ScAsAT_functions_Buenrostro_Breaking_into_Chunks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Apllication of Scasat to Buenrostro data\n",
    "\n",
    "This is the python notebook that uses __S__ingle-__c__ell __A__TAC-seq __A__nalysis __T__ool (Scasat) to process single cell ATAC-seq data from [_Buenrostro et. al._](https://www.nature.com/articles/nature14590) which is publicly available. In this anlaysis we only used the human data from this public data.\n",
    "\n",
    "__Prerequisites:__\n",
    "\n",
    "In order to use Scasat, you have to have the following tools installed in the machine where you are running Scasat\n",
    "1. [samtools](http://www.htslib.org)\n",
    "2. [bedtools](http://bedtools.readthedocs.io/en/latest/)\n",
    "3. [macs2](https://github.com/taoliu/MACS)\n",
    "\n",
    "If you are using jupyter from anaconda installation then you can install the tools with the following command\n",
    "* samtools: conda install -c bioconda samtools\n",
    "* bedtools: conda install -c bioconda bedtools \n",
    "* macs2: conda install -c bioconda macs2 (please note that macs2 only runs on python2.7 so you have to create a python2.7 environment if you are using python3\n",
    "\n",
    "This jupyter notebook introduces __S__ingle-__c__ell __A__TAC-seq __A__nalysis __T__ool (Scasat) to deconvolute three cell types from a complex cell mixture. Scasat analyses single cell ATAC-seq data to identify regulatory regions that separates cells from a complex population. This notebook is the tool mentioned in [___Murtuza Baker et. al.___]()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import python packages\n",
    "\n",
    "We import the python packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import subprocess, os, csv, signal, rpy2\n",
    "import numpy as np\n",
    "import readline\n",
    "import rpy2.robjects\n",
    "import matplotlib.pyplot as plt\n",
    "import csv\n",
    "from operator import itemgetter\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code toggles between showing/hiding the codes. The outputs and the discussions would always be visible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "rpy2 package provides a simple and robust access to `R` from within `Python`. The following cells loads the package to run `R` code within `Python` notebook. To write and execute `R` code we will use the magic command `%%R` at the top of the cell. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python Functions\n",
    "\n",
    "Below we define the python functions for Scasat. One of the advantage of Scasat is the use of both python and R programming language. We use python for file processing and R for doing the data analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def formatSummitBed(summitFile, fileToSave):\n",
    "#fileToSave = 'location_toSave/AllFiles_Filtered_summits_shifted.bed' \n",
    "#peakFile = 'location_PeakFile/AllFiles_Filtered_summits.bed'\n",
    "\n",
    "    with open(summitFile,'r') as f:\n",
    "        peaks=[x.strip().split('\\t') for x in f]\n",
    "    for i in range(len(peaks)):\n",
    "        peaks[i][1] = str(int(peaks[i][1]) - 250)\n",
    "        peaks[i][2] = str(int(peaks[i][2]) + 250)\n",
    "\n",
    "    peaks.sort(key=lambda x: float(x[4]), reverse=True)\n",
    "\n",
    "    with open(fileToSave, \"w\") as f:\n",
    "        writer = csv.writer(f,delimiter='\\t')\n",
    "        writer.writerows(peaks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`formatSummitBed()`: \n",
    "> This functions formats the summit file by adding 500 bp distance from the summit position.\n",
    "\n",
    "Arguments:\n",
    "> `summitFile` : The name of the summit file. This has to be the file that is generated from the aggreagated `BAM` file<br>\n",
    "> `fileToSave`: The name of the file where the save the peak file with the 500 bp peaks.\n",
    "\n",
    "Value:\n",
    "> File with 500 bp peaks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_chunks(l, n):\n",
    "    # Codes modified from stackOverflow,\n",
    "    # For item i in a range that is a length of l,\n",
    "    for i in range(0, len(l), n):\n",
    "        # Create an index range for l of n items:\n",
    "        yield l[i:i+n]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`create_chunks()`:\n",
    "> Divides items in `l` into chunks with size `n`.\n",
    "\n",
    "Arguements:\n",
    "> `l`: Array of elements. <br>\n",
    "> `n`: Number of elements in each chunk .\n",
    "\n",
    "Value:\n",
    "> Returns arrays with `n` elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getMergedBamSerial(BAMFolder, BAMFilePattern):\n",
    "    \n",
    "    import pandas as pd    \n",
    "        \n",
    "    mergeFiles = ''\n",
    "    for root, folder, files in os.walk(BAMFolder):\n",
    "            files = [os.path.join(root, f) for f in files if (f.endswith(BAMFilePattern))]\n",
    "    \n",
    "    #print(files)\n",
    "    BamFileList = list(create_chunks(files,100))\n",
    "    \n",
    "    fileIndex = 0\n",
    "    for bamFiles in BamFileList:\n",
    "        mergeFiles = ''\n",
    "        for f in bamFiles:\n",
    "            mergeFiles = mergeFiles +  f + ' '\n",
    "        mergeCommand = 'samtools merge ' + BAMFolder + str(fileIndex) + '_part_merged_nodup.bam' + ' ' + mergeFiles \n",
    "        fileIndex = fileIndex + 1\n",
    "        #print(mergeCommand)\n",
    "        subprocess.call(mergeCommand, shell=True)   \n",
    "    \n",
    "    mergeFiles = ''\n",
    "    for root, folder, files in os.walk(BAMFolder):\n",
    "            files = [os.path.join(root, f) for f in files if (f.endswith('_part_merged_nodup.bam'))]\n",
    "            for f in files:\n",
    "               mergeFiles = mergeFiles +  f + ' '\n",
    "\n",
    "        \n",
    "    mergeCommand = 'samtools merge ' + BAMFolder + 'AllFiles_Filtered_Merged_nodup.bam' + ' ' + mergeFiles    \n",
    "    subprocess.call(mergeCommand, shell=True)     \n",
    "    \n",
    "    indexMergeCmd = 'samtools index ' + BAMFolder + 'AllFiles_Filtered_Merged_nodup.bam'\n",
    "    subprocess.call(indexMergeCmd, shell=True)\n",
    "    \n",
    "    cleanCommand = 'samtools view -b ' + BAMFolder + 'AllFiles_Filtered_Merged_nodup.bam' + ' ' \\\n",
    "                    + 'chr1 chr2 chr3 chr4 chr5 chr6 chr7 chr8 chr9 chr10 chr11 chr12 chr13 chr14' \\\n",
    "                    + ' chr15 chr16 chr17 chr18 chr19 chr20 chr21 chr22 chrX  > ' \\\n",
    "                    + BAMFolder + 'AllFiles_Filtered_Merged_nodup_cleaned.bam'\n",
    "    subprocess.call(cleanCommand, shell=True)            \n",
    "    \n",
    "    sortMergeCmd = 'samtools sort -o ' + BAMFolder + 'AllFiles_Filtered_Merged_nodup_cleaned_sorted.bam' \\\n",
    "                    + ' ' + BAMFolder + 'AllFiles_Filtered_Merged_nodup_cleaned.bam'        \n",
    "    subprocess.call(sortMergeCmd, shell=True)\n",
    "    \n",
    "    indexMergeCmd = 'samtools index ' + BAMFolder + 'AllFiles_Filtered_Merged_nodup_cleaned_sorted.bam'\n",
    "    subprocess.call(indexMergeCmd, shell=True) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`getMergedBamSerial()`:\n",
    "> First divides the number of BAM files into number of chunks. Each chunk would consist of 100 BAM files (these can be adjusted). Files in each chunk is merged to create partly merged BAM files. These partly merged BAM files are then merged to create `AllFiles_Filtered_Merged_nodup.bam` file. This file is then sorted and indexed. This function should be used when the number of BAM files exceeds the threshold of number of files that can be simultaneously opened in a system (`ulimit -Hn`).\n",
    "\n",
    "Arguements:\n",
    "> `BAMFolder`: Location of the folder where all the BAM files for each of the single-cells are. <br>\n",
    "> `BAMFilePattern` : The end pattern of the `BAM` files to be merged.\n",
    "\n",
    "Value:\n",
    "> The merged `BAM` file `AllFiles_Filtered_Merged_nodup_sorted.bam`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getMergedBam(BAMFolder, BAMFilePattern):\n",
    "    \n",
    "    import pandas as pd\n",
    "    \n",
    "        \n",
    "    mergeFiles = ''\n",
    "    for root, folder, files in os.walk(BAMFolder):\n",
    "            files = [os.path.join(root, f) for f in files if (f.endswith(BAMFilePattern))]\n",
    "            for f in files:\n",
    "                mergeFiles = mergeFiles +  f + ' '\n",
    "    \n",
    "        \n",
    "    mergeCommand = 'samtools merge ' + BAMFolder + 'AllFiles_Filtered_Merged_nodup.bam' + ' ' + mergeFiles    \n",
    "    subprocess.call(mergeCommand, shell=True)   \n",
    "    \n",
    "    indexMergeCmd = 'samtools index ' + BAMFolder + 'AllFiles_Filtered_Merged_nodup.bam'\n",
    "    subprocess.call(indexMergeCmd, shell=True)\n",
    "    \n",
    "    cleanCommand = 'samtools view -b ' + BAMFolder + 'AllFiles_Filtered_Merged_nodup.bam' + ' ' \\\n",
    "                    + 'chr1 chr2 chr3 chr4 chr5 chr6 chr7 chr8 chr9 chr10 chr11 chr12 chr13 chr14' \\\n",
    "                    + ' chr15 chr16 chr17 chr18 chr19 chr20 chr21 chr22 chrX  > ' \\\n",
    "                    + BAMFolder + 'AllFiles_Filtered_Merged_nodup_cleaned.bam'\n",
    "    subprocess.call(cleanCommand, shell=True)            \n",
    "    \n",
    "    sortMergeCmd = 'samtools sort -o ' + BAMFolder + 'AllFiles_Filtered_Merged_nodup_cleaned_sorted.bam' \\\n",
    "                    + ' ' + BAMFolder + 'AllFiles_Filtered_Merged_nodup_cleaned.bam'        \n",
    "    subprocess.call(sortMergeCmd, shell=True)\n",
    "    \n",
    "    indexMergeCmd = 'samtools index ' + BAMFolder + 'AllFiles_Filtered_Merged_nodup_cleaned_sorted.bam'\n",
    "    subprocess.call(indexMergeCmd, shell=True) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`getMergedBam()`:\n",
    "> merges all the individual `BAM` files for each of the cells and creates `AllFiles_Filtered_Merged_nodup.bam` file. This file is then sorted and indexed.\n",
    "\n",
    "Arguements:\n",
    "> `BAMFolder`: Location of the folder where all the BAM files for each of the single-cells are. <br>\n",
    "> `BAMFilePattern` : The end pattern of the `BAM` files to be merged.\n",
    "\n",
    "Value:\n",
    "> The merged `BAM` file `AllFiles_Filtered_Merged_nodup_sorted.bam`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getAggregatedPeak(BAMFolder, BAMFilePattern):\n",
    "    \n",
    "    # getMergedBamSerial() should be called if number of BAM files exceeds the limit set by ulimit -Hn\n",
    "    getMergedBamSerial(BAMFolder, BAMFilePattern)\n",
    "    \n",
    "    # This function should be used if the numbe of BAM files are does not exceed the limit set by ulimit -Hn\n",
    "    #getMergedBam(BAMFolder, BAMFilePattern)\n",
    "    \n",
    "    MergedMacs2Cmd = '/home/mqbsxsm2/anaconda3/envs/python2/bin/macs2 callpeak -t ' + BAMFolder + 'AllFiles_Filtered_Merged_nodup_cleaned_sorted.bam' +' -n ' \\\n",
    "                    + BAMFolder + 'AllFiles_Filtered' +' -q 0.2 -g hs -f BAMPE --nomodel --nolambda \\\n",
    "                    -B --call-summits --keep-dup all; source deactivate'\n",
    "    subprocess.run(MergedMacs2Cmd, shell=True)\n",
    "    \n",
    "    narrowPeakFile = BAMFolder + 'AllFiles_Filtered_peaks.narrowPeak'\n",
    "    mergedPeakFile = BAMFolder + 'mergedPeaks.bed'\n",
    "    tmpPeakFile = BAMFolder + 'tmpPeak.txt'\n",
    "    \n",
    "    \n",
    "    with open(narrowPeakFile,'r') as f:\n",
    "        peaks=[x.strip().split('\\t') for x in f]\n",
    "    peaks.sort(key=lambda x: float(x[8]), reverse=True)\n",
    "    \n",
    "    \n",
    "    peaksToWrite = list()\n",
    "    for i in range(len(peaks)):\n",
    "        peaksToWrite.insert(i, peaks[i][0:4])\n",
    "        peaksToWrite[i].append(peaks[i][8])      \n",
    "    \n",
    "    \n",
    "    with open(mergedPeakFile, \"w\") as f:\n",
    "        writer = csv.writer(f,delimiter='\\t')        \n",
    "        writer.writerows(peaksToWrite)        \n",
    "        \n",
    "    sortCmd = 'sort -k1,1 -k2,2n ' + mergedPeakFile + ' > ' + tmpPeakFile  \n",
    "    subprocess.call(sortCmd, shell=True)\n",
    "    \n",
    "    bedToolsCmd ='bedtools merge -i ' + tmpPeakFile + ' -c 5 -o last > ' + mergedPeakFile\n",
    "    subprocess.call(bedToolsCmd, shell=True)\n",
    "    \n",
    "    sortCmd = 'sort -nrk4 ' + mergedPeakFile + ' > ' + tmpPeakFile  \n",
    "    subprocess.call(sortCmd, shell=True)    \n",
    "    \n",
    "    copyCmd = 'cat ' + tmpPeakFile + ' > ' + mergedPeakFile\n",
    "    subprocess.call(copyCmd, shell=True)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`getAggregatedPeak()`:\n",
    "> This function calls `MACS2` on the aggregated `BAM` file and generates the peaks. If the user is following the merging BAM option then this is the function they need to call. Then the `getMergedBam()` is called within this function\n",
    "\n",
    "Arguements:\n",
    "> `BAMFolder`: Location of the folder where all the BAM files for each of the single-cells are. <br>\n",
    "> `BAMFilePattern` : The end pattern of the `BAM` files to be merged.\n",
    "\n",
    "Value:\n",
    "> Peak files from MACS2 call."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------\n",
    "## R functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading R packages (if not available it will install it before loading)\n",
    "\n",
    "Pleae note that inorder to install `GenomicFeatures` the mysql library has to be alreay installed. If it is not available in your system the use the following command to install it\n",
    "\n",
    "`sudo apt-get install libdbd-mysql libmysqlclient-devthe`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%R\n",
    "if (!require(\"pacman\")) install.packages(\"pacman\")\n",
    "pacman::p_load(ggplot2, GenomicFeatures, ComplexHeatmap, Rtsne, prabclus, GGally, rtracklayer, corrplot, \n",
    "               cluster, EnsDb.Hsapiens.v75, ChIPpeakAnno, org.Hs.eg.db, rGREAT, marge, tidyverse, tibble)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### R functions for the Scasat\n",
    "\n",
