ScAsAT_functions_Buenrostro_All_Bam_Together.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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  },
  {
   "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 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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getAggregatedPeak(BAMFolder, BAMFilePattern):\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": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mqbsxsm2/anaconda3/lib/python3.6/site-packages/rpy2/rinterface/__init__.py:145: RRuntimeWarning: Loading required package: pacman\n",
      "\n",
      "  warnings.warn(x, RRuntimeWarning)\n"
     ]
    }
   ],
   "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": 7,
   "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": 8,
   "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": 9,
   "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": 10,
   "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": 11,
   "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": 12,
   "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": 13,
   "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": 14,
   "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(\"Cordinate 1\") \n",
    "    p <- p + ylab(\"Codinate 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": 15,
   "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": 16,
   "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": 17,
   "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": 18,
   "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": 19,
   "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": 20,
   "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": 21,
   "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_Finall/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": 28,
   "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": 29,
   "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": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%R\n",
    "peakFile <- 'All_Human_Bam.csv'\n",
    "cd <- read.csv(peakFile, sep=' ', header=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R -i peakFile\n",
    "cd <- read.csv(peakFile, sep=' ', header=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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 740 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": 23,
   "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": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "colnames(cdFiltTop) <- gsub('_.*','',colnames(cdFiltTop))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] \"seqnames\"   \"start\"      \"end\"        \"SRR1779589\" \"SRR1779592\"\n",
       "[6] \"SRR1779594\"\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "head(colnames(cdFiltTop))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read annotation file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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$run_accession\n",
    "head(annot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 740   3\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "annotFilt <- annot[colnames(cdFiltTop[,4:dim(cdFiltTop)[2]]),]\n",
    "dim(annotFilt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "write.csv(annotFilt,'sraAnnotWithSpeciesFilteredList.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] \"BJ\"    \"GM\"    \"H1ESC\" \"HL60\"  \"K562\"  \"TF1\"  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "Batches <- levels(as.factor(sub('-.*','',annotFilt$CellInfo)))\n",
    "Batches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "cellNames <- colnames(cdFiltTop[,4:dim(cdFiltTop)[2]])\n",
    "cellTypes <- as.factor(sub('-well.*','',annotFilt$CellInfo))\n",
    "#cellTypes <- as.factor(gsub('[0-9]','',colnames(cdFiltTumorTopSel[,4:dim(cdFiltTumorTopSel)[2]])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 750 -h 550\n",
    "#pdf('MDS_with_batch_effect.pdf')\n",
    "MDSClust <- plotMDS(cdFiltTop, 4, groups=cellTypes, ret.val=TRUE, text.label=FALSE, title=\"MDS without additional filtering\")\n",
    "#dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cells largeley seprate based on the cell types although no obvous cluster comes up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "cellTypesRepComb <- as.factor(sub('-.*','',annotFilt$CellInfo))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": 42,
