirror_mode": "r",
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