    "Now I define all the R functions. These functions will be used for most of the downstream analysis. A short explanation of each function along with its input and output are also given below of each function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "peakAccessibility <- function(mergedPeakFile, peakFolder, peakFilePattern, outputFolder, outputPeakFileName){\n",
    "    if(length(list.files(path=peakFolder, pattern = paste0(peakFilePattern,'.bed'), full.names=TRUE))>0){\n",
    "        files = list.files(path=peakFolder, pattern = paste0(peakFilePattern,'.bed'), full.names=TRUE)\n",
    "        unlink(files)\n",
    "    }\n",
    "    formatingToBed(peakFolder, peakFilePattern, outputFolder)\n",
    "    peakFilePattern = paste0(peakFilePattern,'.bed')\n",
    "    query = import(mergedPeakFile)\n",
    "    queryDF <- data.frame(query)\n",
    "    #queryDF <- queryDF[order(queryDF$name, decreasing=TRUE),]    \n",
    "    \n",
    "    totalOverlap <- data.frame(seqnames = queryDF$seqnames, start = queryDF$start, end = queryDF$end)    \n",
    "    files <- list.files(path=peakFolder, pattern = peakFilePattern, full.names=TRUE)\n",
    "    cellName <- list.files(path=peakFolder, pattern = peakFilePattern)\n",
    "    for (i in 1:length(files)){\n",
    "        subject = import(files[i])\n",
    "        hits = findOverlaps(query, subject)\n",
    "        hitsDF <- data.frame(hits)\n",
    "        cellName[i] <- gsub(peakFilePattern, '', cellName[i])\n",
    "        totalOverlap[hitsDF$queryHits, cellName[i]] <- 1\n",
    "        totalOverlap[-hitsDF$queryHits, cellName[i]] <- 0\n",
    "    }\n",
    "    outputFile = paste0(outputFolder,outputPeakFileName)\n",
    "    write.table(totalOverlap, outputFile, row.names=FALSE)    \n",
    "}    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`peakAccesibility()`:\n",
    "> This function calculates the accessible peaks for each of the cells. It looks at each of the peaks listed in _Aggregated-Peak_ table and tries to find out whether there is any overlap between these peaks and the peaks of each cell. If there is then it puts a 1 agains that cells for that peak. Otherwise it puts 0. Calling these function for each of the cell completes the _Aggregated-Peak_ for which rows represnt all the peaks and the column represents cells.\n",
    "\n",
    "Arguements:\n",
    "> `mergedPeakFile`: This is the name and location of `mergedPeaks.bed` file that were generated while calling `mergePeaks()` python function.<br>\n",
    "> `peakFolder`: This is the location of the folder where all the peaks for each of the single-cells are located. <br>\n",
    "> `peakFilePattern` : The end pattern of the `peak` files of each single cells found in the `peakFolder`. Currently the only two accepted patterns are `_peaks.narrowPeak` and `_summits_shifted.bed` <br>\n",
    "> `outputFolder`: Folder where all the outputs will be saved\n",
    "> `outputPeakFileName`: Name of the file where the peak accessibility table, _Aggregated-Peak_ would be saved. This file will contain all the accessibility information for all the cells.\n",
    "\n",
    "\n",
    "Value:\n",
    "> Peak accessibile table saved according to the name set in `outputPeakFileName`. The table will have the results in 1 or 0 expression peak opening or closing respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "formatingToBed <- function(peakFolder, peakFilePattern, outputFolder){\n",
    "    files <- list.files(path=peakFolder, pattern = peakFilePattern, full.names=TRUE)\n",
    "    for (i in 1:length(files)){\n",
    "        narrowPeak <- read.csv(files[i], header=FALSE, sep='\\t')\n",
    "        write.table(narrowPeak[,1:4], paste0(files[i],'.bed'), \n",
    "                    row.names=FALSE, sep='\\t', col.names=FALSE, quote=FALSE)\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`formatingToBed()`:\n",
    "> This function formats the bed file suitable for processing by ScAsAT. It adds a `.bed` extension at the end.\n",
    "\n",
    "Arguements:\n",
    "> `mergedPeakFile`: This is the name and location of `mergedPeaks.bed` file that were generated while calling `mergePeaks()` python function.<br>\n",
    "> `peakFolder`: This is the location of the folder where all the peaks for each of the single-cells are located. <br>\n",
    "> `peakFilePattern` : The end pattern of the `peak` files of each single cells found in the `peakFolder`. Currently the only two accepted patterns are `_peaks.narrowPeak` and `_summits_shifted.bed` <br>\n",
    "> `outputFolder`: Folder where all the outputs will be saved\n",
    "\n",
    "Value:\n",
    "> Formats the bed files with adding a `.bed` extesion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "calcEntropy <- function(data){\n",
    "\tfreqs <- table(data)/sum(table(data))\n",
    "\treturn(-sum(freqs * log(freqs)))\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`calcEntropy()`:\n",
    "> Calcuates entropy is calculated as\n",
    "$$\\sum_{i=1}^c -p_i log_2 p_i$$\n",
    "\n",
    "\n",
    "`Usage`:\n",
    "> `calcEntropy(count_data)`\n",
    "\n",
    "Arguements:\n",
    "> `count_data`: The peak information converted into binary. \n",
    "\n",
    "Value:\n",
    "> Returns entropy of the count data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "getDiffAccessInformationGain <- function(cdBinary, groups=NULL){\n",
    "\n",
    "    if (length(levels(groups)) != 2) {\n",
    "        stop(paste(\"ERROR: wrong number of levels in the grouping factor (\", \n",
    "            paste(levels(groups), collapse = \" \"), \"), but must be two.\", \n",
    "            sep = \"\"))\n",
    "    }\n",
    "    if (is.null(groups)){\n",
    "        stop(\"ERROR: groups factor is not provided\")\n",
    "    }\n",
    "    \n",
    "    \n",
    "    SingleCell.Group1.CellNames <- names(groups[groups==levels(groups)[1]])\n",
    "    SingleCell.Group2.CellNames <- names(groups[groups==levels(groups)[2]])\n",
    "    \n",
    "    SingleCell.Group1.Binary <- cdBinary[,SingleCell.Group1.CellNames]\n",
    "    SingleCell.Group2.Binary <- cdBinary[,SingleCell.Group2.CellNames]\n",
    "    \n",
    "    CellType <- data.frame(CellType=gsub('_.*','',names(groups)))\n",
    "    \n",
    "         \n",
    "    SingleCell.Group1VsGroup2 <- cbind(SingleCell.Group1.Binary,SingleCell.Group2.Binary)\n",
    "    SingleCell.Group1VsGroup2 <- t(SingleCell.Group1VsGroup2)\n",
    "    #SingleCell.Group1VsGroup2 <- cbind(SingleCell.Group1VsGroup2,CellType)   \n",
    "\n",
    "    \n",
    "    dataDim = (dim(SingleCell.Group1VsGroup2)[2])\n",
    "    #dataDim = 5\n",
    "    \n",
    "    information.gain = vector(mode=\"numeric\", length=(ncol(SingleCell.Group1VsGroup2)-1))\n",
    "    SingleCell.Group1VsGroup2.res <- data.frame(Chr = cdBinary[1:dataDim,1],\n",
    "                                           Start = cdBinary[1:dataDim,2],\n",
    "                                           end = cdBinary[1:dataDim,3])        \n",
    "    \n",
    "    \n",
    "    for(i in 1:dataDim)\n",
    "    {\n",
    "        jointData = cbind(SingleCell.Group1VsGroup2[,i], CellType)\n",
    "        \n",
    "        gain <- calcEntropy(jointData[,2]) - \n",
    "                (sum(table(jointData)[c(1,3)])/length(jointData[,2]))*calcEntropy(jointData[jointData[,1]==0,]) - \n",
    "                (sum(table(jointData)[c(2,4)])/length(jointData[,2]))*calcEntropy(jointData[jointData[,1]==1,])\n",
    "        SingleCell.Group1VsGroup2.res$information.gain[i] <- gain        \n",
    "    }\n",
    "    \n",
    "        \n",
    "    SingleCell.Group1.rawAvg <- rowSums(SingleCell.Group1.Binary==1)/dim(SingleCell.Group1.Binary)[2]\n",
    "    SingleCell.Group2.rawAvg <- rowSums(SingleCell.Group2.Binary==1)/dim(SingleCell.Group2.Binary)[2]\n",
    "    \n",
    "    SingleCell.Group1VsGroup2.res$rawMeanGroup1 <- SingleCell.Group1.rawAvg\n",
    "    SingleCell.Group1VsGroup2.res$rawMeanGroup2 <- SingleCell.Group2.rawAvg\n",
    "    SingleCell.Group1VsGroup2.res$log2FoldChange <- log2(SingleCell.Group1VsGroup2.res$rawMeanGroup1/\n",
    "                                                       SingleCell.Group1VsGroup2.res$rawMeanGroup2)\n",
    "    \n",
    "\n",
    "    #write.csv(SingleCell.Group1VsGroup2[order(SingleCell.Group1VsGroup2$information.gain, decreasing=TRUE),], \n",
    "    #          paste0(levels(groups)[1],'_vs_',levels(groups)[2],'InformationGain.csv'), row.names=FALSE)\n",
    "    \n",
    "    SingleCell.Group1VsGroup2.res <- SingleCell.Group1VsGroup2.res[!is.nan(SingleCell.Group1VsGroup2.res$information.gain) &\n",
    "                                                                  !is.na(SingleCell.Group1VsGroup2.res$information.gain),]\n",
    "    \n",
    "    return(SingleCell.Group1VsGroup2.res)    \n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`getDiffAccessInformationGain()`:\n",
    "> Caculates differential accessibility between two groups of cells using entropy and information gain. For each peak the accessibility is encoded as a binary variable. For each peak we calculate the \"Information gain\" by having the peak divided into two groups of cells for which we are trying to find out the differential accessibilty. The Information gain is calculated as\n",
    "\n",
    "$$ \n",
    " Gain(P, P_{G_1}, P_{G_2}) = Entropy(P) - \\sum_{v \\epsilon\\{G_1,G_2 \\}} \\frac{|P_v|}{|P|}Entropy(P_v)\n",
    "$$\n",
    "\n",
    ">where Entropy is calculated as\n",
    "$$\\sum_{i=1}^c -p_i log_2 p_i$$\n",
    "\n",
    "> The peaks are then sorted based on the informatin gain and the user can choose the cutoff value for selecting the DA peaks. A suggested value for this would be values < 0.01\n",
    "\n",
    "\n",
    "`Usage`:\n",
    "> `getDiffAccessLogsitic(count_data, groups)`\n",
    "\n",
    "Arguements:\n",
    "> `cdBinary`: The peak information converted into binary. Ideally this would be file that set in the `outputPeakFileName`. `cdBinary` would be used to calculate the Jaccard distance which would then be used as d matrix in MDS calculation<br>\n",
    "\n",
    ">`groups` : groups into which each cell belong to.\n",
    "\n",
    "Value:\n",
    "> Returns a table with the peaks along with Information gain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "getDiffAccess <- function(cdBinary, groups=NULL){\n",
    "\n",
    "    if (length(levels(groups)) != 2) {\n",
    "        stop(paste(\"ERROR: wrong number of levels in the grouping factor (\", \n",
    "            paste(levels(groups), collapse = \" \"), \"), but must be two.\", \n",
    "            sep = \"\"))\n",
    "    }\n",
    "    if (is.null(groups)){\n",
    "        stop(\"ERROR: groups factor is not provided\")\n",
    "    }\n",
    "    \n",
    "    \n",
    "    SingleCell.Group1.CellNames <- names(groups[groups==levels(groups)[1]])\n",
    "    SingleCell.Group2.CellNames <- names(groups[groups==levels(groups)[2]])\n",
    "    \n",
    "    SingleCell.Group1.Binary <- cdBinary[,SingleCell.Group1.CellNames]\n",
    "    SingleCell.Group2.Binary <- cdBinary[,SingleCell.Group2.CellNames]\n",
    "\n",
    "    SingleCell.Group1.ZeroCount <- rowSums(SingleCell.Group1.Binary==0)\n",
    "    SingleCell.Group2.ZeroCount <- rowSums(SingleCell.Group2.Binary==0)\n",
    "    SingleCell.Group1.OneCount <- rowSums(SingleCell.Group1.Binary==1)\n",
    "    SingleCell.Group2.OneCount <- rowSums(SingleCell.Group2.Binary==1)\n",
    "    \n",
    "    SingleCell.Group1VsGroup2 <- data.frame(Chr = cdBinary[,1],\n",
    "                                           Start = cdBinary[,2],\n",
    "                                           end = cdBinary[,3])\n",
    "\n",
    "    SingleCell.Group1VsGroup2$group1OneCounts <- SingleCell.Group1.OneCount\n",
    "    SingleCell.Group1VsGroup2$group2OneCounts <- SingleCell.Group2.OneCount\n",
    "    SingleCell.Group1VsGroup2$log2Fold <- log2((SingleCell.Group1.OneCount+1)/dim(SingleCell.Group1.Binary)[2]) - \n",
    "                                            log2(((SingleCell.Group2.OneCount+1)/dim(SingleCell.Group2.Binary)[2]))\n",
    "    pvalue = vector(mode=\"numeric\", length=length(SingleCell.Group2.OneCount))\n",
    "    nGenes <- nrow(SingleCell.Group1.Binary)\n",
    "    \n",
    "    \n",
    "    for(i in 1:length(SingleCell.Group2.OneCount))\n",
    "    {\n",
    "        contingencyTable <- matrix(c(SingleCell.Group1.ZeroCount[i], SingleCell.Group2.ZeroCount[i],\n",
    "                                     SingleCell.Group1.OneCount[i], SingleCell.Group2.OneCount[i]), ncol=2)\n",
    "        SingleCell.Group1VsGroup2$pvalue[i] <- fisher.test(contingencyTable)$p.value        \n",
    "    }\n",
    "    \n",
    "    # Bonferroni correction\n",
    "    SingleCell.Group1VsGroup2$p_adjust <- p.adjust(SingleCell.Group1VsGroup2$pvalue, method=\"bonferroni\")\n",
    "\n",
    "    write.csv(SingleCell.Group1VsGroup2[order(SingleCell.Group1VsGroup2$p_adjust, decreasing=FALSE),], \n",
    "              paste0(levels(groups)[1],'_vs_',levels(groups)[2],'.csv'), row.names=FALSE)\n",
    "    \n",
    "    return(SingleCell.Group1VsGroup2)    \n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`getDiffAccess()`:\n",
    "> Caculates differential accessibility between two groups of cells. For each peak it creates a contingency table with number of 0's and 1's for the two groups. <br>\n",
    "\n",
    ">|               \t| Group-1 \t| Group-2 \t|\n",
    "|---------------\t|---------\t|---------\t|\n",
    "| Number of 0's \t|         \t|         \t|\n",
    "| Number of 1's \t|         \t|         \t| \n",
    "<br>\n",
    "It then runs Fisher exact test to calculate the p-values. These p-values are then corrected for multiple testing with Bonferroni correction. Finally the function returns a table with the peaks, their count of 1's in each group, the p-value and the q-value (corrected p-value).\n",
    "\n",
    "Arguements:\n",
    "> `cdBinary`: The peak information converted into binary. Ideally this would be file that set in the `outputPeakFileName`. `cdBinary` would be used to calculate the Jaccard distance which would then be used as d matrix in MDS calculation<br>\n",
    "\n",
    "Value:\n",
    "> Returns a table with the peaks, their count of 1's in each group, the p-value and the q-value (corrected p-value)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "getPeakVsPrecision <- function(peakA, peakB,start=100,stepSize=100){\n",
    "    \n",
    "    if (colnames(peakA)[1] != 'chr' | colnames(peakA)[2] != 'start' | colnames(peakA)[3] != 'end') {\n",
    "        stop(paste(\"ERROR: Column name should have 'Chr' 'Start' 'End'\"))\n",
    "    }\n",
    "\n",
    "    dimToRun <- min(dim(peakA)[1],dim(peakB)[1])\n",
    "    if(dim(peakB)[1]>dimToRun){\n",
    "        peakB <- peakB[1:dimToRun,]\n",
    "    }\n",
    "    GenomicRangeObjA <- makeGRangesFromDataFrame(peakA,\n",
    "                         keep.extra.columns=FALSE,\n",
    "                         ignore.strand=TRUE,\n",
    "                         seqinfo=NULL,\n",
    "                         seqnames.field=c(\"chr\"),\n",
    "                         start.field=\"start\",\n",
    "                         end.field=c(\"end\"),\n",
    "                         starts.in.df.are.0based=FALSE)\n",
    "    j=1\n",
    "    Recall <- vector(mode=\"numeric\", length=0)\n",
    "    Precision <- vector(mode=\"numeric\", length=0)\n",
    "    for(i in seq(100,(dimToRun-stepSize),stepSize)){\n",
    "           GenomicRangeObjB <- makeGRangesFromDataFrame(peakB[1:i,],\n",
    "                         keep.extra.columns=FALSE,\n",
    "                         ignore.strand=TRUE,\n",
    "                         seqinfo=NULL,\n",
    "                         seqnames.field=c(\"chr\"),\n",
    "                         start.field=\"start\",\n",
    "                         end.field=c(\"end\"),\n",
    "                         starts.in.df.are.0based=FALSE)\n",
    "\n",
    "           write.table(GenomicRangeObjA, 'tmpPeakA.bed', sep='\\t', row.names=FALSE, quote=FALSE, col.names=FALSE)\n",
    "\t   write.table(GenomicRangeObjB, 'tmpPeakB.bed', sep='\\t', row.names=FALSE, quote=FALSE, col.names=FALSE)\n",
    "           bedToolsCmd = 'bedtools intersect -a tmpPeakB.bed -b tmpPeakA.bed -u'\n",
    "           intersectRes = system(bedToolsCmd, intern=TRUE)\n",
    "           \n",
    "           FP = dim(peakB[1:i,])[1] - length(intersectRes)\n",
    "           TP = length(intersectRes)\n",
    "           \n",
    "           GenomicRangeObjB <- makeGRangesFromDataFrame(peakB[(i+1):dim(peakB)[1],],\n",
    "                         keep.extra.columns=FALSE,\n",
    "                         ignore.strand=TRUE,\n",
    "                         seqinfo=NULL,\n",
    "                         seqnames.field=c(\"Chr\"),\n",
    "                         start.field=\"Start\",\n",
    "                         end.field=c(\"End\"),\n",
    "                         starts.in.df.are.0based=FALSE)\n",
    "                         \n",
    "           write.table(GenomicRangeObjA, 'tmpPeakA.bed', sep='\\t', row.names=FALSE, quote=FALSE, col.names=FALSE)\n",
    "\t   write.table(GenomicRangeObjB, 'tmpPeakB.bed', sep='\\t', row.names=FALSE, quote=FALSE, col.names=FALSE)\n",
    "           bedToolsCmd = 'bedtools intersect -a tmpPeakB.bed -b tmpPeakA.bed -u'\n",
    "           intersectRes = system(bedToolsCmd, intern=TRUE)\n",
    "\n",
    "           TN = dim(peakB[(i+1):dim(peakB)[1],]) - length(intersectRes)\n",
    "           FN = length(intersectRes)\n",
    "           Recall[j] = 100*(TP/(TP + FN))\n",
    "           Precision[j] = 100*(TP / (TP + FP))\n",
    "           j = j +1\n",
    "           \n",
    "   \n",
    "    }\n",
    "    df<-data.frame(x=Recall,y=Precision)\n",
    "    p <- ggplot(df, aes_string(x=\"x\",y =\"y\"))+ scale_colour_hue(l=40)\n",
    "    \n",
    "    p <- p + ggtitle(\"Recall vs Precision\") + theme(plot.title = element_text(size = 16, face = \"bold\"))\n",
    "    p <- p + geom_point(size = 2)\n",
    "    p <- p + xlab(\"Recall%\") \n",
    "    p <- p + ylab(\"Precision%\")+\n",
    "\ttheme_light(base_size=15) +\n",
    "        theme(strip.background = element_blank(),\n",
    "              panel.border     = element_blank(),\n",
    "              plot.title = element_text(hjust = 0.5))\n",
    "    print(p)\n",
    "    \n",
    "    GenomicRangeObjB <- makeGRangesFromDataFrame(peakB,\n",
    "                         keep.extra.columns=FALSE,\n",
    "                         ignore.strand=TRUE,\n",
    "                         seqinfo=NULL,\n",
    "                         seqnames.field=c(\"Chr\"),\n",
    "                         start.field=\"Start\",\n",
    "                         end.field=c(\"End\"),\n",
    "                         starts.in.df.are.0based=FALSE)\n",
    "                         \n",
    "     write.table(GenomicRangeObjA, 'tmpPeakA.bed', sep='\\t', row.names=FALSE, quote=FALSE, col.names=FALSE)\n",
    "     write.table(GenomicRangeObjB, 'tmpPeakB.bed', sep='\\t', row.names=FALSE, quote=FALSE, col.names=FALSE)\n",
    "     bedToolsCmd = 'bedtools intersect -a tmpPeakB.bed -b tmpPeakA.bed -u'\n",
    "     intersectRes = system(bedToolsCmd, intern=TRUE)\n",
    "   \n",
    "   return(intersectRes) \n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`getPeakVsPrecision()`:\n",
    "> This function is used to see how many peaks from the Bulk data the single-cell have been able to capture. For that we calculate the Peak and the Precision of the single-cell data against its Bulk counterpart and then plot it. The way these two values are calculated are\n",
    "\n",
    "$$\n",
    "Recall = 100  \\frac{TP}{TP + FN}\n",
    "$$\n",
    "$$\n",
    "Precision = 100  \\frac{TP}{TP + FP}\n",
    "$$\n",
    "> where <br>\n",
    "> TP = True Positive <br>\n",
    "> TN = True Negative <br>\n",
    "> FP = False Positive <br>\n",
    "> FN = False Negative <br>\n",
    "\n",
    "`Usage`:\n",
    "> `getPeakVsPrecision(peakA, peakB,start,stepSize)`\n",
    "\n",
    "Arguements:\n",
    "> `peakA`: Peak of the BULK data <br>\n",
    "> `peakB`: Single-cell Peak <br>\n",
    "> `start`: Number of peaks to start with the calculation, default is `100`. <br>\n",
    "> `stepSize`: Size of the increase steps\n",
    "\n",
    "Value:\n",
    "> Plots Recall vs. Precision and returns a summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\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",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`getJaccardDist()`:\n",
    "> This function calculates the jaccard distance between each of the cells. Jaccard distance calculates the ratio between the differences of the sizes of the union and intersection against the size of the union. The equation for calculating this distance is <br><br>$$d_J(A,B)=\\frac{|A \\cup B| - |A \\cap B|}{|A \\cup B|}$$\n",
    "\n",
    "Arguements:\n",
    "> `cdBinary`: The peak information converted into binary. Ideally this would be file that set in the `outputPeakFileName`. \n",
    "\n",
    "\n",
    "Value:\n",
    "> Jaccard distance between each of the cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "plotMDS <- function(cdBinary, k, groups=NULL, cellName, ret.val=FALSE, text.label=FALSE, \n",
    "                    title=\"\"){\n",
    "\n",
    "    if(missing(ret.val)){\n",
    "        ret.val = FALSE\n",
    "    }\n",
    "    if(text.label==TRUE & missing(cellName)){\n",
    "        stop(\"ERROR: Please give cellName\")\n",
    "    }\n",
    "    \n",
    "    SingleCell.Binary.Jaccard <- getJaccardDist(cdBinary)\n",
    "    fit <- cmdscale(as.dist(SingleCell.Binary.Jaccard),eig=TRUE, k=k)\n",
    " \n",
    "    if(is.null(groups)){\n",
    "        df<-data.frame(x=fit$points[,1],y=fit$points[,2], Cell=colnames(SingleCell.Binary.Jaccard))\n",
    "        p <- ggplot(df, aes_string(x=\"x\",y =\"y\"))+ scale_colour_hue(l=40)\n",
    "    }\n",
    "    else{\n",
    "        df<-data.frame(x=fit$points[,1],y=fit$points[,2], Cell=colnames(SingleCell.Binary.Jaccard), Batch=groups)\n",
    "        p <- ggplot(df, aes_string(x=\"x\",y =\"y\", color=\"Batch\"))\n",
    "            #scale_colour_hue(l=40)\n",
    "            #scale_fill_manual(values = getPalette(15))\n",
    "    }\n",
    "\n",
    "    \n",
    "    p <- p + ggtitle(title) + theme(plot.title = element_text(size = 20, face = \"bold\"))\n",
    "    p <- p + geom_point(size = 3)\n",
    "    p <- p + xlab(\"Coordinate 1\") \n",
    "    p <- p + ylab(\"Coodinate 2\")+\n",
    "\ttheme_light(base_size=24) +\n",
    "        theme(strip.background = element_blank(),\n",
    "              panel.border     = element_blank(),\n",
    "              plot.title = element_text(hjust = 0.5))\n",
    "\n",
    "       \n",
    "    if(text.label==TRUE)\n",
    "        p <- p + geom_text(data=df,aes(label=cellName),alpha=0.5,size=4, vjust=1,hjust=0.5,angle=45)\n",
    "\n",
    "    #p <- p + theme(axis.title = element_text(size = 14), axis.text = element_text(size = 14),\n",
    "    #legend.text = element_text(size = 14), legend.title = element_text(size = 14))\n",
    "    \n",
    "    print(p)\n",
    "    \n",
    "    if(ret.val == TRUE)\n",
    "        return(fit)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`plotMDS()`:\n",
    "> This function calculates the Multidimensional Scaling (MDS) and plots the cells based on Jaccard distance to visualize the level of similarity between the cells.\n",
    "\n",
    "Arguements:\n",
    "> `cdBinary`: The peak information converted into binary. Ideally this would be file that set in the `outputPeakFileName`. `cdBinary` would be used to calculate the Jaccard distance which would then be used as d matrix in MDS calculation<br>\n",
    "`k`: The maximum dimension of the space which the data are to be represented in; must be in {1, 2, ..., n-1}. <br>\n",
    "`groups`: If the cells can be organized into different groups (for eg. different batches), then they are passed as groups making them factor variable.<br>\n",
    "`cellName`: If `text.label` is set to `TRUE` then you have to supply `cellName`.\n",
    "`ret.val`: Need to set this TRUE for the function to return the MDS values <br>\n",
    "`text.label`: Need to set this TRUE if label of each of the cells are to be printed in the plot.\n",
    "\n",
    "\n",
    "Value:\n",
    "> Calculates the MDS and plots the cells based on their MDS dimension. It returns the MDS values if `ret.val` is set to TRUE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "plotPCAJaccard <- function(cdBinary, groups=NULL, ret.val=FALSE , cellName, text.label=FALSE, title){\n",
    "    \n",
    "   \n",
    "    if(missing(ret.val)){\n",
    "        ret.val = FALSE\n",
    "    }\n",
    "    if(text.label==TRUE & missing(cellName)){\n",
    "        stop(\"ERROR: Please give cellName\")\n",
    "    }\n",
    "    \n",
    "    SingleCell.Binary.Jaccard <- getJaccardDist(cdBinary)\n",
    "    FinalPCAData <- t(SingleCell.Binary.Jaccard)\n",
    "    PCx=1\n",
    "    PCy=2\n",
    "    pcaPRComp <- prcomp(FinalPCAData)\n",
    "    percentVar <- pcaPRComp$sdev^2/sum(pcaPRComp$sdev^2)\n",
    " \n",
    "    if(is.null(groups)){\n",
    "        df<-data.frame(PCX=pcaPRComp$x[,PCx],PCY=pcaPRComp$x[,PCy])\n",
    "        p1<-ggplot(df, aes_string(x=\"PCX\",y =\"PCY\"))        \n",
    "    }\n",
    "    else{\n",
    "        df<-data.frame(PCX=pcaPRComp$x[,PCx],PCY=pcaPRComp$x[,PCy], groups=groups)\n",
    "        p1<-ggplot(df, aes_string(x=\"PCX\",y =\"PCY\", color=\"groups\"))             \n",
    "    }          \n",
    "    if(missing(title))\n",
    "        p1<-p1+ggtitle(\"PCA with Jacard Matrix\")\n",
    "    else\n",
    "        p1<-p1+ggtitle(title)\n",
    "        \n",
    "    p1<-p1+geom_point(size = 3)\n",
    "    p1<-p1+xlab(paste(\"PC\",PCx,\": \", round(percentVar[PCx] * 100), \"% variance\"))\n",
    "    p1<-p1+ylab(paste(\"PC\",PCy,\": \", round(percentVar[PCy] * 100), \"% variance\"))\n",
    "    if(text.label == TRUE){\n",
    "        p1<-p1+geom_text(data=df,aes(label=cellName),\n",
    "                       alpha=0.5,size=4, vjust=1,hjust=0.5,angle=45, color=\"black\")\n",
    "    }\n",
    "    \n",
    "    p1<-p1 +\n",
    "    theme_light(base_size=15)+     \n",
    "         theme(plot.title = element_text(hjust = 0.5),\n",
    "              panel.border     = element_blank())\n",
    "\n",
    "    if(length(levels(groups)) < 12)\n",
    "        p1 <- p1 + scale_colour_brewer(palette=\"Set1\")\n",
    "    \n",
    "    print(p1)\n",
    "        \n",
    "    if(ret.val == TRUE)\n",
    "        return(pcaPRComp)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`plotPCAJaccard()`:\n",
    "> This function calculates the Principle Component Analysis (PCA) and plots the cells based on Jaccard distance in a reduced dimension to visualize how the cells cluster together.\n",
    "\n",
    "Arguements:\n",
    "> `cdBinary`: The peak information converted into binary. Ideally this would be file that set in the `outputPeakFileName`. `cdBinary` would be used to calculate the Jaccard distance which would then be used as d matrix in MDS calculation<br>\n",
    "`groups`: If the cells can be organized into different groups (for eg. different batches), then they are passed as groups making them factor variable.<br>\n",
    "`ret.val`: Need to set this TRUE for the function to return the MDS values <br>\n",
    "`text.label`: Need to set this TRUE if label of each of the cells are to be printed in the plot.<br>\n",
    "`title`: Sets the title of the plot.\n",
    "\n",
    "\n",
    "Value:\n",
    "> Calculates PCA and plots the cells in reduced dimension. If`ret.val` is set to TRUE it returns the PCA components along with standard deviations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "plotMultiplePCAJaccard <- function(cdBinary, nPCAToDisplay, groups=NULL, ret.val=FALSE , text.label=FALSE, title=\"\"){\n",
    "    \n",
    "    if(missing(nPCAToDisplay)){\n",
    "    \tstop(\"ERROR: Number of PCA's \\\"nPCAToDisplay\\\" to display is missing\")\n",
    "    }\n",
    "    if(missing(ret.val)){\n",
    "        ret.val = FALSE\n",
    "    }\n",
    "    \n",
    "    SingleCell.Binary.Jaccard <- getJaccardDist(cdBinary)\n",
    "    FinalPCAData <- t(SingleCell.Binary.Jaccard)\n",
    "    PCx=1\n",
    "    PCy=2\n",
    "    pcaPRComp <- prcomp(FinalPCAData)\n",
    "\n",
    "    nmax = nPCAToDisplay\n",
    "    plotTitle = paste0('Plotting first ',nmax,' PCAs')\n",
    "    \n",
    "    txt1 <- paste(\"Percent_PC_Var_onfirst\",nmax,\"PCs\",sep=\"\")\n",
    "    pca_var = pcaPRComp$sdev ^ 2\n",
    "    pca_var_percent <- 100 * pca_var / sum(pca_var)\n",
    "    pca_var_percent_first10 <- NA * pca_var\n",
    "    pca_var_percent_first10[1:nmax] <- 100 * pca_var[1:nmax] / sum(pca_var[1:nmax])\n",
    "\n",
    "    pca_var_out <- data.frame(round(pca_var,3),round(pca_var_percent,1),\n",
    "                          round(pca_var_percent_first10,1))\n",
    "    rownames(pca_var_out) <- colnames(pcaPRComp$x)\n",
    "    colnames(pca_var_out) <- c(\"PC_Var\",\"PC_Var_percent\",txt1)\n",
    "\n",
    "\n",
    "    if(is.null(groups)){\n",
    "        df <- as.data.frame(pcaPRComp$x)\n",
    "        p <- ggpairs(df, columns=1:nPCAToDisplay, upper=list(continuous=\"points\"), \n",
    "             title=plotTitle,              \n",
    "             columnLabels = as.character(paste0(colnames(df[,1:nPCAToDisplay]), ' : ', \n",
    "                                                pca_var_out$PC_Var_percent[1:nPCAToDisplay], '% variance')))+\n",
    "    \t     theme_light(base_size=15)+     \n",
    "             theme(plot.title = element_text(hjust = 0.5))        \n",
    "    }\n",
    "    else{\n",
    "        df <- as.data.frame(pcaPRComp$x)\n",
    "        df$Cell=groups        \n",
    "        p <- ggpairs(df, columns=1:nPCAToDisplay, upper=list(continuous=\"points\"), \n",
    "             title=plotTitle, \n",
    "             mapping = aes_string(color=\"Cell\"),\n",
    "             legend = c(1,nPCAToDisplay),\n",
    "             columnLabels = as.character(paste0(colnames(df[,1:nPCAToDisplay]), ' : ', \n",
    "                                                pca_var_out$PC_Var_percent[1:nPCAToDisplay], '% variance')))+\n",
    "             theme_light(base_size=15)+     \n",
    "             theme(plot.title = element_text(hjust = 0.5))\n",
    "    }          \n",
    "    \n",
    "    print(p)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`plotMultiplePCAJaccard()`:\n",
    "> This function calculates the Principle Component Analysis (PCA) based on Jacard distance and plots the first `n` components of the PCA.\n",
    "\n",
    "Arguements:\n",
    "> `cdBinary`: The peak information converted into binary. Ideally this would be file that set in the `outputPeakFileName`. `cdBinary` would be used to calculate the Jaccard distance which would then be used as d matrix in MDS calculation<br>\n",
    "`nPCAToDisplay` : Number of PCAs to display. <br>\n",
    "`groups`: If the cells can be organized into different groups (for eg. different batches), then they are passed as groups making them factor variable.<br>\n",
    "`ret.val`: Need to set this TRUE for the function to return the MDS values <br>\n",
    "`text.label`: Need to set this TRUE if label of each of the cells are to be printed in the plot.<br>\n",
    "`title`: Sets the title of the plot.\n",
    "\n",
    "\n",
    "Value:\n",
    "> Calculates PCA and plots the cells in first `n` components. If`ret.val` is set to TRUE it returns the PCA components along with standard deviations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "plotVarExplained <- function(cdBinary, nPCAToDisplay, groups=NULL, ret.val=FALSE , text.label=FALSE, title=\"\"){\n",
    "\n",
    "    if(missing(nPCAToDisplay)){\n",
    "    \tstop(\"ERROR: Number of PCA's \\\"nPCAToDisplay\\\" to display is missing\")\n",
    "    }\n",
    "    if(missing(ret.val)){\n",
    "        ret.val = FALSE\n",
    "    }\n",
    "    \n",
    "    SingleCell.Binary.Jaccard <- getJaccardDist(cdBinary)\n",
    "    FinalPCAData <- t(SingleCell.Binary.Jaccard)\n",
    "    PCx=1\n",
    "    PCy=2\n",
    "    pcaPRComp <- prcomp(FinalPCAData)\n",
    "\n",
    "    nmax = nPCAToDisplay\n",
    "    plotTitle = paste0('Plotting first ',nmax,' PCAs')\n",
    "    \n",
    "    txt1 <- paste(\"Percent_PC_Var_onfirst\",nmax,\"PCs\",sep=\"\")\n",
    "    pca_var = pcaPRComp$sdev ^ 2\n",
    "    pca_var_percent <- 100 * pca_var / sum(pca_var)\n",
    "    pca_var_percent_first10 <- NA * pca_var\n",
    "    pca_var_percent_first10[1:nmax] <- 100 * pca_var[1:nmax] / sum(pca_var[1:nmax])\n",
    "\n",
    "    pca_var_out <- data.frame(round(pca_var,3),round(pca_var_percent,1),round(pca_var_percent_first10,1))\n",
    "    rownames(pca_var_out) <- colnames(pcaPRComp$x)\n",
    "    colnames(pca_var_out) <- c(\"PC_Var\",\"PC_Var_percent\",\"PC_Var_n_percent\")\n",
    "\n",
    "\n",
    "    p <- ggplot(pca_var_out[1:nmax,]) + scale_colour_hue(l=40) \n",
    "    p <- p + geom_line(aes(x=c(1:nmax), y = PC_Var_percent, color=\"PC_Var_percent\"), size=1.5)+\n",
    "         geom_point(aes(x=c(1:nmax), y = PC_Var_percent, color=\"PC_Var_percent\"), size = 2)+\n",
    "         geom_line(aes(x=c(1:nmax), y = PC_Var_n_percent, color=\"PC_Var_n_percent\"), size=1.5)+\n",
    "         geom_point(aes(x=c(1:nmax), y = PC_Var_n_percent, color=\"PC_Var_n_percent\"), size = 2)+\n",
    "         ggtitle(\"Total variance by the PCAs considered\")+\n",
    "         xlab(paste(\"PC1 to PC10\"))+\n",
    "         ylab(paste(\"% of Variance\"))+     \n",
    "         theme_light(base_size=15)+     \n",
    "         theme(plot.title = element_text(hjust = 0.5),\n",
    "              panel.border     = element_blank())+\n",
    "         scale_x_continuous(breaks=c(1:nmax))+\n",
    "         scale_colour_brewer(palette=\"Set1\")\n",
    "         \n",
    "   print(p)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`plotVarExplained()`:\n",
    "> This function calculates the percent of variance explained by the PCAs and plots it.\n",
    "\n",
    "Arguements:\n",
    "> `cdBinary`: The peak information converted into binary. Ideally this would be file that set in the `outputPeakFileName`. `cdBinary` would be used to calculate the Jaccard distance which would then be used as d matrix in MDS calculation<br>\n",
    "`nPCAToDisplay` : Number of PCAs to display. <br>\n",
    "`groups`: If the cells can be organized into different groups (for eg. different batches), then they are passed as groups making them factor variable.<br>\n",
    "`ret.val`: Need to set this TRUE for the function to return the MDS values <br>\n",
    "`text.label`: Need to set this TRUE if label of each of the cells are to be printed in the plot.<br>\n",
    "`title`: Sets the title of the plot.\n",
    "\n",
    "\n",
    "Value:\n",
    "> Calculates the percent of variance explained by the PCAs and the plots thsoe percentage. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "plot_tSNE <- function(cdBinary, nDimToUSE, groups=NULL, cellName, perplexity_division, ret.val=FALSE , text.label=FALSE, title=\"\"){\n",
    "\n",
    "    if(missing(nDimToUSE)){\n",
    "    \tstop(\"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": "markdown",
   "metadata": {},
   "source": [
    "`plot_tSNE()`:\n",
    "> Caculates the t-distributed stochastic neighbor embedding (t-SNE) which is a non-linear dimensionality reduction technique particualrly suitable for embedding high-dimensional data into a space of two or three dimensions. This function then plots the lower dimensional representation of the cells in a 2-D scatter plot.\n",
    "\n",
    "Arguements:\n",
    "> `cdBinary`: The peak information converted into binary. Ideally this would be file that set in the `outputPeakFileName`. `cdBinary` would be used to calculate the Jaccard distance which would then be used as d matrix in MDS calculation<br>\n",
    "`nPCAToDisplay` : Number of PCAs to be used to calcualte the t-SNE values. <br>\n",
    "`groups`: If the cells can be organized into different groups (for eg. different batches), then they are passed as groups making them factor variable.<br>\n",
    "`ret.val`: Need to set this TRUE for the function to return the MDS values <br>\n",
    "`text.label`: Need to set this TRUE if label of each of the cells are to be printed in the plot.<br>\n",
    "`title`: Sets the title of the plot.\n",
    "\n",
    "\n",
    "Value:\n",
    "> Calculates and plots the t-SNE representation of the cells. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "clean.count.peaks <- function(cd, min.cell.peaks.obs = 10, min.peaks.cell = 50){\n",
    "    cdFilt <- cd[,1:3]\n",
    "    cdCells <- cd[,4:dim(cd)[2]]\n",
    "    valid.cell.ix <- which(colSums(cdCells) >= min.peaks.cell)\n",
    "    cdFilt <- cbind(cdFilt,cdCells[,valid.cell.ix])\n",
    "    cdFilt <- cdFilt[rowSums(cdFilt[,4:dim(cdFilt)[2]]) >=min.cell.peaks.obs ,]\n",
    "    cdFiltTemp <- cdFilt[,1:3]\n",
    "    cdCells <- cdFilt[,4:dim(cdFilt)[2]]\n",
    "    valid.cell.ix <- which(colSums(cdCells) > 0)\n",
    "    cdFilt <- cbind(cdFiltTemp,cdCells[,valid.cell.ix])\n",
    "    return(cdFilt)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`clean.count.peaks()`:\n",
    "> This function filter peaks and cells based on peak and cell requirements.\n",
    "\n",
    "Usage:\n",
    "> `clean.count.peaks(cd, min.cell.peaks.obs = 10, min.peaks.cell = 50)`\n",
    "\n",
    "Arguements:\n",
    "> `cd`: A binay matrix with the peak accessiblity of all the single-cells. The row would reprsent each of the peaks unique name and the columns would be each cell. Each entry in the matrix is 0/1 representing either the peak is close/open for that cell. <br>\n",
    "> `min.cell.peaks.obs`: Minimum number of cells that a peak has to be observed in order for that peak to be a valid peak. <br>\n",
    "> `min.peaks.cell` : Minimum number of peaks that a cell needs to have in order for that cell to be called a valid cell. <br>\n",
    "\n",
    "Value:\n",
    "> A matrix with valid accessible peaks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "multiplot <- function(..., plotList=NULL, File, cols=1, layout=NULL) {\n",
    "\n",
    "  # Adopted from Cookbook for R\n",
    "  library(grid)\n",
    "\n",
    "  plots <- c(list(...), plotList)\n",
    "\n",
    "  numPlots = length(plots)\n",
    "\n",
    "  if (is.null(layout)) {\n",
    "    layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),\n",
    "                    ncol = cols, nrow = ceiling(numPlots/cols))\n",
    "  }\n",
    "\n",
    " if (numPlots==1) {\n",
    "    print(plots[[1]])\n",
    "\n",
    "  } else {\n",
    "    grid.newpage()\n",
    "    pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))\n",
    "\n",
    "    # Make each plot, in the correct location\n",
    "    for (i in 1:numPlots) {\n",
    "      # Get the i,j matrix positions of the regions that contain this subplot\n",
    "      matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))\n",
    "\n",
    "      print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,\n",
    "                                      layout.pos.col = matchidx$col))\n",
    "    }\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis\n",
    "Below we start the analysis with Buenrostro data. \n",
    "\n",
    "### Configuration Cell\n",
    "Following is the cell where we configure all the parameters for the tool."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This cell holds the parameters for the tools\n",
    "\n",
    "# Set this parameter to the folder where all the Peak Files are. This has to be the folder\n",
    "# where each of the individual cells peak files reside\n",
    "peakFolder = '/home/mqbsxsm2/my-single-cell-share/scratch-backup-dpsf/Buenrostro_pipeline_Final/Human_data_analysis/output/Filtered_Macs2_files'\n",
    "\n",
    "\n",
    "# Set this parameter to the folder where the peaks for the merge files are. This can be the same folder\n",
    "# of all the individual cells peak folder or can be a single peak file that is generated from merging BAM files\n",
    "peakFolderToMerge = '/home/mqbsxsm2/my-single-cell-share/scratch-backup-dpsf/Buenrostro_pipeline_Final/Human_data_analysis/output/Bam_Files_Filtered_on_Library_Size/'\n",
    "\n",
    "\n",
    "\n",
    "# Set up the pattern for the BAM file ending\n",
    "BAMFilePattern = '_nodup_sorted_cleaned.bam'\n",
    "\n",
    "# Set this parameter to the pattern that is common to all the peak file names that you wish to merge. \n",
    "# for eg, if you want to combine OE19_C01_peaks.narrowPeak and OE19_C02_peaks.narrowPeak then the\n",
    "# pattern would be '_peaks.narrowPeak'\n",
    "# Allowed patterns while calling with the pipeline provided here are\n",
    "# _summits_shifted.bed and '_peaks.narrowPeak'\n",
    "peakFilePattern = '_peaks.narrowPeak'\n",
    "\n",
    "# This would be automatically set to 'mergedPeaks.bed' file in peakFolder folder\n",
    "mergedPeakFile = '/home/mqbsxsm2/my-single-cell-share/scratch-backup-dpsf/Buenrostro_pipeline_Final/Human_data_analysis/output/Bam_Files_Filtered_on_Library_Size/mergedPeaks.bed'\n",
    "\n",
    "# Set this folder to where the PeakHits.csv File would be saved\n",
    "outputFolder = peakFolderToMerge\n",
    "\n",
    "# Set the outputPeakFileName name here\n",
    "outputPeakFileName = 'All_Human_Bam_NoTrimmo_NoShift.csv'\n",
    "\n",
    "# This will be automatically set to the 'PeakHits.csv' file in outputFolder folder\n",
    "peakFile = outputFolder + outputPeakFileName"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now call `getAggregatedPeak()` to merge all the BAM files and generated the aggregated peak from the merged BAM file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "getAggregatedPeak(peakFolderToMerge, BAMFilePattern)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To calculates the accessiblity of each of the peaks for all the cells we call the following function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%R -i mergedPeakFile,peakFolder,peakFilePattern,outputFolder,outputPeakFileName\n",
    "peakAccessibility(mergedPeakFile, peakFolder, peakFilePattern, outputFolder, outputPeakFileName)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading the peak files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R -i peakFile\n",
    "cd <- read.csv(peakFile, sep=' ', header=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 113343    743\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "dim(cd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtering accessible peaks\n",
    "\n",
    "Now we filter all the low quality cells and peaks.\n",
    "\n",
    "As there are 113343 cells we would consider a cell to be a valid cell if it has at least 114 peaks (at least 0.1% of the total peaks). Also a peak is valid peak to be valid if it is observed in at least 2% of the cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 113343    743\n",
       "[1] 32241   743\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "cdFilt <- clean.count.peaks(cd,min.peaks.cell = 114, min.cell.peaks.obs = 15)\n",
    "print(dim(cd))\n",
    "print(dim(cdFilt))\n",
    "cdFiltTop <- cdFilt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "write.csv(cdFiltTop, 'cdTop_Buenrostro_updated.csv', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "colnames(cdFiltTop) <- gsub('_.*','',colnames(cdFiltTop))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read annotation file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "           run_accession organism_ch1     CellInfo\n",
       "SRR1779587    SRR1779587 Homo sapiens H1ESC-well-1\n",
       "SRR1779588    SRR1779588 Homo sapiens H1ESC-well-2\n",
       "SRR1779589    SRR1779589 Homo sapiens H1ESC-well-3\n",
       "SRR1779590    SRR1779590 Homo sapiens H1ESC-well-4\n",
       "SRR1779591    SRR1779591 Homo sapiens H1ESC-well-5\n",
       "SRR1779592    SRR1779592 Homo sapiens H1ESC-well-6\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "annot <- read.csv('sraAnnotWithSpecies.csv')\n",
    "rownames(annot) <- annot$run_accession\n",
    "head(annot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 740   3\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "annotFilt <- annot[colnames(cdFiltTop[,4:dim(cdFiltTop)[2]]),]\n",
    "dim(annotFilt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "write.csv(annotFilt,'sraAnnotWithSpeciesFilteredList.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "Batches <- levels(as.factor(sub('-.*','',annotFilt$CellInfo)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "cellNames <- colnames(cdFiltTop[,4:dim(cdFiltTop)[2]])\n",
    "cellTypes <- as.factor(sub('-well.*','',annotFilt$CellInfo))\n",
    "#cellTypes <- as.factor(gsub('[0-9]','',colnames(cdFiltTumorTopSel[,4:dim(cdFiltTumorTopSel)[2]])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 750 -h 550\n",
    "#pdf('MDS_with_batch_effect.pdf')\n",
    "MDSClust <- plotMDS(cdFiltTop, 4, groups=cellTypes, ret.val=TRUE, text.label=FALSE, title=\"MDS without additional filtering\")\n",
    "#dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cells largeley seprate based on the cell types although no obvous cluster comes up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "cellTypesRepComb <- as.factor(sub('-.*','',annotFilt$CellInfo))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 750 -h 550\n",
    "#pdf('MDS_with_Buenrostro_data.pdf')\n",
    "MDSClust <- plotMDS(cdFiltTop, 4, groups=cellTypesRepComb, ret.val=TRUE, text.label=FALSE, title=\"MDS with Buenrostro Data\")\n",
    "#dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "set.seed(100)\n",
    "#pdf('tSNE-All-Batch.pdf')\n",
    "tsNE_out_2PCs <- plot_tSNE(cdFiltTop, 14, groups=cellTypesRepComb, perplexity=30, \n",
    "                              cellName=cellNames,ret.val=TRUE, text.label=F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "png \n",
       "  2 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "set.seed(100)\n",
    "pdf('tSNE-All-Batch_Rep_Combined.pdf')\n",
    "tsNE_out_2PCs <- plot_tSNE(cdFiltTop, 14, groups=cellTypesRepComb, perplexity=30, \n",
    "                              cellName=cellNames,ret.val=TRUE, text.label=F)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "set.seed(100)\n",
    "#pdf('tSNE-All-Batch.pdf')\n",
    "tsNE_out_2PCs <- plot_tSNE(cdFiltTop, 14, groups=cellTypes, perplexity=30, \n",
    "                              cellName=cellNames,ret.val=TRUE, text.label=F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "png \n",
       "  2 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "set.seed(100)\n",
    "pdf('tSNE-All-Batch_reps.pdf')\n",
    "tsNE_out_2PCs <- plot_tSNE(cdFiltTop, 14, groups=cellTypes, perplexity=30, \n",
    "                              cellName=cellNames,ret.val=TRUE, text.label=F)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering\n",
    "Now I would do clustering of the cells. I will use the `dynamicTreeCut` method for which I do not need to mention the number of clusters beforehand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "my.dist <- as.dist(getJaccardDist(cdFiltTop))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "#chosen.exprs <- as.matrix(cdTmpIndex)\n",
    "#my.dist <- dist(t(chosen.exprs), method=\"binary\")\n",
    "my.tree <- hclust(my.dist, method = \"ward.D2\")\n",
    "\n",
    "\n",
    "library(dynamicTreeCut)\n",
    "my.clusters <- unname(cutreeDynamic(my.tree, distM=as.matrix(my.dist), verbose=0))\n",
    "\n",
    "\n",
    "levels(as.factor(my.clusters))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "df<-data.frame(X=tsNE_out_2PCs$X,Y=tsNE_out_2PCs$Y,  \n",
    "               Cluster = as.factor(my.clusters), \n",
    "               cellType = as.factor(cellTypes),\n",
    "               cellTypesRepComb = as.factor(cellTypesRepComb))\n",
    " ggplot(df, aes_string(x=\"X\",y =\"Y\", color=\"Cluster\")) + geom_point(size=2) + theme_classic(base_size=24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "plot_tSNE_cluster <- function(cdBinary, nDimToUSE, groups=NULL, cellName, perplexity_division, ret.val=FALSE , text.label=FALSE, title=\"\"){\n",
    "\n",
    "    if(missing(nDimToUSE)){\n",
    "    \tstop(\"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$Y[,2], Cell=colnames(SingleCell.Binary.Jaccard), Cluster=groups, cellName=cellName)\n",
    "        else\n",
    "            df<-data.frame(X=rtsne_pca_out$Y[,1],Y=rtsne_pca_out$Y[,2], Cell=colnames(SingleCell.Binary.Jaccard), Cluster=groups)\n",
    "        p1 <- ggplot(df, aes_string(x=\"X\",y =\"Y\", color=\"Cluster\"))\n",
    "    }\n",
    "    \n",
    "\n",
    "    p1<-p1 + ggtitle(\"t-SNE plot\")\n",
    "    p1<-p1 + geom_point(size = 2) \n",
    "    p1<-p1 + xlab(paste(\"Dim-1\"))\n",
    "    p1<-p1 + ylab(\"Dim-2\")+\n",
    "        theme_light(base_size=24) +\n",
    "        theme(strip.background = element_blank(),\n",
    "              panel.border     = element_blank(),\n",
    "              plot.title = element_text(hjust = 0.5))\n",
    "    if(length(levels(groups)) < 12)\n",
    "        p1 <- p1 + scale_colour_brewer(palette=\"Set1\")\n",
    "    if(text.label==TRUE)\n",
    "        p1<-p1 + geom_text(data=df,aes(label=cellName),alpha=0.5,size=4, vjust=1,hjust=0.5,angle=45)\n",
    "    print(p1)\n",
    "    return(df)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "png \n",
       "  2 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "set.seed(100)\n",
    "pdf('tSNE_with_Cluster.pdf')\n",
    "tsNE_out_2PCs <- plot_tSNE_cluster(cdFiltTop, 14, groups=as.factor(my.clusters), perplexity=30, \n",
    "                              cellName=cellNames,ret.val=TRUE, text.label=F)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 950 -h 700\n",
    "df<-data.frame(X=tsNE_out_2PCs$X,Y=tsNE_out_2PCs$Y,  \n",
    "               Cluster = as.factor(my.clusters), \n",
    "               cellType = as.factor(cellTypes),\n",
    "               cellTypesRepComb = as.factor(cellTypesRepComb))\n",
    "p1 <- ggplot(df, aes_string(x=\"X\",y =\"Y\", color=\"Cluster\")) + geom_point(size=2) + theme_classic(base_size=20)\n",
    "p2 <- ggplot(df, aes_string(x=\"X\",y =\"Y\", color=\"cellType\")) + geom_point() + theme_classic()\n",
    "p3 <- ggplot(df, aes_string(x=\"X\",y =\"Y\", color=\"cellTypesRepComb\")) + geom_point() + theme_classic()\n",
    "cellTypesRepComb\n",
    "\n",
    "multiplot(p1,p2,p3,cols=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The clusters nicely overlays on the t-SNE visualization. Inspecting the t-SNE plots we can see that some of the cell-types are getting more than one cluster. As mentioned by [Weber & Robinson et. al. Cytometry A. 2016 Dec;89(12):1084-1096](https://www.ncbi.nlm.nih.gov/pubmed/27992111), it is better to start with slightly higher number of clusters and then using expert knowledge to manually merge clusters with similar phenotypes. We can merge those clusters by visually inspecting it or by characterizing the peaks associated with each cluster. In this notebook we took the first approach."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Characterizing the cell types\n",
    "\n",
    "Now we will try to characterize the different cell types using the differentially accessible peaks. For this we identify the differentially accessible peaks associated with each cell-type and then characterize the cells based on the motifs associated with the peaks as well as the functional analysis based on `Great`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Detecting motifs for cell-types (K562)\n",
    "We now identify the motifs associated with K562 cells. For this we first calculate Differential Accessibility to find out the peaks that are differentially accessible in K562 vs all cells."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first identify the K562 cells and the cells that are not K562"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "c1CellName <- rownames(annotFilt[sub('-.*','',annotFilt$CellInfo) == 'K562',])\n",
    "c2CellName <-  rownames(annotFilt[sub('-.*','',annotFilt$CellInfo) != 'K562',])\n",
    "c1CountData <- cdFiltTop[,as.character(c1CellName)]\n",
    "colnames(c1CountData) <- paste0('C1_', colnames(c1CountData))\n",
    "c2CountData <- cdFiltTop[,as.character(c2CellName)]\n",
    "colnames(c2CountData) <- paste0('C2_', colnames(c2CountData))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We prepare for the Differential Accessibility analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sg\n",
       " C1  C2 \n",
       "278 462 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "CellCluster.Annot.counts <- cbind(cdFiltTop[,1:3], c1CountData ,c2CountData)\n",
    "rownames(CellCluster.Annot.counts) <- paste0(cdFiltTop[,1],'_',cdFiltTop[,2],'_',cdFiltTop[,3])\n",
    "sg <- factor(gsub(\"(C1|C2).*\", \"\\\\1\", colnames(CellCluster.Annot.counts[,4:dim(CellCluster.Annot.counts)[2]])), \n",
    "             levels = c(\"C1\", \"C2\"))\n",
    "# the group factor should be named accordingly\n",
    "names(sg) <- colnames(CellCluster.Annot.counts[,4:dim(CellCluster.Annot.counts)[2]]) \n",
    "table(sg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time difference of 2.027216 mins\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "start.time <- Sys.time()\n",
    "diffRes <- getDiffAccessInformationGain(CellCluster.Annot.counts, groups=sg)\n",
    "end.time <- Sys.time()\n",
    "time.taken <- end.time - start.time\n",
    "time.taken"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We consider all the peaks that have an information gain of above 0.01 to be significant. So we extract those peaks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 3945    7\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "significantDEpeaks <- diffRes\n",
    "significantDEpeaks <- significantDEpeaks[order(significantDEpeaks$information.gain, decreasing=TRUE),]\n",
    "significantDEpeaks <- significantDEpeaks[significantDEpeaks$information.gain>0.01,]\n",
    "rownames(significantDEpeaks) <- paste0(significantDEpeaks[,1],'_',significantDEpeaks[,2],'_',significantDEpeaks[,3])\n",
    "print(dim(significantDEpeaks))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 3945 peaks that are significantly differentially accessible in K562 cells compared with all other cells indicating that these can be marker peaks for these cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                           Chr     Start       end information.gain\n",
       "chr22_22899001_22902550  chr22  22899001  22902550        0.1796699\n",
       "chr9_133999990_134002602  chr9 133999990 134002602        0.1440533\n",
       "chr22_22873690_22876012  chr22  22873690  22876012        0.1297814\n",
       "chr22_22107612_22111638  chr22  22107612  22111638        0.1238037\n",
       "chr22_21995184_21998492  chr22  21995184  21998492        0.1234496\n",
       "chr9_134147539_134155387  chr9 134147539 134155387        0.1223317\n",
       "                         rawMeanGroup1 rawMeanGroup2 log2FoldChange\n",
       "chr22_22899001_22902550      0.5179856   0.025974026       4.317770\n",
       "chr9_133999990_134002602     0.5611511   0.077922078       2.848285\n",
       "chr22_22873690_22876012      0.3417266   0.004329004       6.302664\n",
       "chr22_22107612_22111638      0.4316547   0.036796537       3.552236\n",
       "chr22_21995184_21998492      0.6618705   0.173160173       1.934442\n",
       "chr9_134147539_134155387     0.6510791   0.166666667       1.965867\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "head(significantDEpeaks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "sigK562 <- significantDEpeaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 3945    7\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "dim(sigK562)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Annotating peaks\n",
    "\n",
    "We annotate the these peaks. We use `marge`, an `R` package working as an API for `homer` to annotate the motifs associated with these peaks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "dat <- as_tibble(sigK562[, c(1:3)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find Motifs Across the Genome\n",
    "From the highly open regions we now find the enriched _de  novo_ and/or _known motifs_ via the `find_motifs_genome()`\n",
    "function. This function makes a connection to the HOMER utility `findMotifsGenome.pl`. A de novo and known motifs enrichment analysis is run on the test regions loaded prior, and writes results to the designated path directory. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "## Create a temporary directory to write results\n",
    "## This directory is erased once R session is closed\n",
    "#results_dir <-tempfile(pattern ='test-dir_')\n",
    "results_dir <- '/home/mqbsxsm2/Rna-seq_Data-Analysis/ScAsAT_notebooks_Updated/HOMER_results/K562'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "find_motifs_genome(dat,path = results_dir, genome ='hg38', overwrite = TRUE, motif_length=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "?find_motifs_genome"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Denovo and Known Motif Enrichment Results\n",
    "By default `HOMER` produces two distinct files for _de novo_ and _known motif_ enrichments within the results\n",
    "directory, in `homerMotifs.all.motif` and `knownResults.txt` file respectively. In `marger`, these results can be\n",
    "read in using the respective `read_*_results()` functions, where * is either denovo or known."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "known  <- read_known_results(path = '/home/mqbsxsm2/Rna-seq_Data-Analysis/ScAsAT_notebooks_Updated/HOMER_results/K562/knownResults.txt'\n",
    "                             , homer_dir = FALSE)\n",
    "#denovo <- read_denovo_results(path = results_dir, homer_dir = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[90m# A tibble: 363 x 14\u001b[39m\n",
       "   motif_name motif_family experiment  accession database consensus log_p_value\n",
       "   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m        \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m       \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m    \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m           \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
       "\u001b[90m 1\u001b[39m Sp1        Zf           Promoter    \u001b[31mNA\u001b[39m        Homer    GGCCCCGC…           2\n",
       "\u001b[90m 2\u001b[39m SF1        NR           H295R-Nr5a… GSE44220  Homer    CAAGGHCA…           2\n",
       "\u001b[90m 3\u001b[39m AP-2alpha  AP2          Hela-AP2al… GSE31477  Homer    ATGCCCTG…           1\n",
       "\u001b[90m 4\u001b[39m RAR:RXR    NR,DR5       ES-RAR-ChI… GSE56893  Homer    AGGTCAAG…           1\n",
       "\u001b[90m 5\u001b[39m RAR:RXR    NR,DR5       ES-RAR-ChI… GSE56893  Homer    AGGTCAAG…           1\n",
       "\u001b[90m 6\u001b[39m Erra       NR           HepG2-Erra… GSE31477  Homer    CAAAGGTC…           1\n",
       "\u001b[90m 7\u001b[39m GSC        Homeobox     FrogEmbryo… DRA000576 Homer    RGGATTAR            1\n",
       "\u001b[90m 8\u001b[39m STAT6      Stat         Macrophage… GSE38377  Homer    TTCCKNAG…           1\n",
       "\u001b[90m 9\u001b[39m AP-2gamma  AP2          MCF7-TFAP2… GSE21234  Homer    SCCTSAGG…           1\n",
       "\u001b[90m10\u001b[39m IRF4       IRF          GM12878-IR… GSE32465  Homer    ACTGAAAC…           1\n",
       "\u001b[90m# ... with 353 more rows, and 7 more variables: fdr \u001b[3m\u001b[90m<dbl>\u001b[90m\u001b[23m, tgt_num \u001b[3m\u001b[90m<dbl>\u001b[90m\u001b[23m,\n",
       "#   tgt_pct \u001b[3m\u001b[90m<dbl>\u001b[90m\u001b[23m, bgd_num \u001b[3m\u001b[90m<dbl>\u001b[90m\u001b[23m, bgd_pct \u001b[3m\u001b[90m<dbl>\u001b[90m\u001b[23m, motif_pwm \u001b[3m\u001b[90m<list>\u001b[90m\u001b[23m,\n",
       "#   log_odds_detection \u001b[3m\u001b[90m<dbl>\u001b[90m\u001b[23m\u001b[39m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "known"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "write.csv(data.frame(motifName=known[[1]], log_p_value=known[[7]], FDR=known[[8]]),'K562_motifs.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Disease ontology analysis\n",
    "\n",
    "We now do a disease ontology analysis with the K562 significant peaks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "sigK562Sel <- sigK562[sigK562$log2FoldChange>2,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "bedg1 = sigK562Sel\n",
    "jobg1 = submitGreatJob(bedg1, version = \"3.0\", species='hg19')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "tbDisG1 = getEnrichmentTables(jobg1, ontolog=\"Disease Ontology\")\n",
    "dftbDisG1 = data.frame(Name = tbDisG1[[1]][,2], Binom_p=-log10(tbDisG1[[1]][,8]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                         Name  Binom_p\n",
       "1           lymphoid leukemia 21.05432\n",
       "2            myeloid leukemia 18.18818\n",
       "3     alveolar echinococcosis 18.02069\n",
       "4    chronic myeloid leukemia 17.93534\n",
       "5              echinococcosis 16.62967\n",
       "6  gallbladder adenocarcinoma 15.57111\n",
       "7                    leukemia 13.48804\n",
       "8      lymphoblastic leukemia 13.04917\n",
       "9         hemorrhagic disease 12.62245\n",
       "10  blood coagulation disease 12.24694\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "dftbDisG1[1:10,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 600 -h 350\n",
    "library(reshape)\n",
    "df.long<-dftbDisG1[1:10,]\n",
    "p <- ggplot(df.long,aes(Name,Binom_p,fill=Binom_p)) + ggtitle('K562 disease ontology') +\n",
    "    geom_bar(stat=\"identity\",position=\"dodge\",width = 1) + coord_flip()+\n",
    "        theme_light(base_size=12) +\n",
    "        theme(text=element_text(size=14,  face=\"bold\"),\n",
    "            strip.background = element_blank(),\n",
    "              panel.border     = element_blank(),\n",
    "              plot.title = element_text(hjust = 0.5))+ xlab(\"Disease Ontology\") + \n",
    "             ylab(\"-log10(Binom_p-value)\")\n",
    "             #scale_fill_manual(\"red\")\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We know that __K562__ cells are the human myelogenous leukemia cell lines. This is the exact disease ontology picked up from the first 2 disease ontology in the GREAT analysis confirming our pipeline's efficieny in characterizing the cells correcting. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "png \n",
       "  2 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "pdf('K562_disease_ontologly.pdf', width=10, height=5)\n",
    "\n",
    "df.long<-dftbDisG1[1:10,]\n",
    "p <- ggplot(df.long,aes(Name,Binom_p,fill=Binom_p)) + ggtitle('K562 disease ontology') +\n",
    "    geom_bar(stat=\"identity\",position=\"dodge\",width = 1) + coord_flip()+\n",
    "        theme_light(base_size=12) +\n",
    "        theme(text=element_text(size=14,  face=\"bold\"),\n",
    "            strip.background = element_blank(),\n",
    "              panel.border     = element_blank(),\n",
    "              plot.title = element_text(hjust = 0.5))+ xlab(\"Disease Ontology\") + \n",
    "             ylab(\"-log10(Binom_p-value)\")\n",
    "             #scale_fill_manual(\"red\")\n",
    "print(p)\n",
    "\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Detecting motifs for cell-types (H1ESC)\n",
    "We now identify the motifs associated with H1ESC cells."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agia, we first identify the H1ESC cells and the cells that are not H1ESC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "c1CellName <- rownames(annotFilt[sub('-.*','',annotFilt$CellInfo) == 'H1ESC',])\n",
    "c2CellName <-  rownames(annotFilt[sub('-.*','',annotFilt$CellInfo) != 'H1ESC',])\n",
    "c1CountData <- cdFiltTop[,as.character(c1CellName)]\n",
    "colnames(c1CountData) <- paste0('C1_', colnames(c1CountData))\n",
    "c2CountData <- cdFiltTop[,as.character(c2CellName)]\n",
    "colnames(c2CountData) <- paste0('C2_', colnames(c2CountData))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We prepare for the Differential Accessibility analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sg\n",
       " C1  C2 \n",
       " 60 680 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "CellCluster.Annot.counts <- cbind(cdFiltTop[,1:3], c1CountData ,c2CountData)\n",
    "rownames(CellCluster.Annot.counts) <- paste0(cdFiltTop[,1],'_',cdFiltTop[,2],'_',cdFiltTop[,3])\n",
    "sg <- factor(gsub(\"(C1|C2).*\", \"\\\\1\", colnames(CellCluster.Annot.counts[,4:dim(CellCluster.Annot.counts)[2]])), \n",
    "             levels = c(\"C1\", \"C2\"))\n",
    "# the group factor should be named accordingly\n",
    "names(sg) <- colnames(CellCluster.Annot.counts[,4:dim(CellCluster.Annot.counts)[2]]) \n",
    "table(sg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time difference of 2.069025 mins\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "start.time <- Sys.time()\n",
    "diffRes <- getDiffAccessInformationGain(CellCluster.Annot.counts, groups=sg)\n",
    "end.time <- Sys.time()\n",
    "time.taken <- end.time - start.time\n",
    "time.taken"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We consider all the peaks that have an information gain of above 0.01 to be significant. So we extract those peaks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 617   7\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "significantDEpeaks <- diffRes\n",
    "significantDEpeaks <- significantDEpeaks[order(significantDEpeaks$information.gain, decreasing=TRUE),]\n",
    "significantDEpeaks <- significantDEpeaks[significantDEpeaks$information.gain>0.01,]\n",
    "rownames(significantDEpeaks) <- paste0(significantDEpeaks[,1],'_',significantDEpeaks[,2],'_',significantDEpeaks[,3])\n",
    "print(dim(significantDEpeaks))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 617 peaks that are significantly differentially accessible in H1ESC cells compared with all other cells indicating that these can be marker peaks for these cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                          Chr     Start       end information.gain\n",
       "chr8_105235368_105236506 chr8 105235368 105236506       0.04884215\n",
       "chr8_17270582_17271344   chr8  17270582  17271344       0.04852606\n",
       "chr3_67039181_67039625   chr3  67039181  67039625       0.04487596\n",
       "chr5_140345779_140347966 chr5 140345779 140347966       0.04240195\n",
       "chr1_95391852_95393214   chr1  95391852  95393214       0.04156323\n",
       "chr2_111489796_111490785 chr2 111489796 111490785       0.04062765\n",
       "                         rawMeanGroup1 rawMeanGroup2 log2FoldChange\n",
       "chr8_105235368_105236506     0.3166667   0.010294118       4.943073\n",
       "chr8_17270582_17271344       0.2500000   0.001470588       7.409391\n",
       "chr3_67039181_67039625       0.2333333   0.001470588       7.309855\n",
       "chr5_140345779_140347966     0.3000000   0.013235294       4.502500\n",
       "chr1_95391852_95393214       0.3500000   0.026470588       3.724893\n",
       "chr2_111489796_111490785     0.2500000   0.005882353       5.409391\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "head(significantDEpeaks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "sigH1ESC <- significantDEpeaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 617   7\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "dim(sigH1ESC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Annotating peaks\n",
    "\n",
    "We annotate the these peaks. We use `marge`, an `R` package working as an API for `homer` to annotate the motifs associated with these peaks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "dat <- as_tibble(sigH1ESC[, c(1:3)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find Motifs Across the Genome\n",
    "From the highly open regions we now find the enriched _de  novo_ and/or _known motifs_ via the `find_motifs_genome()`\n",
    "function. This function makes a connection to the HOMER utility `findMotifsGenome.pl`. A de novo and known motifs enrichment analysis is run on the test regions loaded prior, and writes results to the designated path directory. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "## Create a temporary directory to write results\n",
    "## This directory is erased once R session is closed\n",
    "#results_dir <-tempfile(pattern ='test-dir_')\n",
    "results_dir <- '/home/mqbsxsm2/Rna-seq_Data-Analysis/ScAsAT_notebooks_Updated/HOMER_results/H1ESC'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "find_motifs_genome(dat,path = results_dir, genome ='hg38', overwrite = TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Denovo and Known Motif Enrichment Results\n",
    "By default `HOMER` produces two distinct files for _de novo_ and _known motif_ enrichments within the results\n",
    "directory, in `homerMotifs.all.motif` and `knownResults.txt` file respectively. In `marger`, these results can be\n",
    "read in using the respective `read_*_results()` functions, where * is either denovo or known."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "known  <- read_known_results(path = '/home/mqbsxsm2/Rna-seq_Data-Analysis/ScAsAT_notebooks_Updated/HOMER_results/H1ESC/knownResults.txt'\n",
    "                             , homer_dir = FALSE)\n",
    "#denovo <- read_denovo_results(path = results_dir, homer_dir = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[90m# A tibble: 363 x 14\u001b[39m\n",
       "   motif_name motif_family experiment  accession database consensus log_p_value\n",
       "   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m        \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m       \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m    \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m           \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
       "\u001b[90m 1\u001b[39m Tcf21      bHLH         ArterySmoo… GSE61369  Homer    NAACAGCT…           2\n",
       "\u001b[90m 2\u001b[39m Myf5       bHLH         GM-Myf5-Ch… GSE24852  Homer    BAACAGCT…           2\n",
       "\u001b[90m 3\u001b[39m Tcf12      bHLH         GM12878-Tc… GSE32465  Homer    VCAGCTGY…           1\n",
       "\u001b[90m 4\u001b[39m Six2       Homeobox     NephronPro… GSE39837  Homer    GWAAYHTG…           1\n",
       "\u001b[90m 5\u001b[39m MyoD       bHLH         Myotube-My… GSE21614  Homer    RRCAGCTG…           1\n",
       "\u001b[90m 6\u001b[39m ELF5       ETS          T47D-ELF5-… GSE30407  Homer    ACVAGGAA…           1\n",
       "\u001b[90m 7\u001b[39m E2A        bHLH         proBcell-E… GSE21978  Homer    DNRCAGCT…           1\n",
       "\u001b[90m 8\u001b[39m Tbx5       T-box        HL1-Tbx5.b… GSE21529  Homer    AGGTGTCA            1\n",
       "\u001b[90m 9\u001b[39m PPARE      NR,DR1       3T3L1-Ppar… GSE13511  Homer    TGACCTTT…           1\n",
       "\u001b[90m10\u001b[39m RFX        HTH          K562-RFX3-… SRA012198 Homer    CGGTTGCC…           1\n",
       "\u001b[90m# ... with 353 more rows, and 7 more variables: fdr \u001b[3m\u001b[90m<dbl>\u001b[90m\u001b[23m, tgt_num \u001b[3m\u001b[90m<dbl>\u001b[90m\u001b[23m,\n",
       "#   tgt_pct \u001b[3m\u001b[90m<dbl>\u001b[90m\u001b[23m, bgd_num \u001b[3m\u001b[90m<dbl>\u001b[90m\u001b[23m, bgd_pct \u001b[3m\u001b[90m<dbl>\u001b[90m\u001b[23m, motif_pwm \u001b[3m\u001b[90m<list>\u001b[90m\u001b[23m,\n",
       "#   log_odds_detection \u001b[3m\u001b[90m<dbl>\u001b[90m\u001b[23m\u001b[39m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "known"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "write.csv(data.frame(motifName=known[[1]], log_p_value=known[[7]], FDR=known[[8]]),'H1ESC_motifs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Detecting motifs for cell-types (BJ)\n",
    "Now, we identify the motifs associated with BJ cells."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like before, we first identify the BJ cells and the cells that are not BJ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "c1CellName <- rownames(annotFilt[sub('-.*','',annotFilt$CellInfo) == 'BJ',])\n",
    "c2CellName <-  rownames(annotFilt[sub('-.*','',annotFilt$CellInfo) != 'BJ',])\n",
    "c1CountData <- cdFiltTop[,as.character(c1CellName)]\n",
    "colnames(c1CountData) <- paste0('C1_', colnames(c1CountData))\n",
    "c2CountData <- cdFiltTop[,as.character(c2CellName)]\n",
    "colnames(c2CountData) <- paste0('C2_', colnames(c2CountData))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We prepare for the Differential Accessibility analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sg\n",
       " C1  C2 \n",
       " 56 684 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "CellCluster.Annot.counts <- cbind(cdFiltTop[,1:3], c1CountData ,c2CountData)\n",
    "rownames(CellCluster.Annot.counts) <- paste0(cdFiltTop[,1],'_',cdFiltTop[,2],'_',cdFiltTop[,3])\n",
    "sg <- factor(gsub(\"(C1|C2).*\", \"\\\\1\", colnames(CellCluster.Annot.counts[,4:dim(CellCluster.Annot.counts)[2]])), \n",
    "             levels = c(\"C1\", \"C2\"))\n",
    "# the group factor should be named accordingly\n",
    "names(sg) <- colnames(CellCluster.Annot.counts[,4:dim(CellCluster.Annot.counts)[2]]) \n",
    "table(sg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time difference of 2.085277 mins\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "start.time <- Sys.time()\n",
    "diffRes <- getDiffAccessInformationGain(CellCluster.Annot.counts, groups=sg)\n",
    "end.time <- Sys.time()\n",
    "time.taken <- end.time - start.time\n",
    "time.taken"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We consider all the peaks that have an information gain of above 0.01 to be significant. So we extract those peaks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 1172    7\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "significantDEpeaks <- diffRes\n",
    "significantDEpeaks <- significantDEpeaks[order(significantDEpeaks$information.gain, decreasing=TRUE),]\n",
    "significantDEpeaks <- significantDEpeaks[significantDEpeaks$information.gain>0.01,]\n",
    "rownames(significantDEpeaks) <- paste0(significantDEpeaks[,1],'_',significantDEpeaks[,2],'_',significantDEpeaks[,3])\n",
    "print(dim(significantDEpeaks))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 1172 peaks that are significantly differentially accessible in BJ cells compared with all other cells indicating that these can be marker peaks for these cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                           Chr     Start       end information.gain\n",
       "chr7_73309035_73309793    chr7  73309035  73309793       0.07760495\n",
       "chr6_117761914_117762805  chr6 117761914 117762805       0.06441182\n",
       "chr20_49982694_49983371  chr20  49982694  49983371       0.06213293\n",
       "chr1_99922886_99923706    chr1  99922886  99923706       0.05826243\n",
       "chr1_44793385_44793943    chr1  44793385  44793943       0.05826243\n",
       "chr18_26734605_26735364  chr18  26734605  26735364       0.05826243\n",
       "                         rawMeanGroup1 rawMeanGroup2 log2FoldChange\n",
       "chr7_73309035_73309793       0.3928571   0.001461988       8.069929\n",
       "chr6_117761914_117762805     0.3750000   0.005847953       6.002815\n",
       "chr20_49982694_49983371      0.3750000   0.007309942       5.680887\n",
       "chr1_99922886_99923706       0.3214286   0.002923977       6.780423\n",
       "chr1_44793385_44793943       0.3214286   0.002923977       6.780423\n",
       "chr18_26734605_26735364      0.3214286   0.002923977       6.780423\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "head(significantDEpeaks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "sigBJ <- significantDEpeaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 1172    7\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "dim(sigBJ)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Annotating peaks\n",
    "\n",
    "We annotate the these peaks. We use `marge`, an `R` package working as an API for `homer` to annotate the motifs associated with these peaks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "dat <- as_tibble(sigBJ[, c(1:3)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find Motifs Across the Genome\n",
    "From the highly open regions we now find the enriched _de  novo_ and/or _known motifs_ via the `find_motifs_genome()`\n",
    "function. This function makes a connection to the HOMER utility `findMotifsGenome.pl`. A de novo and known motifs enrichment analysis is run on the test regions loaded prior, and writes results to the designated path directory. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "## Create a temporary directory to write results\n",
    "## This directory is erased once R session is closed\n",
    "#results_dir <-tempfile(pattern ='test-dir_')\n",
    "results_dir <- '/home/mqbsxsm2/Rna-seq_Data-Analysis/ScAsAT_notebooks_Updated/HOMER_results/BJ'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "find_motifs_genome(dat,path = results_dir, genome ='hg38', overwrite = TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Denovo and Known Motif Enrichment Results\n",
    "By default `HOMER` produces two distinct files for _de novo_ and _known motif_ enrichments within the results\n",
    "directory, in `homerMotifs.all.motif` and `knownResults.txt` file respectively. In `marger`, these results can be\n",
    "read in using the respective `read_*_results()` functions, where * is either denovo or known."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "known  <- read_known_results(path = '/home/mqbsxsm2/Rna-seq_Data-Analysis/ScAsAT_notebooks_Updated/HOMER_results/BJ/knownResults.txt'\n",
    "                             , homer_dir = FALSE)\n",
    "#denovo <- read_denovo_results(path = results_dir, homer_dir = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "known"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "write.csv(known[[1]], 'BJ_motifs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Biological process analysis\n",
    "\n",
    "We now do a biological proces analysis associated with the BJ significant peaks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "sigBJSel <- sigBJ[sigBJ$log2FoldChange>2,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "bedg1 = sigBJSel\n",
    "jobg1 = submitGreatJob(bedg1, version = \"3.0\", species='hg19')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "tbDisG1 = getEnrichmentTables(jobg1, ontolog=\"GO Biological Process\")\n",
    "dftbDisG1 = data.frame(Name = tbDisG1[[1]][,2], Binom_p=-log10(tbDisG1[[1]][,8]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                                        Name  Binom_p\n",
       "1           regulation of cell communication 17.26924\n",
       "2                    regulation of signaling 16.97066\n",
       "3          regulation of signal transduction 16.94500\n",
       "4                         response to stress 16.78951\n",
       "5  negative regulation of biological process 16.73278\n",
       "6        regulation of programmed cell death 16.30698\n",
       "7            regulation of apoptotic process 16.26233\n",
       "8         regulation of response to stimulus 15.75750\n",
       "9    negative regulation of cellular process 15.30380\n",
       "10                  regulation of cell death 15.28546\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "dftbDisG1[1:10,]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the BJ cells are from skin or foreskin tissue it is heaviliy involved with the regulation of cell-singalling process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Detecting motifs for cell-types (TF-1)\n",
    "Now, we identify the motifs associated with TF-1 cells."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like before, we first identify the TF-1 cells and the cells that are not TF-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "c1CellName <- rownames(annotFilt[sub('-.*','',annotFilt$CellInfo) == 'TF1',])\n",
    "c2CellName <-  rownames(annotFilt[sub('-.*','',annotFilt$CellInfo) != 'TF1',])\n",
    "c1CountData <- cdFiltTop[,as.character(c1CellName)]\n",
    "colnames(c1CountData) <- paste0('C1_', colnames(c1CountData))\n",
    "c2CountData <- cdFiltTop[,as.character(c2CellName)]\n",
    "colnames(c2CountData) <- paste0('C2_', colnames(c2CountData))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We prepare for the Differential Accessibility analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sg\n",
       " C1  C2 \n",
       " 64 676 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "CellCluster.Annot.counts <- cbind(cdFiltTop[,1:3], c1CountData ,c2CountData)\n",
    "rownames(CellCluster.Annot.counts) <- paste0(cdFiltTop[,1],'_',cdFiltTop[,2],'_',cdFiltTop[,3])\n",
    "sg <- factor(gsub(\"(C1|C2).*\", \"\\\\1\", colnames(CellCluster.Annot.counts[,4:dim(CellCluster.Annot.counts)[2]])), \n",
    "             levels = c(\"C1\", \"C2\"))\n",
    "# the group factor should be named accordingly\n",
    "names(sg) <- colnames(CellCluster.Annot.counts[,4:dim(CellCluster.Annot.counts)[2]]) \n",
    "table(sg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time difference of 2.164782 mins\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "start.time <- Sys.time()\n",
    "diffRes <- getDiffAccessInformationGain(CellCluster.Annot.counts, groups=sg)\n",
    "end.time <- Sys.time()\n",
    "time.taken <- end.time - start.time\n",
    "time.taken"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We consider all the peaks that have an information gain of above 0.01 to be significant. So we extract those peaks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 676   7\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "significantDEpeaks <- diffRes\n",
    "significantDEpeaks <- significantDEpeaks[order(significantDEpeaks$information.gain, decreasing=TRUE),]\n",
    "significantDEpeaks <- significantDEpeaks[significantDEpeaks$information.gain>0.01,]\n",
    "rownames(significantDEpeaks) <- paste0(significantDEpeaks[,1],'_',significantDEpeaks[,2],'_',significantDEpeaks[,3])\n",
    "print(dim(significantDEpeaks))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 676 peaks that are significantly differentially accessible in TF-1 cells compared with all other cells indicating that these can be marker peaks for these cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                          Chr    Start      end information.gain rawMeanGroup1\n",
       "chr19_38754356_38756643 chr19 38754356 38756643       0.10894056      0.671875\n",
       "chr19_39182621_39184937 chr19 39182621 39184937       0.08839462      0.562500\n",
       "chr19_39164751_39168157 chr19 39164751 39168157       0.07570920      0.609375\n",
       "chr19_38809674_38812785 chr19 38809674 38812785       0.07362591      0.468750\n",
       "chr15_80852914_80853833 chr15 80852914 80853833       0.06927068      0.406250\n",
       "chr19_38510660_38511315 chr19 38510660 38511315       0.06891305      0.328125\n",
       "                        rawMeanGroup2 log2FoldChange\n",
       "chr19_38754356_38756643    0.03550296       4.242182\n",
       "chr19_39182621_39184937    0.02810651       4.322877\n",
       "chr19_39164751_39168157    0.06065089       3.328730\n",
       "chr19_38809674_38812785    0.02071006       4.500415\n",
       "chr15_80852914_80853833    0.01183432       5.101319\n",
       "chr19_38510660_38511315    0.00147929       7.793197\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "head(significantDEpeaks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "sigTF1 <- significantDEpeaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 676   7\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "dim(sigTF1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Annotating peaks\n",
    "\n",
    "We annotate the these peaks. We use `marge`, an `R` package working as an API for `homer` to annotate the motifs associated with these peaks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "dat <- as_tibble(sigTF1[, c(1:3)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find Motifs Across the Genome\n",
    "From the highly open regions we now find the enriched _de  novo_ and/or _known motifs_ via the `find_motifs_genome()`\n",
    "function. This function makes a connection to the HOMER utility `findMotifsGenome.pl`. A de novo and known motifs enrichment analysis is run on the test regions loaded prior, and writes results to the designated path directory. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "## Create a temporary directory to write results\n",
    "## This directory is erased once R session is closed\n",
    "#results_dir <-tempfile(pattern ='test-dir_')\n",
    "results_dir <- '/home/mqbsxsm2/Rna-seq_Data-Analysis/ScAsAT_notebooks_Updated/HOMER_results/TF1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "find_motifs_genome(dat,path = results_dir, genome ='hg38', overwrite = TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Denovo and Known Motif Enrichment Results\n",
    "By default `HOMER` produces two distinct files for _de novo_ and _known motif_ enrichments within the results\n",
    "directory, in `homerMotifs.all.motif` and `knownResults.txt` file respectively. In `marger`, these results can be\n",
    "read in using the respective `read_*_results()` functions, where * is either denovo or known."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "known  <- read_known_results(path = '/home/mqbsxsm2/Rna-seq_Data-Analysis/ScAsAT_notebooks_Updated/HOMER_results/TF1/knownResults.txt'\n",
    "                             , homer_dir = FALSE)\n",
    "#denovo <- read_denovo_results(path = results_dir, homer_dir = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[90m# A tibble: 363 x 14\u001b[39m\n",
       "   motif_name motif_family  experiment accession database consensus log_p_value\n",
       "   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m         \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m    \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m           \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
       "\u001b[90m 1\u001b[39m Mef2c      MADS          GM12878-M… GSE32465  Homer    DCYAAAAA…           2\n",
       "\u001b[90m 2\u001b[39m Fox:Ebox   Forkhead,bHLH Panc1-Fox… GSE47459  Homer    NNNVCTGW…           2\n",
       "\u001b[90m 3\u001b[39m ZNF519     Zf            HEK293-ZN… GSE58341  Homer    GAGSCCGA…           1\n",
       "\u001b[90m 4\u001b[39m Foxo1      Forkhead      RAW-Foxo1… Fan_et_a… Homer    CTGTTTAC            1\n",
       "\u001b[90m 5\u001b[39m Nr5a2      NR            Pancreas-… GSE34295  Homer    BTCAAGGT…           1\n",
       "\u001b[90m 6\u001b[39m ERE        NR,IR3        MCF7-ERa-… Unpublis… Homer    VAGGTCAC…           1\n",
       "\u001b[90m 7\u001b[39m Oct4:Sox17 POU,Homeobox… F9-Sox17-… GSE44553  Homer    CCATTGTA…           1\n",
       "\u001b[90m 8\u001b[39m PGR        NR            EndoStrom… GSE69539  Homer    AAGAACAT…           1\n",
       "\u001b[90m 9\u001b[39m Oct2       POU,Homeobox  Bcell-Oct… GSE21512  Homer    ATATGCAA…           1\n",
       "\u001b[90m10\u001b[39m TEAD4      TEA           Tropoblas… GSE37350  Homer    CCWGGAAT…           1\n",
       "\u001b[90m# ... with 353 more rows, and 7 more variables: fdr \u001b[3m\u001b[90m<dbl>\u001b[90m\u001b[23m, tgt_num \u001b[3m\u001b[90m<dbl>\u001b[90m\u001b[23m,\n",
       "#   tgt_pct \u001b[3m\u001b[90m<dbl>\u001b[90m\u001b[23m, bgd_num \u001b[3m\u001b[90m<dbl>\u001b[90m\u001b[23m, bgd_pct \u001b[3m\u001b[90m<dbl>\u001b[90m\u001b[23m, motif_pwm \u001b[3m\u001b[90m<list>\u001b[90m\u001b[23m,\n",
       "#   log_odds_detection \u001b[3m\u001b[90m<dbl>\u001b[90m\u001b[23m\u001b[39m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "known"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "write.csv(known[[1]], 'TF1_motifs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Disease Ontology analysis\n",
    "\n",
    "We now do a disease ontology analysis associated with the TF1 significant peaks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "sigTF1Sel <- sigTF1[sigTF1$log2FoldChange>2,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "bedg1 = sigTF1Sel\n",
    "jobg1 = submitGreatJob(bedg1, version = \"3.0\", species='hg19')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "tbDisG1 = getEnrichmentTables(jobg1, ontolog=\"GO Biological Process\")\n",
    "dftbDisG1 = data.frame(Name = tbDisG1[[1]][,2], Binom_p=-log10(tbDisG1[[1]][,8]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                                                          Name   Binom_p\n",
       "1                                    regulation of endocytosis 10.463048\n",
       "2                           positive regulation of pinocytosis  8.882493\n",
       "3                                    regulation of pinocytosis  8.705260\n",
       "4   positive regulation of sodium:hydrogen antiporter activity  7.031639\n",
       "5            regulation of sodium:hydrogen antiporter activity  6.345670\n",
       "6                                          response to hypoxia  6.231737\n",
       "7  negative regulation of calcium-dependent cell-cell adhesion  6.205563\n",
       "8                          response to decreased oxygen levels  6.053775\n",
       "9                     regulation of vesicle-mediated transport  5.921590\n",
       "10                                   response to oxygen levels  5.590102\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "dftbDisG1[1:10,]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Detecting motifs for cell-types (HL60)\n",
    "Now, we identify the motifs associated with HL60 cells."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again we first identify the HL60 cells and the cells that are not HL60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "c1CellName <- rownames(annotFilt[sub('-.*','',annotFilt$CellInfo) == 'HL60',])\n",
    "c2CellName <-  rownames(annotFilt[sub('-.*','',annotFilt$CellInfo) != 'HL60',])\n",
    "c1CountData <- cdFiltTop[,as.character(c1CellName)]\n",
    "colnames(c1CountData) <- paste0('C1_', colnames(c1CountData))\n",
    "c2CountData <- cdFiltTop[,as.character(c2CellName)]\n",
    "colnames(c2CountData) <- paste0('C2_', colnames(c2CountData))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We prepare for the Differential Accessibility analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sg\n",
       " C1  C2 \n",
       " 92 648 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "CellCluster.Annot.counts <- cbind(cdFiltTop[,1:3], c1CountData ,c2CountData)\n",
    "rownames(CellCluster.Annot.counts) <- paste0(cdFiltTop[,1],'_',cdFiltTop[,2],'_',cdFiltTop[,3])\n",
    "sg <- factor(gsub(\"(C1|C2).*\", \"\\\\1\", colnames(CellCluster.Annot.counts[,4:dim(CellCluster.Annot.counts)[2]])), \n",
    "             levels = c(\"C1\", \"C2\"))\n",
    "# the group factor should be named accordingly\n",
    "names(sg) <- colnames(CellCluster.Annot.counts[,4:dim(CellCluster.Annot.counts)[2]]) \n",
    "table(sg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time difference of 2.124501 mins\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "start.time <- Sys.time()\n",
    "diffRes <- getDiffAccessInformationGain(CellCluster.Annot.counts, groups=sg)\n",
    "end.time <- Sys.time()\n",
    "time.taken <- end.time - start.time\n",
    "time.taken"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We consider all the peaks that have an information gain of above 0.01 to be significant. So we extract those peaks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 562   7\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "significantDEpeaks <- diffRes\n",
    "significantDEpeaks <- significantDEpeaks[order(significantDEpeaks$information.gain, decreasing=TRUE),]\n",
    "significantDEpeaks <- significantDEpeaks[significantDEpeaks$information.gain>0.01,]\n",
    "rownames(significantDEpeaks) <- paste0(significantDEpeaks[,1],'_',significantDEpeaks[,2],'_',significantDEpeaks[,3])\n",
    "print(dim(significantDEpeaks))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 562 peaks that are significantly differentially accessible in HL60 cells compared with all other cells indicating that these can be marker peaks for these cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                          Chr     Start       end information.gain\n",
       "chr8_130677121_130679694 chr8 130677121 130679694       0.08531696\n",
       "chr8_130566009_130568572 chr8 130566009 130568572       0.07303881\n",
       "chr8_126517715_126520158 chr8 126517715 126520158       0.06547579\n",
       "chr8_130558448_130561453 chr8 130558448 130561453       0.06025955\n",
       "chr8_126524439_126527264 chr8 126524439 126527264       0.05439976\n",
       "chr8_126440866_126448872 chr8 126440866 126448872       0.05033241\n",
       "                         rawMeanGroup1 rawMeanGroup2 log2FoldChange\n",
       "chr8_130677121_130679694     0.3260870   0.001543210       7.723179\n",
       "chr8_130566009_130568572     0.3369565   0.009259259       5.185522\n",
       "chr8_126517715_126520158     0.3695652   0.023148148       3.996860\n",
       "chr8_130558448_130561453     0.3804348   0.032407407       3.553254\n",
       "chr8_126524439_126527264     0.3369565   0.026234568       3.683022\n",
       "chr8_126440866_126448872     0.7934783   0.324074074       1.291867\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "head(significantDEpeaks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "sigHL60 <- significantDEpeaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 562   7\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "dim(sigHL60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Annotating peaks\n",
    "\n",
    "We annotate the these peaks. We use `marge`, an `R` package working as an API for `homer` to annotate the motifs associated with these peaks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "dat <- as_tibble(sigHL60[, c(1:3)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find Motifs Across the Genome\n",
    "From the highly open regions we now find the enriched _de  novo_ and/or _known motifs_ via the `find_motifs_genome()`\n",
    "function. This function makes a connection to the HOMER utility `findMotifsGenome.pl`. A de novo and known motifs enrichment analysis is run on the test regions loaded prior, and writes results to the designated path directory. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "## Create a temporary directory to write results\n",
    "## This directory is erased once R session is closed\n",
    "#results_dir <-tempfile(pattern ='test-dir_')\n",
    "results_dir <- '/home/mqbsxsm2/Rna-seq_Data-Analysis/ScAsAT_notebooks_Updated/HOMER_results/HL60'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "find_motifs_genome(dat,path = results_dir, genome ='hg38', overwrite = TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Denovo and Known Motif Enrichment Results\n",
    "By default `HOMER` produces two distinct files for _de novo_ and _known motif_ enrichments within the results\n",
    "directory, in `homerMotifs.all.motif` and `knownResults.txt` file respectively. In `marger`, these results can be\n",
    "read in using the respective `read_*_results()` functions, where * is either denovo or known."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "known  <- read_known_results(path = '/home/mqbsxsm2/Rna-seq_Data-Analysis/ScAsAT_notebooks_Updated/HOMER_results/HL60/knownResults.txt'\n",
    "                             , homer_dir = FALSE)\n",
    "#denovo <- read_denovo_results(path = results_dir, homer_dir = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[90m# A tibble: 363 x 14\u001b[39m\n",
       "   motif_name motif_family experiment  accession database consensus log_p_value\n",
       "   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m        \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m       \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m    \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m           \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
       "\u001b[90m 1\u001b[39m CLOCK      bHLH         Liver-Cloc… GSE39860  Homer    GHCACGTG            2\n",
       "\u001b[90m 2\u001b[39m STAT6      Stat         Macrophage… GSE38377  Homer    TTCCKNAG…           1\n",
       "\u001b[90m 3\u001b[39m Sp1        Zf           Promoter    \u001b[31mNA\u001b[39m        Homer    GGCCCCGC…           1\n",
       "\u001b[90m 4\u001b[39m E2F4       E2F          K562-E2F4-… GSE31477  Homer    GGCGGGAA…           1\n",
       "\u001b[90m 5\u001b[39m ISRE       IRF          ThioMac-LP… GSE23622  Homer    AGTTTCAS…           1\n",
       "\u001b[90m 6\u001b[39m c-Myc      bHLH         LNCAP-cMyc… Unpublis… Homer    VCCACGTG            1\n",
       "\u001b[90m 7\u001b[39m Olig2      bHLH         Neuron-Oli… GSE30882  Homer    RCCATMTG…           1\n",
       "\u001b[90m 8\u001b[39m Tbet       T-box        CD8-Tbet-C… GSE33802  Homer    AGGTGTGA…           1\n",
       "\u001b[90m 9\u001b[39m IRF1       IRF          PBMC-IRF1-… GSE43036  Homer    GAAAGTGA…           1\n",
       "\u001b[90m10\u001b[39m FOXM1      Forkhead     MCF7-FOXM1… GSE72977  Homer    TRTTTACT…           1\n",
       "\u001b[90m# ... with 353 more rows, and 7 more variables: fdr \u001b[3m\u001b[90m<dbl>\u001b[90m\u001b[23m, tgt_num \u001b[3m\u001b[90m<dbl>\u001b[90m\u001b[23m,\n",
       "#   tgt_pct \u001b[3m\u001b[90m<dbl>\u001b[90m\u001b[23m, bgd_num \u001b[3m\u001b[90m<dbl>\u001b[90m\u001b[23m, bgd_pct \u001b[3m\u001b[90m<dbl>\u001b[90m\u001b[23m, motif_pwm \u001b[3m\u001b[90m<list>\u001b[90m\u001b[23m,\n",
       "#   log_odds_detection \u001b[3m\u001b[90m<dbl>\u001b[90m\u001b[23m\u001b[39m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "known"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "write.csv(known[[1]], 'HL60_motifs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Biological process analysis\n",
    "\n",
    "We now do a biological proces analysis associated with the BJ significant peaks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "sigHL60Sel <- sigHL60[sigHL60$log2FoldChange>2,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "bedg1 = sigHL60Sel\n",
    "jobg1 = submitGreatJob(bedg1, version = \"3.0\", species='hg19')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "tbDisG1 = getEnrichmentTables(jobg1, ontolog=\"GO Biological Process\")\n",
    "dftbDisG1 = data.frame(Name = tbDisG1[[1]][,2], Binom_p=-log10(tbDisG1[[1]][,8]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                                        Name  Binom_p\n",
       "1  negative regulation of biological process 16.36668\n",
       "2          regulation of signal transduction 16.31961\n",
       "3           regulation of cell communication 16.16977\n",
       "4                    regulation of signaling 15.88796\n",
       "5        regulation of programmed cell death 15.63753\n",
       "6            regulation of apoptotic process 15.63339\n",
       "7    negative regulation of cellular process 15.26132\n",
       "8                         response to stress 15.14043\n",
       "9                   regulation of cell death 14.54183\n",
       "10                      response to wounding 14.39194\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "dftbDisG1[1:10,]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the BJ cells are from skin or foreskin tissue it is heaviliy involved with the regulation of cell-singalling process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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