   "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": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uq6oOwtFAAu1cdAqF2IA6+oR7NcpUGpYUBT3oMhl0zU16hrM71wAA2TrqcLEyRfXCJe0bOHAgAKhUqidPnjT1XBA2Bgm0c8El4MNedfQJdngfO5qBmUn6owUVltO3rpHfPpJ3OZW35iTMPkBNPa2UayBfBi/aglIqrVJS58iRI6+//np4eLhAIAgICOjWrduGDRt0uoovsLFjx2IYplQqAUAgEGAYVlBQYLw2JSVlwoQJrVu3lkgkrVu3njlzZmGhha0LkiQXLlzYrFkzgUAQFhb2zjvvPHpUR6YBxDPxooR6v0AQdX3phjh8NPMTNXOmuIr6/pSiG1QUzL4m02F2ekX7V0Mh3LtxJ9foHD16FADatGnj7V15q8uWLfv8888BQCAQeHt7l5SUXLp06dKlS2fOnImPjweA7t27czicnTt3UhQ1duxYgiAEgoro/z/++GPq1KkajYYgCC8vr+Tk5OTk5G3btt24cSM0tDLnuFar7d+//7lz5wIDAyMjI+/du7d9+/ZTp07dvn3bz+8Zkr0gagGtoJ2RFv61nS13+PLe7lVTLREA4RrLqUUWHwTKSe3UFEWlp6evXr36008/5XA4q1evNp7Ky8v78ssvMQz75ZdfFApFVlaWUqncuXMnn8/fvXv3gwcPAGDatGlbt25lRXnLli1bt251dXUFgEePHk2ePFmr1X777bfl5eX5+fmZmZl9+vQpLS2dNGmS6QSOHDly9+7df/75Jzs7OyUl5datW97e3oWFhVu2bGncJ+HMIIF2Rnxrrh4LAIeTGmseDcLPj6n/XNH3UPDeKxYOkPGHyPjvFgtbKmvMl7TlsfP4kIWFhRmdoLlcbnh4+Ny5cymKOnbsWN++fY3d/v33X5qm+/fvP3XqVA6HAwA4jo8ePZrtk5KSUstbfPnll3q9fsaMGZ9//jmbwjA4ODg+Pp7P5yckJJg68zEMs3Hjxh49erCHrVq1mj17NgCkpaU1wK2/oCAThzMS5Fbb2dvZjTUPG3PsDsRfBQCOK8740RgABOjrXmHcVdMAtsk42OQQBGGa3pamaZqmtVrtxIkTN27cOGDAALZ95MiRFEVVT4SrVqsBoHavZ9ZgwkqtES8vrzVr1uTk5MhkMnahDQCBgYFDhw417dasWTMAaOSoGecGCbQz0jWCPpyEWzRlMAAdQxp9QjbgZgarzgAAQrq2HNwqnBE/7fCvRD/P1Xl+Jj569MjUCkzT9KNHj3788cf169cPGTLk7Nmz3bp1AwAMw4xZcDMyMm7dunX16tXjx48nJibWPn5BQQGblzw8PNzs1NSpU81amjdvbtaC487zqO0E9ECdEQKXTXnJ8ikMYFjbxp2NbUjJtbbnHdeysy7kFbF+v5uuRTjT1xPP0lGXZBo5VYdXx2Od4c98+XnHsdHjON6sWbN169aNGDFCr9cvXbrUeOro0aN9+/Z1c3MLCwsbPnz42rVrJRJJRERE7QNmZGQAgL9/rXsYT0FVCxoBtIJ2Tmg3Ifw0Gu4VQL4cHhRCUjZ0CIFukRDjCxyH/L0vtrqSAR/oNI8MMPCBEu7Pk+zPMwChBX454OS+NoHDvS2Uhrmp0I1PzbvztPB5F6nwfMcQruOsXkaMGLFv377bt2+zhzt37nz77be5XO6YMWNee+21Tp06RUdH4zg+duzYx48f1zKOr68vABQXWxGPCmCrWkKIWkAC7bzwONA6EFoHwmstmnoq9YemAcchswTcrY6RNAADGA0YA9RTLTYIQO0HkswRSTm6PtElcuyv63AjA1oHwYDWQLiRHa6mm45wRa4ZlJR1ol2wLe+kIWG10ugnt2TJEoZhNm/e/J///Me0W535OoKDg/l8fnl5eV5entk6evPmzdevXx8/fvzLL6NsiI0HEmiEPcIA7E+EQ7cAAHykz5CAjwFQcvUAADS/2jkCMMMDlf7oJV5aLgDAnWy4kw1+XS3UCTtZoi7VGzy4jvFrY//+/QDQtm2F8SorKwsAXnqpipkrJyfn/PnztY+D43jPnj1Pnjy5evVqNoKcRafTLViwIDc3d/r06TaeOqJWHOdXHOJF4t8HFeoMz5geVcXVZwtYwa2WAAijAeBOiSGtqjn7QQ1lwsrqslk3Oewm4axZs/766y8Mw6ZNm8a2s9t3q1atYt02KIo6ePBg79692bjB1NRUs3FycyufyPLlyzEMW7Vq1ffff89eXlZW9t577+Xm5nbo0CE2NrZxbg3BggQa0ZRoSDhyG36/AOfuVYnMTrV6S9DMZUyi5xpwBgCAowaOyXYfrxyAAcAOF1UTbsJCKEtfd1GE0Dalx21IZGQktypRUVE//fQTAMyfP79Pnz5sty+++AIAfv31V3d394CAAKFQOGzYMG9vb7Z9wYIFrLMHAAQEBABA586dO3bsyJqeO3TosHLlSgzD5s2bJ5VKAwMDvb29d+zY4eHhsWPHjia56xcZZOJANBkkBetPV2jxP/fg/y5AqCdklAAA6DgMH6zag9LgjKSq19349OhMkfKKR6FSUAyUCBgO4FogdAAYaLy3KTm9eYZwssJ2kSdUn9KXmL3Va3mClc287XALrLoLc2hoaJs2bWbPnm0aqDJs2LATJ04sW7YsOTmZJMlXX3114MCBM2bMAIC7d++ePn3auBBev379jBkznjx5YpqIY86cOd26dVu9evWtW7eys7NbtWrVq1evBQsWeHp6NvwtIqqAMYyTRsJaoqSkBABs9TkrLy93cXExOpw+J+np6QEBATye1c4KtVJSUmKr21QoFAqFgl1qPT8ajYZhGJFIBAA3M2DtqecdMFVItdRYWGdkipSnfXKqNOnFoPMEAIKBGC3Hi8LlwvI0lzItUUX1Dp/17ljGE/XiSkdVs2IjEI0LWkEjmgw1WUeH+wKDHphl3QihiIq7VhCllEaqKqPYNTiTLKSShZSMYLoqzc0RIWoJwWAGjAGAgR6Snu6CBzLhlnQAAAMGKUIKAEBcBliFOicf9SvnMaFKgsNgAECWvEALF4TdggQa0WQ0ryvl2U2RXo0zE7MK4gK4uUKVkqs3FegzLmQhlwaAuwLqroBa4AFZ94WmlxswZqyvy//F+nMxDADOFNNb0qtmi36qzt1KJRguiFRUxiirBIQzpM1GODhokxDRZHi5wKzXKg/NlqyP+QY1zgBAhk67MqPMj8+Rc8gdwQ//9SxI91HGu2tZdWaZ14wzrEWVhHYdW+mLe0ZtbxXAfRpP0dcLX9C8ckUyIaJiC/GlcvHUDJ88F165oOJsoYSn8bO7HULEC4h92aDLysq2bt2amppaUlISGBjYpUuXuLg4LtdmfyrIBl0PGs4GbeRmGf32CYOPHtfgDAAjYPAiDn1f8NTNjasEfqlp/3H+0iGeUldcEP9IocK4bwQIWrhgnc/phDQWrSUEDPZeLP5+W8uLDzkFhTomXIQRGGi1TOIhRnWvsicGINWqeQbK9w1hQA+k0Ygmxo5MHHK5fMaMGSqVql+/fr6+vqmpqTt27EhMTPzuu+9YEWQYZtSoUSRpbrncuHEjShDu0IRJsEyeQYsxWpwp4NL+etxbj4fqiAyegcYA9BIzgd6aJ9+aJx/h7bI+FJNICBcX4r2bJABocOa2iAIALw3xPlj+qpNyQMqpWFMLBFj7V7EL9ypOYQCtCjN9leUAAFuAEYZinVwb6p4RCCuwI4HesWOHXC7/7LPPunbtyrbs2rVr27ZtR48eHTJkCAAUFRWRJBkdHR0cXCUGVygUWhgO4Thk5WOjSysMFHKckT51m8viGc64kDWlrttXpGhHCOZIAADKq2a4PFJgbQ5okQe072ooP0K6kAYDrq1QZwAAYH7OwDa3sc7ZD4FoEOxIoO/cuePj42NUZwAYNmzY9u3b79y5wwp0dnY2AMTFxaFsAM4EQ8OaE5WHUhM9DiaJQD2RJS4LE3LTNRayDH+dr50TAgDQ2wvfn1cpytPDrf1gG0oZwz4Nm+1fSlbLYyfXgysydCCaDHvZJGQYhsPhdOjQwbSRTTrOxqcCQE5ODgCEhYUxDFNn2heEo3DvbG1nAzkY8GWsOrtZysNH0oySgr5exLSnojwumFja0ipVpdWM+mylxYzCqllFpEidEU2JvaygMQxbs2aNWeM///xD0zRbpgEAsrOzMQyLj48/d+6cXq8Xi8VdunSZOHGiWTFjhGOR+S9AzfmH7zE0MBhgDACUUwZgoNLmwMBrLpz4AuLDtIqV79YOvDFBBMc6owR5jyr/SUVjlaKvI8RajkRAVSwIyL7BlgsdIhCNhb0IdHWOHz++ceNGsVjM2jcAICcnh2GYhw8fjhs3TiKRJCUlnTlzJi0tbe3atWbODxqNpqioyLSFx+OJxWK9Xs8wjEKhsMkMdTodhmE2rCKhUql0Ol3d/axAr9fb6ja1Wq3BYLDVaOw/gWnIch5PctSVfENWEbaXx6X9nxayuiTRlxE06KXAqzANcwCP5HDuUSQAtOa75CkkH+ZXPv9xN8iuIsrHCkcY7mkZ72CpF4CeECq4nhRecY2c56PmuuEMRWEC+jLO91Zyullwc3JxqbEEIgJhQ+xRoHNycjZt2nTjxg2pVPr55597eVVEDMTGxkZHR7/11lts3tt+/foFBQX9+eefBw4cGDVqlOkIWq2WrQ1hxNXV1cvLi6IoALCV1hgMBouV3+qNWq221WgURdG0bZKxGQwGGwo0OyvT7yG6OV4A+J+eGk8DrgUo59AYQLiO8DDgOANSAy7UihSEUosbupb4RitdAWCCjzbbU72+yMLm8JVCTW/XynRIWgrLLONxcAjzIHGsQmp5T/TigxUmMq5B4wIlZfzKNTyF8eCprUObRjGt1dXfBQk0onGwLz9ohmEOHTr0xx9/6PX6nj17Tpo0yc2ttvqnOp3urbfe6tix44IFC6wZH/lB14OG9oPOU0PAySq7c200nI4qrqkxAwCyRKpgdWXS/ocCw3mJhVDxu68Kmksqrsspg6/2VrS3DICpfUHCBwBgDhUy+6rkGC0WhpraOozwW3HcpyI7B6LJsKMVNMMwa9asSUhICAkJmT17dlRUVJ2X8Pl8qVQqlz9LwmCEfaAxwPxU/U+PzZN/YgAdVVwAc/c2U3UGgCgtcb5a7aoPwjlGdQaA/TcqT6XmwpFEGCamyPsG4TWFmRgr+EShROih1guA4WsrzS/8Vo6RsB/hrNiRQB88eDAhIeGVV16ZNWuWsXiPkdzc3I0bN/bu3bt3797GRrVaLZfLO3Xq1KgTRdiCL9IsqDMAcKz7RZfNq+LpHCLEfu/A6+NVZT8gMb3KJZmXqfLHWp5BTejMS6iElz9W84Iz3NwBwF1LuWoooTt4vUoIuyIvDkRTYkdudgcPHnR1dZ0zZ051dQYAf3//jIyMrVu3mhpDd+7cyTCMqes0wlFY/YiS0Fh3BW+gnN9WXelAp8cgh1d3mMldaYnxdQsxdq4b30ydAaBTeJVDbx0NAFza8jZsbGEW30ABQJmAk+4uSAOB06vzt99+i1VFKpV26dJly5YttVg+p0yZgmGYsUAtokGxlxV0bm5uUVGRv7//xo0bzU6FhIQMHToUw7CpU6cuXbp05syZPXv25PF4ycnJKSkpffr0Mau9hrB/DDQMc+F4PKlQQH8S96WIE9IK6bwi0ncBCCQtmBc0OJXqWp4lUpZxdcCU/8F36/KXSEoB7Kc07/DNJHVEB7j+pOJ1lNzQO18PAAa8RtkVkxqdsHL37+rv0OldsJ2Tjp0yevRo1pOVYZj8/PyjR49OnDgxLS3NtCYhoqmwF4FmCzrk5eXl5eWZnerQocPQoUMBoEuXLt98882ePXvOnDmj1+tDQ0NnzpzZr1+/Jpgu4jl4UoyvPAkeUEUoA0lcwGBajAEAGYc5ISVdKSxEQHcvFWrJSrOyjqCTXEsAABgY6Cnq+z+RsV6VbJuOG05w/J4KKkn7PVKsi4YrtwXiLDpIXVEJS0uIBYSIZ7Dgm6HlVsnQX54Nt3dD+0cG1l4AACAASURBVNG2um87Zdy4cYMGDTIeKpVKturVnDlzzAp7IxofexHoDh06HDx4sM5ubdu2NZYuRjgWDANZ16HwPu9nreWdN54BtBwAAF8BE1ZOt1RKAEBbtU9nH65voFuGVh8r5n/Md2OYKvYK/RNDhUDLKcPsVAxAANALoIwfqCeM4ouV8/3ATVeix9w1Kj9lGdtaInTXV1tcy6wujdjY6HRMWSnm4QE8G5d9kUgko0ePXrJkye3bt5FANzn2ItAIpyfzKtw7CVqcAF/LHeQchgAgGCwayCil5TRyr7fE2oVUXE8rmEKoItC4a8Xymfm7yNQJxI3MxxgDSYhUXHcmXOT1kfBGvKT4IeRKPBhMLNFqKYJvwPhB5dp0dyHfoI8qzfdTlhWKXXMDfQDsLhWX4dplas929jXnrf8QHW1s4mMN0Ba3ghCNDBJoRCNR+gQAgF9D9MxVsb6bkttcywEAGcfc7tszBng4dImESJ/KRtwFYzqR2PUKz3F+Kw4/5unavKiKcGOMAQB4BjXPoL5ZFiOSgcgdAEBA0YSBp+MShIEkGMqFBE8NGSLL9tAoAcBHJfO5LwN5S5Da0Z8JIys3qjMAUPF/4s2aY1KbpUVVKpXx8fH+/v7Gyt+IJsSOPnmIF4FARekYrXZneEXYi5LLpPKoHJ4hgCRYdQYAV8o8WufdboBbDLEcoBPEcjlFfMIbF3bgVDolhQjhhmXv+EBFXvkOl8h33dWlmPouw6eUrmQhAAAwaq57aJnAXac07c+kKrGXa4uWamSY3Gzzlryc5xHorVu3Xr58GQAYhikoKDh69CjDMHv37rVVzBTieUACjWgkPMKh6CG0LMpuCRAlVz2Qin8NdTngymGX1O2pGr0lJvaoQZ0BAICIAUlncynBBnqTaRruvUqNNsYl+qhkcF2mK1cEx4Xyu+OSrwuNF4n05SKzgYABK3MvNRaYu3mAKObxXCGju3btMmvx9/e/efMmSuprDzi7DxHCPtA/MUiva9rkV/iwhyk1r+YW7/PhGA0eGqyK420213DMVXdeQspaqL9TZw+7nbO38FmSgfBw1YBA0wYzleU/lD3+U1u0q1oCaHMwrGW1gMUmBfPzJ17ubjwkuvbAvGsw6lvH4cOHmacYDIb79+937dr1ww8//OGHH557sojnBQk0osFhtEzJSo0uzQCA6TgVi1SzT16KsEpU4UOBIY9LMxi4pom8E4PUKW4TbxTvK1KC1bheMvfXNCOsRCVJtxDKaETJE14Micm+a3fR3pwRb3H/O4MzNI47dSZn+Ki6L7AaHMebNWv2559/isXiDRs2AMDKlSuHDBnC1spgYTOOIRoHJNCIBkefVbkzqOR6kUSFRu+hKkOuFQSz00N7Tax/wjMAQG8Fb3A5v6eiwnYRrBG/XOK7Pd/qpCsaA3atvK5OXAZwBc/LeGyWL+mRu6+Gwyu6b+17NiZ4ZDPilZ54eN35auqBUCj08PBgC2WkpaUdPnzYNDqhsLAQAFxdUbXGxgAJNKLBwUWVBgYDxinn+1FzmuMbW4980+vNwMpPoAZnsniG8KcBhN5VrdIBWhFYii20TKl5fSwK55cLKioLk7ionB/AYDgAaDjSEmGIjOdTJggo5/vriQqnOjXXTcmTAIAdJXtsLM6dO5eVldW5c2cAaNmyJQAcP36cPSWTyS5cuODm5hYaGtqUU3xhQJuEiAaHE4Dz23B0SRQA6AjsRgxHq+RHZUG2wPBXThW3O9+atwoBYKhftT08UzI1zP4C5pYcayfFhviYnVTwvPQ4v1AUgTEMUzXptgHjGDgVhuYyvj/OUAzgDIZjwACAq23SrNovRi8OADAYDE+ePNm7d69AIFi2bBkATJgw4fvvv1+4cGFmZmZwcHB8fLxcLv/pp59smAYdUQtIoBENDwZu7wk0/+rV2fTvDOe+joBUOJkKEn/zP3JtzX/1AWHkuMCa0+RrDPTCB+xL5pacuSXHPgxlfq4o2qDkeurxiog7Hq0WUgoAIHGhmiuttn0INMYBAFKCa7hERA+I6A7OjZkXR1BQ0ODBg5csWdKiRQsA8PDwuHjx4meffXb06FGZTBYTE7N9+/axY8c20WRfOJBAIxoDjAuiXtzkx3A/obJRmYfzPDHSxH8jh2vI4dKB+sp1tI8LNPOD1kHQOaI2t1zmSTV/DBFRJAonGD0NHBqrGJBHq111BRWvDWoerdbjApIQGeWbBXfFAz8WhrjX40YdiS+++OKLL76os1tERER1VzxE44Bs0IiGpURv2FOoOFaiImlGoTU/+4ppvV8GDBickeoKuJV2j0IFMAy8FFFtoVsVjG/+SWY0wABGYTyjOgOAsSAsC8+gEevL3LXZ7p2r6Ds1UEC4o5/wiKYHraARDchVubbLtQo7w6seohW+gWZrgpO9BdtzyaPFWgbTPVYIrpVwKAzc+AAmm3z/PoTJvep6pzAh1sqFSa7wlSYJUfkWhhfLIVPMUktblF2Mey6vqFMsZFA0hpWKuPpzmKQZSP2e5VYRiAYAraARDciSJxVp9QkGo+65rvu78vPWOggWDgcCh3cCeRSh3FVcek2XC+LsqdHKfgFVPpZdIqx4JwLDpoaQYZ46Qqziust5PgAYxmCGwCqOHxpCUpNfRn4pkefCL5Dw9DgGAKXpz3Sjjs3+/ftHjhwZFBQkEAgiIyMHDhy4Z88es5z9c+bMwTCMIIjqCYEBQC6XCwQCDMOmTJnSWLM2Z8KECc5nHEcCjWhADhUrwcADrU+boogIVaU5o2dzmDMAQjwBAM6Vq/cUKoDhgoEPABtyS19uVSUUYnjHWt+DAeaaTL+nIHunqoDylPF9VVz3CrMGxjBV3e30hLCc71/d6KwjxGYlRHi1Oow4DSqVavDgwSNGjDh16lR0dPSUKVM6duyYmJg4atSoXr16qdXmWbNpmt6zZ0/1cQ4ePGhaqb3xycjI2Lt3b939HA1k4kA0IEO8pIfS3QDAk6piWyg3+cPP0lKg8wD904hqYaGcoH4eQdw8oNc+pFsGMK6PuNDe/IOqvU5pEinQCHnKIuaxkgAIAJAJpDq8MvAE42OcQvOwN5IQkYRIxSV81UUifTnbQnX2CQ2HjCsVfTzCwDfGBrdv/4wZM+bIkSPDhw/fvHmzh4cH26hWq+fNm7d+/foJEybEx8eb9vf19d21a9eMGTPMxomPj/f19WXLbjQmNE0fOnQoMTHxt99+My2G5zQggUY0ICM9PQ+lGwBAV9X2S5j8civR8EFvkilf4xMr5ml+04XfoQCAkUF5msFznogbYhLScpWS/aEFAD6t52or9/1ctfJSgZTCeQDApXWcyyUSwHQcsdl6GQDypDyZ0D9Q5o4Bo/DkBcfxm3sA4aagSl2EbhDYDogXIJXbX3/9dfjw4ddee23fvn2m7SKRaN26dbdv396zZ09GRoZpTMqbb775yy+/ZGVlBQcHGxtlMtmJEycmTZr0888/N97sAQBAo9EMHz68kd+0MUEmDkQD0kpSsQK4UzXVRkQh3N4DxY8AAAq15h/CgYk5iwylGuKpLwcDuhSKvGco+1mTP11Z/j+t5kqF5UJMlplda0z97K7NEVEyEVXurs3hGiqcNJg23HwJ7563SM0likXc2/4ut/ylj3iC0jwMANwjyJgBENoFODauUmJj9IVJpXtH5C7HSvfFUUXJ9R7nt99+wzBs9erVFs/+/vvvhw4d4nCqrOHeeustANi9e7dpI2vfYE/VwtixYzt37iyTycaNGyeVSlNSUgCgtLT0gw8+iI2NFYlEMTExCxYsMLWruLu7f/XVVxcuXBg8eLCHh0eLFi0+/PBDubwy4l8sFmu1Wq1WW15eZ2S/Q4IEGtGAtHfF3/AhAKCcQ+/y0JZh+gG55Mg8mvsYCu7CjR1Q8hiaSfAoHdFPzusn57XQcADgokL9Y3PFl21lFaNgUHbZULpWo0s1AID2BkXl0ACAAcOlzR33DDgfAERUlawdLu7K4iB+sq/4TiG/UMLTEeYfe4psiLtvEBidvOh/bbX39wOA9t5fhZtbM+Qz5JAyJSkpKTw8PDY21uLZyMjIQYMGBQZWSQoYFhbWpUsXM7fo+Pj4Fi1a1DSOKSRJvvnmm6dPn+7evburq2tOTk7r1q23bdvWp0+fBQsWREdHL168uGfPnqbm7HPnzg0YMIDP58+ePTs6OvqXX37p2LEjmyeEhc/n8/l8Z63/gkwcTcOJsqKvnqReVZT19/CZERAxwMM8NNk5IDDY1Zn340190gMqKo+emGnQhnNzlJX6mJsEzVsRPRQVvhbBJIFzVCkAALA9VP3dTTcOgwEAt6SKtxytZMCSN4Ye55t6PRtR67FciluLK7Vn+LPfWxNB5l2t3sIP7fus48jl8tzc3DfeeMO0MTs7+9dffzVt6dixo5kNYfTo0XPmzHny5El4eDg8tW98/vnn1gR/JyUlxcXFPXr0SCgUAsA777yjUqlu3LgRFVWR9Wnt2rUzZ878+eef58yZw7acP39+5cqVc+fOZQ9Xr149d+7cVatWLViw4Flv2RFBAt0EXFOUDUi6yL4+UVp4orQQAMZIPFZ6e6WpZBvz0mlgRngFjPUJcoJgCQkHvujMhc5chgSMB9e3GcBkwcfQcCujSn9/kpvy9LVMyGEwXKCnJaSZOzO4DOGpUwzyB65SnczYyKV1HIakgUNUem8wAFgRU6NPhlckhHYBoR2VTKkDjGNeIxHj1sfjhLUkmFkwcnJylixZYtoyefJkM4EeNWrU3Llz4+PjP/30UwA4cOAASZKjRlXJekrTdFlZpfVJJBKxigwA3333Hfuaoqjdu3ePGTPG29tbJqv4R3znnXc+++yzkydPGgXa399/5syZxqFmzpy5evXqvXv3viACjUwcTcCch3eqN+5UlgZeP9Uv6WJ8Uc6eotx30q5Pe3CrgGxK1yXbgvEAACR+VRa+vi1q7B+m5uVKhbkufNrS0kzwElfXR3jfy8usnUOTEn0JQRsFGpMJROmu3hbfovO70GEseFrjZ203cP0788NeMx7yw/tz/Wr3Q7SMn5+fu7v7/ftVsql26dLFmLyfoigu17zMOQAEBgZ2797daOWIj49v1aoVm/TOSF5enpcJxs1DLpfLrrsB4PHjx3q9fuvWrW4meHp6qlQqNqMpS6tWrUy/RQiCaNu27cOHD+txy44IWkE3ARflpdZ0+yU3/Zfc9F+atZ0a4Di/wOsi5GU9YIymkMsw4NYCdguoVA4GUBlOkiusSBKdLiLDSzVZboJyIUeqM/eWUyeQ4o58dbXtPBrjCKgq7lZ6nMPU8OvbNdBis12DETz3YTuUV76nilM43m0kXT7CcAsyag2xsbGXL18289MwkpqaqtebZ21lGT169PTp0x88eODt7X3y5Mkvv/zSrIOXl9epU6eMh82aNWNfiMVioyWETfw/YcKECRMmmF0uElX+JjBb4wMAjuMURdE0jePOv75EAt0EeHN5RXprt6U+eHB7hFeAL8++HQusBsMh5GW9SMSlGBh+hTySYQCACBeim4bgUPrLnrK7LhXb8X0LBBLS4Ksks6V82k0QVl5lP7AsjSnRA+4NT+Q+4WUVCy6ZQPrEzTWiXOGqrdRokrD8IfeKAtzuiqVYBS70lPZe/vzjjB49+sKFC/PmzbOYC2nt2rU1XRgXFzdr1qxdu3YFBwdXt28AAJ/Pf/XVV2t/98jISIIg1Gp1r16Vgfw0TSckJEillTFNqampplpsMBhu3boVFRX1IqgzIBNHk0DX3aUKKWqrK4k4DpdK6SMFFZblx3zDn25k9zydUZ0B4I1cIQBwDTSPoqU6g76q60Wxiih6ANpSziN3v2uBUfe8Am/4R1zzDysRcR+5VzFo6AjX6iZsAGjez/Y35VhMmTKlXbt28fHxEydONI3yIElyyZIlrBOexQt9fX179eq1a9eu+Pj4Nm3axMTUJ6qHz+e//vrre/fuvXnzprFx06ZN/fr1u3XrlrElIyNj06ZNxsOffvopMzNz2LBh9XhHRwStoBsbkqFLrF4+s1yUlXaTeggcdL1XAyWkuRcGJ0qy60rQUR8ZhRkG5Qr7FAgAwIDjMcXmAcelQi6HZsLLNAxgJWKujC+S8Z/+KGagTCi5FNw8SFbmrqW0hERMYhGlmgw3gYzPMTpy+DQHsbn5+oWDz+cfPnx40KBBW7ZsOXDgQKdOnVq0aJGfn3/p0qX8/PzNmzevXLmypmtHjx79/vvvp6WlLVq0qN4TWLVq1cWLF1955ZVx48YFBAT8+++/p0+f7t+//5tvvmnsExgYOHPmzFOnTsXGxiYmJh4+fDgiImL+/Pn1flPH4kURaJqm9Xq9wWAAAFslDaAoiiTJZ/2pVY8SSgvS067ISuKjOxBWl7EwGAw2vE2apm01ml6vZxhGp9N1qFYs2+0xIaTFE3KEMUUViqzkEVqO+S3f9RL5qPU+yoovOamOUvYR5GQTpBzzV+i81HoAKBNySI6rmmNgfaUZwN01HJmg8tMe9BKp05n/U1j50DAM4/GcJNAwMDDw2rVrv/32244dO65du3b27NmAgIABAwZ88sknzZo1O3/+fE0Xjhw5ctq0aRRFVbdvWE+zZs2Sk5Pnz5+fkJCQk5MTFha2ePHi2bNnm9qde/ToMXXq1IULF65Zs8bX13fq1KnLly93cam5dINzgZnlrHJWKIrSaDQqlYphGImkmjbUC7VaLRAI6mELW5H3ZEne42e96lzMS+1F1n4ulUqlrW6TjdRyc7ONJxor0KzAnSrFRt4iAKCvBz3omrCVuuJJYgyI9QYGQM0lPDX6AHkV0SyQ8HyVVX6C4F2wWxliL5U+QFHZkyRwEaVzIYvZMEIVT3wlIJzG8ZCuOu9YPU9s4WNv/UN7cQSiaXF3dx84cOCOHTuaeiJNxouyguZwOC4uLiRJgu3+ugwGg0QiIYhntjwsdmkT4+qxMCPtoUZVd++nXCVVPX2tLZBHkqQNRUSv19tqNI1GwzAMu00/wgWYUCBpIAxw+lxlHwYDJa/iqcp5hNk9m6kzAKiLuAAg1lcxNHNoxkVXzKUrgrzFpCpEXpzu5hPZjc8VWt5xte1DQyCeH7RJ2DT8Iyt+JnUGgE8epwy5c/m+pp5xvXYLDweCW2P2OJKD50jr8GCh1QzGAF3VFsIz6IzqzCIlNUEdgGse54FA2C9IoJuGjXnp9bjqcGl+86un9hdbyJju6LR4HQLbAoAFI73Ikg+GQlj540+Ur/dX6opFVezCAoN58klxLL/F6zaYqvPh0An78/LyJk6cGB0dLRaL27dvv2jRoqbNTG1bkEA3DW94+Nb72qn3b5VTliMIHBeeGGKHwKufWihKRVSTbCWP0Fft56XSa7hVPsxYtc0Vl3e8rd5kfVGw84T9ZWVltRugi4uL27Rp8+eff/bp0+err74KDAxcuHBhr1692CgYJwAJdNMw0c9C7JaVFOh1b6Ve09HP6k7tAOSnWGjUSKpY+XUEliPlM9Wkttcs4MRwTLpVyVCBdXMHiVP5KdoEY8L+9PT0M2fOrF27Nj4+PiMjY9q0aefPn68e48cm7K8+DpuwvzFmXJVFixYVFxfv3Lnz119/nT9//uHDh7/55psrV66sX7++8SfTECCBbhre9A640qHXp8HN+rjVxx33ZFnheVmJzWfVZORq6V8zydUZuXss2OW1QdwicUU0c6mIe99LrOPgZcIq+9vi/jy+C7gOr7RyGNzFhtcCsFgJAGC9PLC3rd1ffXEwTdhvLKcCTxP2d+/enU3Yb3rJm2+++e+//2ZlZZk2sgntTJ2XG42EhITQ0NCRI0caW+bOnYvj+NmzZxt/Mg0BEugm4yUX9+URscfadJvgF8K2SGoISrbII41KYXCK33F3FPSX9+FKOeeOrGPuoyC5+RePVzPIc+En+UmS/CTZT9fOai7x2F2o8cKIGHAZwXcZxAMAbhDuu1LsMUPo+bHQ+1sxd6wX9lEE/r822PggEDnP8pmm1PlJK5+cnZh/Zw1Naeq+oAYcPWE/6685cOBA03chSRLDMNNceg4NEugmhofhW5p3UPcYss0vSvksgjv1wS3phcOfPrZkFHAomO05pofh5YWmhx3fhoieENiu4lAAtJ+CDJLrpFpKyScecMT8cYz4Va4x2xLGx3jNCW4YUUv2Z4eGoamHJ9/KvPRx0b0tmf/OeXjqLYaxsIlqDY6esB/DsBs3bmzYsMH0LXbs2GEwGDp37mz9c7BnXhQ/aDtHiBPZemv3WEQER/1UyldkPegq9Rju5d9gU2t4tFWM6fyq+5+J26H5axA7GGL6A1VIly2vWF55qPV5LrwiMY9xQlN8baiKE8szjxgPyzMOq4tvir07Pes4Tpmwf+PGjTNnznRzc5s1a5aVz8HOQStoeyHaagddddWF9sVqNgHHgvGoki1Txa3i9YwBZB013PqZfngWdIlVbtxfQbpFavAXbI1h0JkX36N09fk5X0vCflOOHDliduGoUaMwDDNW+64pYX+JCRpNpR3GLGH/0KFD2YT9LO+8845YLD558qSxf/WE/cHBwXv37jWb1f379wcOHPj++++7u7sfOnQoKCioHs/EDkECbS8IcTyUX58giqB6XWU/4B+GYk81GpNyeJ+EBT1NQM+lmfBSTVSJxi9VjR/TlKaae84FdJXBC4bYx/zHu6RaizU4TcJ+hmHWrFnTtm3bEydOvP3228nJyd27d6/HA7FPXrDlh13CABwvLZhelJ6hNy+BWid93bzf8wslGfqWUsbBsHYSV9zhjK+ePOy7GOyJGgAgQsTHseahkJ0IAOCn0BkzhbqQBkUxmFYGFXTiaPgvRCYZUzh8jxZD/8m9sUSWfcI1eEBgxwUEr55pUpwgYT/DMOPHj9+6dWtsbOzvv//eqdMzm3rsHCTQTQzJ0C2vnn6kfbawbyOHWr9crCffSb1+QV4CAN2knodad/HgOFquNQKDKHHlERcie8Cj8+CuMd81lYzgK/fpAEDQiSN9k+90ce9W4eLfo/mg488/jhMk7F+zZs3WrVvj4uJ+//13sVgMTgcycTQxMx8k1VudZwVFinBixoOkC0/N0P/KS967e8Oaa3Uy9bmT1y8cuaJ/Ulh370Ynshd0Hgf6gCoLCJ4PLnmV67dO4rdO4jZBgLs42m8FO8PRE/bTNL1mzRofH5+tW7c6pToDWkE3OdcV5ns+VtLfw+e78FgAOFhSJTfCmfLiq4qyyFqvzSwsnXA2IcGXAB68ei37z9xYv1fq8zfWoLiHgsskXvHiykV0wCRH+2Vg3zh6wv4HDx5kZmZGRUXNmDHDbOTY2NjZs2fXe2L2AxLoJiZKJE5U1kejB3v48XEcAAJ5whyycpdcaaC63Dj3pW/YYk/Pmq796vaNBN8Kz+HTvsTXd5N/tT+BBgCOL+77g5i8bwAceM05WD2LoyJqxKET9qenpwPAw4cPqxf5HjhwoHMI9IuSsJ+lpKQEADxrVq5nory83MXFpR75oE0p1Ot8//27ftfuj+0yzMt/3N3EPwuyqp9V9RgiqqFKFnZuv+nhG7mGI2PerGlzUaFQKBSKgADbhEqb5oN+fvLy8iQSia2SOJeUlNjqs4GwCShhP7JBNzE+XL6s++BvwlqMlTyzNAxPuXK6rMiiOgPARVlpTRe+Q3iYHvqLRQ7n+oFAvAgggW56pATnq9DmSz2DE2JfftZrZz1KqunUxLs3/izIsvj76JuOHY2v+6g4C1/u8qzvi0AgGgEk0HZEN6mHtsfQxA69XzVJcddO7DrY02+Ip9+m5u2qX5KiMk9LbySb1Iy7m/h91oPqpyKEYlWPIcdiu56IeenowNeDfCoX1BfLNSOScl6/lT3tXkGxvp5JHhCOhUMn7L9x48Ybb7wRFBQklUo7deq0YcMGtja0c4A2Ce0LPo53cHE71Lrrb3kZ99TK9i6u7/oGc7GK79E2YtcRKVdzdc+QwOzTxynzgptVbxfhxAAv8wS+jzT67omZxsMcHbW/TSAgnBeVSjV69OgjR464uLh06tRpxIgRBQUFCQkJx48f79Gjx7Fjx8x2C9iE/dW9JhouYX/tHa5evdqtWzcfH593331XKpUePHjwgw8+uHfvXk0p+hwOJND2iBAnZgRGVG9/ycU9o0v//967taUgo/pZq9Ab4FERnH8AOgoivOG1FsCt3Eg8Wlwl8ONAkbKINAiqjYFwGowJ+zdv3mxMCa1Wq+fNm7d+/foJEyYYc26wsAn7qws0m7C/oKCgkeb9lPnz5/N4vKtXr7LJN+bPn9+1a9cff/zx888/9/b2buTJNATIxOFgcDBsUfgzuMRNC3gq9DQD267CjJ2w5jQkZkJyLhy8Dd8cgesZwEApycxI0m98xAOtFxgq3Y056APivDhBwv7r16/37t3bmBoJx/GhQ4cyDJOcnNz4k2kI0N+f4xHMFx5uVdt2YhC/ctXbz/3pOiLhHpyvZo8uUsBvF+BE6sSb+nVPqGQZDpQINH7AcABgtK+LO8d58tw7E+WylIzMeJks9XkGcfSE/SRJLlu27OOPPzZ9F5lMBgCmweIODRJoh6StxLWWs9m6yqRLI1Ku6NmUyQ+LTLpUde7Yd/NAfpV9lVZ816/CPX9r4ff8U0XYnFu3vzh8pNX5C6MPHYm9nWQhLbKVOHrCfh6PN23atL59+wJAYWFhUlLS2rVr169f36dPn44mfkoODbJBOyT55DNsyHyZnvZIowoL0s25iwdq2Pz2dbg9Twt2mxqGPhv2iFxxPzllqfHwTvLiiIjxLpLaY/stjeNcCftHjx7N1iFs06aNxbrjDgpaQTsk0SKJ9Z1XZD74qyh3pUQZNFx4T2r5r2ikfxVTxmBfZNmwUxTy+2Ytcvm9eozjZAn7V6xYsXv37k8++eThw4fdunUrKioCp8DBBFqlUm3evHnWrFlvv/32woULL1682NQzahqkBOf/YurzuHMVIAAAIABJREFUIy5mkChHWO0fvVXA5vbcN/xp4GiBo37Fv7zEQNpglogGwNXV3JLg5tqqHuM4TcJ+ls6dO8fFxa1YseLXX3+9d+/eTz/9VI9nYoc40s9Yg8GwYMGChw8fvvLKK506dbp06dJ33303a9asOjPPOiXv+gb/kZ9xurz4WS/cHEksSK5ayK9vTC6pP6rIZvPhX1RAuytyQ9/mOIr/tj8kkvAO7VfcuDmPPezYYZVYHFK/oRw9Yf+jR49OnDgxYMAAo4UEAFijDbsD6QQ4kkBfuXLlwYMH77zzzujRowFg+PDhc+fO3bFjR9++fa2xfzkfMwIj6yHQRYJqzyot7x9X87pZDzVktAil97RHWrb4JCx0rFx+z9W1hVBY/yRWjp6wXyaTTZ8+ffny5Z9++qmxf2lpKQAEBwfX/u6OgiOZOE6ePMnhcNhc3QDg4uIyYMAAdve2aSfWVAzz8j/Ttvtk/9Ap/mErI639ndsvr1og7Mk0z2qfBE8uMkPbLyJRkJ/fq8+jzuD4CfvbtGnj7u6+ceNG1qmDZdWqVQDQp0+fekzJDnGkFXRaWlpMTIxAUOnk265duz/++CMtLa1t27ZNOLEmpI+bV5+niTve9gkamnz5WtUKAG4cTjlVmfN+6W1yWI65QDO06PWF6a+Ol55+6t70fqAbEminx9ET9vN4vJUrV06cOLFVq1ZxcXEikejs2bPnz59/8803zTxPHBeHWUGTJKlWq83S9bLhT3UG7L8g+PEE30VUWUeHC0Sm6iyi9P/4EIN6CdY14zImayMMY0QG2LNV/kOU9/Qg922x/j83N0/TgXBK2IT9P//8c2xs7LVr13755ZcrV64MGDAgOTn5vffee+mll2q6cOTIkRwOx2AwPH/C/ri4uISEhBUrVmRnZy9evHj//v1mCftPnDhRXFy8Zs2a+/fvT5069caNG8YM4O+9996RI0dCQ0P/+OOPtWvX6vX6DRs2mIWnOzQOk7C/sLBw8uTJgwYNev/9942NJEnGxcV17dr1s88+M+2sUCiePHli2iISidzc3FgHeD6fb5MpkSTJ5XJtZf5WKpUikchoa6s3h+Ul28ryaZpuI5auKbKcKhoA1l4npz+o2AJiDK6MIQQAyr/wpV3NF856vV6v19sqxT67NVR956d+qNVqLpdr0dmgHmi1WtPfZ7Xg5eVVdyfEc4MS9juSiaMmqmcXxDDM7I+Wy+XiOI5hGMMwzy+Cxndhx7TJaMYB63dtKSWX4EIezh3q5j3UzVuj0QiFwttaRUINNQ/3hHIeuOBKLgzJMQxLfxqX6MrBq7lusDdoq4eG47ht/wme56FVH82aoV7MHWlEk+AwAu3m5gYAprsBxsPqZYokEkmLFi2qD8KWvDLNC/M82KTklRG5XO7q6srjPbPjRIY2f2rqd8dKLgPAx6HvrIiehgFWUlLi4eHxGdYyIelfi1ed88bPeeMA8L8IzmoBzEwFGBfk4WXhySgUCpqmbfXQbFvySqfTicViG5a8stVtIhA2wWFs0DweTyQSsT40Rljrs7u7exNNqmlQKB8l3vz48pXJWVn7AOCjez+x6gwAP2Rs25Z3nH2te3Ky3cEh3z/+uc4B53Qu+TeoOdMdaRMCYV84jEADQExMzL1790iyMsiNdbBr3rx5002qsVEoHh44GJWWtvLho83nzo/cuh37qzDBtMM/ZbcAgC66XbKrP5l39e2CkxdvfjhRebOG8SqQtucTqGb2i4rdVlQpKyt7JgP0lClT2Dhyp8GRBPq1114jSfLYsWPsoVarPXXqlI+PT7t2FmpBOSuPHm8xPaxuDfXne9K0PvX6f86EQEII3PWAEF3e4jvftBPUaDyJM4S2fKOmkwhnRqVSDR48eMSIEadOnYqOjp4yZUrHjh0TExNHjRrVq1cv08yfLGxFlerjNFBFlWciISFh8+bNTTsHm+MwNmgAePnll6OiorZs2ZKXl+fn53f+/PmsrCz2i72pp9Z4kGS5AcOOCv3vcaU4ML20hf9VPNroUpnMbGrQiNS0H7LwimVOrgS4NESWw9bQkNb3nmYwYMRAi4AoAoAogfemjq1s5FWBcDAcvaKKEY1GM2XKFEfxSbMeR1pBEwSxePHiN954Izk5edeuXRKJZP78+U4TMmQlAQED/xIFx4tDbvPcbvLc10ibe9DkWoy/KHLKmuazS3of9+d7FRVX2RhUcoGQhrTyi1V0H3ysdbf3fFuBIRgYL6BaANViX2wnNw6ybjgeWqrGesFW4gQVVYwsWLAgNze3fjGN9owjCTQAiMXiKVOmrF27dvv27QsXLuzWrVtTz6ixCQocckhUJcD3Et+zq2fnBRETZ4WM9uBKAYBDVPGRwLlizzEnATAJwRng4fO/mKiUlyOWRHp9F+Wd+UpkK4ltvMIRjUZKwdHpB7GPj0qnH8TSCo/XexxHr6hi5Pr166tXr/7mm2/CwsKsvXkHwcEEGmGRoIAqJuTm0dNNf+m16ruD4xFt2qGlmPdFmOe8UI9gATJtOBgKXeEvVwYZD9dfHqgiS+o3lKNXVGHR6/WTJk1q166dMce/M4H+Ph2Pz8PeXZr+h/FwsCjEy6uraQcfnx7du54tKz/MMIaw0LGenp0bfY6IhiJLlmjWkll+vYXPgGcdx2kqqnz33XepqanXrl2zVUSCXYFW0I7Hoqj/Lot4rwfPvRvGW+USM+2VHQRhHqAslbbq0P77jh1WIXV2MiQ8H7MWF359Eqc4R0WVtLS0JUuWfPzxx87qyoUE2vHgYMT8yP/+0+voxX7n5ry8RShApV1fIILdOrTzr9yOax8wKtC1PqkcnaCiCk3TkyZNCgkJ+frrr+vxBBwCZOJAIBwJDLCJnXZey96eK78T5Nq2Y+AYrK4SwDXh6BVVtm3bdunSpU2bNuXk5LCnVCoVW2kFACIjn7mQrh2CBBqBcDBwjNMl+N3nH8fRK6rk5uYCQPXYxaioKA6HU9O3i2PxzAKdk5OTlZUVHh7u62vZ8mUwGNLT08FZvsEQCGdlypQpmzdvjo+PF4vFP/74ozHnFEmSK1assKaiSkhIiE0qqrRv355t3LRp09SpU3fu3GnM38BWVDEmGWYrqnz++ecAMHz4cNNqhACwZMmSlJSUHTt2OE/wGmMdNE1v2LDBx6dyg6Jnz54XLlyo3jM/P/+ZRm5MiouLi4uLbTVaWVkZRVG2Gu3Jkyc6nc5Wo9nwNuVyeU5Ojq1GU6vVKpXKVqPl5ubK5XJbjWbDh+YoZGdns9WIPDw8+vfvP2vWrNGjR4eEhPB4vP/973+xsbGTJ09me86ePRsA0tPT2UPW2YMgiCVLlrAtxcXFAGDsX50xY8a4ubmZtty/f9/d3V0oFP73v/9duHBh//79CYLo37+/Xq9nO7i5uQUGBvJ4vLi4uK+//nrw4MEAEBERUdM/+sCBAwUCwXM+E7vC2k3CiRMnTp06tbCwEMdxV1dXAPjnn3/69Omzbds2W35dOAUMY8jK3n/37pri4stNPRcEojYcvaKK82ONirOuNmwAqFKpZBgmLS3t9ddfBwAul3v9+nXTzi/4CpqmqbPnhm/dBux/SXcWWTkaWkHXA7SCdm7c3NzGjBnT1LNoSqxaQW/YsAEA1qxZM23aNLFYDAAxMTGHDh0aPHiwXq//z3/+4xz2eJuQm3csK3u/8fBW0td6vawJ54NAIBwXqwQ6KSmJy+WOHz/etJEgiN9//93T0/Pu3bvr1q1rmOk5HipVpukhBqBSZ+fmHTv7z/CEs4PT7q5mGH1m1t7Emx/fvfcTRSlrGgeBQCCsEujS0lJ3d/fq1Zg8PT2XL18OAN98800TZhq0K7w8zc12Wm3emYTXs7MP5OQeSbwx9/iJ7v+cfzMtbeX1xFln/xlOUeYpdxGIRqYREvZ/8MEHWK2wqZw/+eQTDMMGDhxYfYTjx49jGGaMRD99+nQto5kl4audjRs3tm3bls3W9O2337IO2ixjx45t2oJNVrnZNWvW7MaNG1lZWcHBwWanJk2a9H//938XLlwYPXr0qVOnbFWt2XHx8OjYof2KGzfnsYev9jmekVElwXlxyVXj6/z809n/z965x8WY/XH8+0wzTTPTZUoXKl2UQpJLcomSWxFq3UOLXNb+tFbLEkIk1q4l+vmhxbaEXSvrlutKSHLJpRAqKV2k+3SZ+zy/Px7GNDPVNE016bxf/pjnPOc5zzlP5jvn+Z7v+Xzzz1pZ+rVqFxGIT9TU1MyYMSMuLk5HR8fZ2fmrr74qKiq6cePGlStXhg8ffvnyZanskYRgv6wedKOC/ePGjZNMhR4eHt65c+cFCxaISyT3al+5ciUmJmbOnDlETrsGGDZs2IgRI2TLiUAGRQgKCoqIiHB2dl65cuWDBw9CQkLy8vL27dun4OUtjiKO6iVLlgDAqFGjKioqZM9mZ2cTgeVLly7FO/wiIQGPV1lZmS4UcnEcv5u8ULxmKPsv/WUEcQlaJFQCtEjYTIjANV9f39LSUnFhTU3N0qVLAWDatGniQiLMzsTExNXVVbadiRMnEhsjGgizk4RMJjs7O8uWr1y5EgBoNJqhoWFxcbHkKSKV0v79+4lDYqdiSEiIIrerj5cvXwLA2LFjxV/kKVOmYBiWnp5OHMqGBrYyCrk4Nm3aZGBgcP36dWtr62+//Xbv3r2Sv2xWVlZRUVEkEmnv3r0+Pj7ilFQdGQpFV1e3B4mkCQCWljMaqMmtff/gYWB6+q9CYU1r9Q6BAFBjwf7Q0NCSkpJWkA/97bffAGDHjh1iJbxly5bp6eklJCS09K0VRCEDbWxsfPbs2V69epWXl+/fvz8wMPDNmzeSFWbMmHH27FkGg3Hu3DnZnfUdnC6dR7sNP2VsNJxG6yJ1iiqEtPSfXr3em/J4Zfqrb5A/GqEI7zhFC19snfB45eIXP+Vzi5Vup5UF+xXHx8dn6tSpMTExV64on46A4PLlyyNHjjQ2NtbS0rK2tv7Pf/4jqcR0+vTpbt26OTo6ikvc3NzKy8sJn4EYFosVEBBgY2Ojq6vr5uaWlFQnY1GLouhGlWHDhj19+vT48eOrVq3y8fEhgu0kmTBhwqtXr0JDQ+3s7DqgJ1pQ8rws1qfgJ6z05HhuTp0023x+VXXVmw/Ft9nsQgAQr7zoc4ErIWBbUXHn/Xv08oFoBLaIO//5lkP55+NK7vyWf3beszCOiNf4ZfJofcF+xYmMjGQymUuWLKmpUf7NMiEhYcKECbm5uUuWLFm3bl2vXr32798v/iHBcTw/P1/c7fLycqFQKNsIl8sdNmwYi8VavXr10qVLHz58OH78+NJSJZMkNJUmWFIymezn5+fnV++KlpmZ2caNGzdu3CgUCqVegr5scCHvw8He2VStUoZOr7dXuG8umSzN09Ax4/HKk+8tyn0XK1lZCJBHhfcUYGqBsQAMPq8YA4dbQnwQccr471NIjM4UI0dAICS4X/nietlD8eG/ZQ8estKHMZusONomgv2K07lz519++WXRokUbNmz49ddf66sWHx8vGXRBEBgYSPyuHD16lEQi3blzRywc5O/vf/z4cTabTaPRSktLeTyekZHR7t27d+3alZOTQ6VS3d3dIyIievbsKW6NzWZ7eHjs3r2bOGQymcHBwffv3yd26rU0LTLV1dDQ+PKSgzUAt/j591Y2pzoZEYdnXj0f++4WrZff4ydrpKwzAORoQTEZAKCCDBVkcKgFHdHHUyZGwwXlGfy8O+Vx84kSmsMc/Ql/AIZkuxEfEeEimRJlUlk3INgvWbJw4UIpAz1t2rQffvjh5MmTq1evhvoF+yWXqeh0OiHS3yQWLFgQExOze/duPz8/Z2dnuXWSkpJkHQ5Tp04lDPSOHTu2b98ujh7BcZzFYolEIh6PR6PRiB7GxsbGxcUFBgZ269bt+fPne/bsGTp0aFpamrm5ubhBYumSoH///vDp6bUCKvjmL1q0SCxG1TE5UZMqts4A4GvvICRTACAj84BUTRw+Wmcx5ZSPTg9r/cnCq8tTY+yS787P1gOeBgAA+3kMO/3PFu08on3holdHGn+UgfNAvZ71VW6ANhHsbxIYhkVFRVEolIULF8pOkwnkRnEMGDCAOKuvr8/n8//5559NmzZNnjzZxMTk3Llz4ms7deoEAAKB4O7duyEhIbNmzQoPDz916lRFRcX27dvF1Wg0mmR4sVj4tHVQwc0yMzOfPHnS/HbaL8+E0pu5T6bMeJN9VJFrMRwwACsWWD89nV6V8KIT5GtDth4kmoGABADA/5Cm8g4j2i8MDVqG699zungBwFzT8VG9gmkkJfOyOzg4ZGZm1reno2HB/sePH2dkZFRUVFy7dk12eZAQ7BejtKCSnZ1dSEjI06dPd+zYocTlx44ds7Ozmz9/flpa2vDhw0+dOhUYGCg+a2BgoKmp6erqKk7yAgCenp46OjopKZ8TP1KpbZn2Hr07K49QWMPjlQNAd7r0/h1NXPQi/WfZSzCAXtp13IXjzL93LYBuFSDCILeuRFcRHQBAWPGG/146TyiiI2NLNz/aeyM+5m60w/puNLPGL6iHGTNmCASCVatWyT3bsGC/hobGX3/9Jde/AZ8E+8VYWFgo3clVq1Y5Ojpu2rQpIyOjSRcKBILAwMChQ4eWlJScOnUqKCjIzc1NJKrjIHJ0dGSxWFJX8fl8yaDDtgUZaEURCWpYBQkZV3xfX5qQm/xjUpL/pSvmsf8YJdzy9es02NvQVVxzeeVrAMBFclaEHXuv/8pksQt0NeGBDanziiHXbfi6VAEAgFAmcxGfBADAfnmyONq59slvLTc0RMdk0aJFffv2PXnyZEBAQFVVlbicx+Nt2bJFEcH+kydPKi3YryAUCuXgwYM8Hm/9+vVNujAvL6+iosLe3l7sZ8/Nzf3nn38AQGymFy9efO/evatXr4qvioyM5HA4cncntgkdLh5OCbhVb3MSAytyP6c3LnsfV/LpvScv76wW1fCcS9TumxMzyu734LEMRDwAoJFosip2FD7v8aOlJE2wBgDe+xe35rrZbfp4SgSd2FAqsZRiwPn8ueLyYnpf+SoHCIRyUKnUCxcueHt7//7772fPnnV2du7Zs+f79+/v3r37/v37Q4cONRA+MWPGjG+++SY9PX3Tpk0t3U8XF5fAwMA9e/Y06SpLS0t7e/sDBw4UFRU5ODi8ffs2Nja2W7duhYWF3333XVhYmLW19ezZs3fu3Ont7T1jxozu3bunpKScP39+4MCB33//fQuNpamoYAZtZGQkueL55ZGbtLw8t07yebZE/DJLA67nHbqasXUkpj+6poSwzgwh0PMe6crEp1YWJZZ9kpzCAcq4ebe5dxLsuz0wAB4J7MrBhGJFnHWo7SR1OS5gAwKhUtpWsF9xwsPDm+onwTAsLi7O29s7Pj4+MjIyPz8/NjY2MTFx9OjR8fHxRCAzg8G4devWN998k5aW9ssvv+Tm5oaGht66dUvu6mibgOFKxei0O/h8fm1tLREcI6X/0jCpr0KzsyJIODAEYMIFsggAoEgLKikAAEUUyJZYQpj8Acg4AAZaQijThBKZ1YWehr7pJZ/Vot9S4f2n/wmW5O7+PaJpnRwAcM55X2HeTckLNSzGaHnX2b7VMLW1tU0aZgNwuVwulyuZx7M5ENmMZJURlaOqqkpTU1NVyziKPzTFtXgQiObQUVwcZDKZwWDweDwAkN0GKZcPNa+PPfLLqUoFBnThgwUACaALGwBAn/fRQGfXtQyZdOj7yZX3gME4RzfjYyQGzh3Cz9PG+Y56w616LE+/+dFAc0nYC00tGghoOB8AcgQZ2VjmQIaLqPZDjYR1xgHIzG7MUTs0FOs2AY/HU3CYisDn8yVb4+PCa+VvKgUcdz0rU2rTkg9xOBwcx5WIipVLTU0NlUpV1UhV+9AQiOajvIEuLS3NyspqIJeKq6trfadaHwzDyGQyEcPY6E70qveJNcUPY0uP51SlEiWFFGAIAQA6A1C1u5raze3XY+7LjBPJeRvqXKhnDFUfeCR4T9bcoP95E2A+mTqB9/JGze0PiSMIe5ZNZogrOAreu/HfAkCtoIRMJuPaRpJtYgA6Q9dRTfo0abwkEklVG+5JJBLx9IjDbE653/O/71XlEYdX+nw91sC2/qul0dDQwHFcVX3DMEy1I+2AKgUIdUYZH3RFRcXkyZMNDQ0HDRo0rH5U3tfWIe/+uvSzw3OSgl6xHkiWV2kABoABmA8MMx8YxtCxten2XS8Dd8k65JoPVXZj3jLgsh5TsjxHg1mB0TI1Ot2gG1aTyADwp/Znh1oauXOmRicAsDFwAwCMRNEZvll8lmo1itazIT281iQsJ6Fb8i6xdQaAX/NaTzgGoVoOHDiAYdgff/whe2rv3r0YhslNCT1v3jxZvYeG5fP37t0rWfnMmTNjxoyxtLSk0+k9e/acMWPGw4cPQQaRSBQdHT1q1CgTExMGg9G7d+8FCxZkZ2c3Y8TtD2XmC+vXryeiVSwtLS0tLVt5a02LIuCUFDzeCiAd8QYAmiIAAB3baYbd/QFAIKj58OFKNwGtUAAlZIyFadlxeZ0EwsLCa3IvP6b1Mfz5by0ILX/2glLHiVlE0u5MH7ToabgGBm5d3FYPXadp7srLTdDQMac5zMEoqvEmN5OHVfkbsuOlCq+WZeKAy4QIIr5McnJyTp8+7e3tLfdsffL5khu1FyxYcPjw4e7du0+ePNnAwIAIrjh58uRvv/22cOFCcbXKyspJkybdunXLzMzM3d3dyMjo5cuXv//+e0xMzOXLlz08PFQ9MjVFGQN94cIFADhw4MDixYtV3Z82hlfzeW7ozIKHEgtjVRogEADZchRgpOqat2fOftx9JNDU20/rCQCgBdNr3k2szS8nQyesTiYIHZxbhX12Vx/W6SZ13xIS/Q9BGfH5yru/hOzK9f1CqZYjVTi0ZiLERdHvH8uWu+tZI+v8xSMSic6fP5+SknLw4EHJiGkpRowYERYW1kA7cXFxhw8fnjZt2okTJ8QSzDt27HB1df3uu+/Eqv8AsHDhwlu3bi1fvlxSrPnevXujR4+eMWPGq1ev2jYTVauhzOS3oKDAxsbmy7POAKClZy/+XFh3AbCCDLlUwDAyAKSmhRKFQsB+klBCOMnoepem/UoLqjX4fpyndsKSXvyK4bwCpogj2VQuWXpGnEeSnFBjie8Tmj8WFSIA0VfPTuzNvy97qpBf79cV8cXAZrN9fX3DwsLkJiRUHEIIf9WqVWKbCwD6+vorV67kcDj373/8D/bw4cNTp05NnTp1165dkjUHDRoUEhJSXFx8+vTp5nSjHaHMDFpKIvZLgkSm2Y2Le33Ju0qDdJlhVIlpGYtqugtLiCniBzKYm00AAC7no0p6sYZ0gFcqVdtJUA0ABjh7DC8TALrwIIli+Y7ehMAsYY16ZeA9z8o6X/pK7qnXtSUsAVeX3JZ6BR0NEY7Hlrx4UfPBkWHylVHPVniDYTAYHA4HADgcDpPJbLR+fRDizrm5uVLqdDNnznR1dRVPn3/++WcA2LBhg2wLRD4Qcc0vHmUM9Lx58zZu3Hjz5k13d/fGa7crBCIuzdQNJhxb++b4fQ6hyY3nCvRG87MAwETDoKb2HZXCJMPHX3V9Ga10HZmSQk3wrM3LJ9Oeatb7P9taUJNN/hzg1VvHOObx/EpuAV/I0SLrWDCdPbuv0yC1WfD8uwanySHZ15/VFDE0KAu6DPA1VEZZDaE4OOBz0k+d+CShNcfE6WhPleWaagCVBJv7+vru27dvzpw5s2fP/uabbwYMGEDsJqfT6fb2n19e09LSjIyMJBOdiDExMSFkTjsIyhhoQl9qypQpv/zyi4+Pj/oIizSHGl7pn6lLHhecAoB3JL37VLGhwV6RjQYJ8nRwrnZN2aXLA/tru2kU3aJrQa0GlJNFgwTv7pE/iiVZC8utRHLyEL+mC4cJX9K43ZKpxnLvvrAqq5ik9YRkRBaBvZaWQJSc/O6W+OyzojghLpjYY4vca1sBBy3DBs5G5icTHy6Uvo7vO9+Dad1AZUQzeVL9/oSEwGFM0dNVFsMcGcrPKP/++28id6okknJuiiNXPh8AVq5cSWh7jh079tChQ+vWrTt48ODBgwc7derk7u7u4eHx1Vdfid/LRSLRmzdvOriCsRhlDDSJROrdu/fp06cDAgIAgEqlyhVVYbPb09bkMy9WEdYZANiY9FyVjVF0cG4nIQBA8YdbNBGYs6FEE5K1wZmf31VY+Z6koyPiWovKMah3Z2YfPAcnW96T2bH9a9Hz3pxak9pao9oyqvXojaJ/Za/Nr2xLQddkdoGCNY++f4IMdItSyJV+m8nnsppjoOPi4uLi4hqvpwBy5fMBYOHChYSBBoCAgIC5c+feuXPnxo0bCQkJFy9ePH369PLly5csWbJr1y4KhcLn8/l8ftuKfKoPyiwSRkREbN78OVCXy+Vy5KG6TrY4OC68m3tYfNhFJP0d6CSqteACCQeAT94NHKoxEGFYKrnzXYpFIUmHCewGrDMAUHDhD2Vvfq7NGyLIFRcu4ZBn5lXVcHS2GNt+2637RUPpxLKf7taWO/L/Vybv50Fej8oE7env3h7pr2MKUOfhDyBKlCU6OlpW8/6///2vEk3Jlc/HcdzGxkaymoaGhpub28aNG2/cuFFcXBwZGWlpabl3796ffvoJAKhUqoWFRVZWVn13SUtLe/TokRLda48oY6CJsPNZs2a9fPmytrZWrnVuXwY6IzOKKfFmpoNzvXgfM0048CqXVj/vWysy/bRlkvJJUZYhgpsU69sUq3ySbpaGwQmqUxHpox9ZX37+B9ArzpmVnvdrZsHx8tK9b18lPH+6/nni3U7mk+0dTnUyPKff6WtRRpFIjhtEq7aUxytTxVhVh7ylqcnIB93CdNbUPuUwU/zw/+ntZ0RpH9vTBQLB9OnTpRTytLW1AwMD7969q6mpefLkSaLQ3t7Sb/OvAAAgAElEQVS+oKAgNzdXthEWi9W/f3/i3b0joIyBzs3NNTQ0jI6Otre3p9Fo1HpQeV9bjpKSZOO6JtVGWLaUnby6+v7iqvRB7CqGhMx3KQWIIwM+9kKjjjG9RekGAH2qgS5HCxosa8GOBRiAZQ24v8nwLS3vzmEDwHFdLclq8bSulpyP3z+GEPQF0I0L5MJ7Dx62mQTiAn1Fc9dGv3/8hi3HC49QIVOMegncQ/OH/Ch039SOVmXJZPKTJ0/2798vpZoPAEZGRnQ6XRxRt3jxYhzH5QpAx8bGCgSCUaNGtXh31QNlDHSnTp26dOmiPop8zYdO72ogAIqQcotidUGzxzVN2zISDQCqNUSv6ZBX97emjAqF2hQcQCgzh/xAYsx8D32rQL+uQokxH/qxYMo7oEr/zwQA4NbNCVtGolQJDL8uBK9ycGSDPQeM+YABZL+NwXF5hr/lWWc4eGXXxvbu4wAANyqyQ7Ll+NARqkUDI5lSdUgqTaTdCkycODEzM3PFihWEbJmYvXv3VlRUjBz5cWfWlClTXF1djx49unbtWslVx2fPnq1atYrBYCxZsqRV+912KLNI6Ovre/DgwYKCAlPTZjm/1IHqoruFT35m55zBteFfqs0z8sdIuNcahgHsFBrwAUAg42ytwfi1GsAQikwF3AKJEOCB7EpNHADAqRr4JHhFBwDoVQ0DqkCbC1r1WNfhrMo4/TqRMG8pNADQkLkvhmlIF7UKVExjilHPHe8S5ZzDP/k6PtmKEx/SjvdqDY1ghLqRkJAgd9prYWGxaNEiANi6dWtSUlJERERsbOywYcPMzMwqKyvv3buXmprar18/yV2Ix44d8/T03LZt2/HjxwcNGmRqapqVlXXp0iUMww4dOtS9e/fWG1WbooyBDg8Pj4+Pnzx58okTJyTzLbY7WPk3Xl4YCQAaAHQu9RmlTpzyOw09O2EJAJBx6FoL7+ru/hNhAADbil7NNfusM7e87GP+TQ0cBlXCoE8pVbS5wKw/Tbt/SVGwZZ3H2M+gLw0z0Cl/Uiohm9zT9j9NHaAK6Ur9tNEGr+t9lpnDzTRW1B+C+MJITExMTJTzKz5s2DDCQFOp1MTExJiYmOjo6Dt37hQVFXXp0sXa2vrQoUNz5syRVAm3tLR88uTJvn37Tp48efXqVR6PZ2Nj4+fnt379+o5jnUE5wf41a9aUlZUdPHiQTCY7Ojp27txZbpjd+fPnVdFDVUKkUSAifnBcmHbSgVPxcYNcPoU6uWud0MsxvEzCQA+shB5syNCu01S3aiDjAADVJI27dKYQsCHsCj2h/MVBs/KGNntVUyDaQncb82Pieg928dbCLMLrwdOACjIIMWAIYeRcDklm42IDIxUHNjWTqqqqqqoqU1PTqc//ii1+3mj9jEHLbWn1hsaz2Wwcx1WVTKCwsFBbW1tHp2ma1PWhwoeGQKgEZWbQRDQMAPB4POUC2tUBbmWG2DoDgCmfO5RdkUT7PIkeUlupjUNXLljXAgZgUQvVRrZlNZl61M7a5e/Jn37XtEXCMdWlUo2T8I9TbADQ0TTHIA/qp4QOvfisqJIHOWSGrohvKmSLsI8hfZpCMBYCAHSqBrzmA+hKpw9vTWJ6To4vf1POZ3/8tcHlB3LMfXl6JNN6veUITVLbOGQQiC8G5dXs2jskSp1pFwaw4UPWb/rmZUCl44LZVQU2vDorfVpCsDKeZDHkVyG3LCW6kXmWSMJyVfHyTBy/5qQdwcg02byCbArUUAEAaLiwB/9jBnjSJ+uvXwtkIWiKABOBoOSZRpsa6KfVReUC9mejXM9LQVJlblJlLh8X/dRtTGt1DYH4MlHGQNenBtu+0GSYaTHtJSfR+kL+qpKG5MCZlhMAoLYsrYE6ctEaHKQ/7reKC/PYL05A3aknSwu0BSDWRsIBSDjUkEFHAACgxfu8VEjS7tzU+6qWJkUMbM+9jQw0AtFMvhytfSWwHBrRpPq1JU8AAGu6aNGzU/0e/EbNyz0hJAHUtXR8MmjgYFMNBjzQEEEWFe7qwD9GcFMfmDWfrTOlizPFqGlZr1ROX+0uo/VtGq8HAADWWh1Crrddo3hGle3bt2MYFhsb23CDUVFRTk5OdDq9R48e4eHhUrocFRUVP/zwQ9++fQ0MDMaNG/f3301IgtxhUXQGTchjBwcHr169WsFE6+r/B9Dr6mU5NCInaXl97lQpqgpvde4TxDAcoGs2mpXfULSvtsnQ6iJpUQI2BTA6GFTDKy1aCYXSu7ZWTyigCoGrARo4GPAgjQ4ln4x/jhZkD1/k/CaHm31Vy85X130rtFGMnRhNkkZ0j682vb2RxS7L5lRkc8q70zplsKX97wTZnPKXtSU96A2pLCG+JIKCgiIiIpydnVeuXPngwYOQkJC8vLx9+/YRZ/l8/tixYx8+fDht2jRvb+/Tp09Pnz79999/J+RDEfWhqIGuqKgAAGID96lTp1qwR62LieP3uuajCx5tEXAruZWvOKw3H0/IM9lkLQMAwDSoNiOPFjzeWpL5l5DzQXxWiGH5ZCpTKNAVCYx7LtIF7Yp3V3ESsCWecTUFNlvZ/N3JkGj9SObLgdyKKgYUa0GNBryrs6MQHn0478wYzRz3G73P/Da3zgRmVN0oex/i8z8l6ZOfnfh4Qt7jymSXIgPdQXj16lVERMTYsWMvXrxIbAicOnXqgQMHvv/++x49egDAuXPnHjx4EBYWFhISAgArVqxwdnYODQ2dO3eu3BgwBIGiBnr58uUAMHjwYAA4c+ZMC/ao1aHpO9iMOgEANUX3n58Z9LFU3v+Zzo5BxAcKvbOl6x6GTWDli7DSjBgKzTiDpv+zlkaKli4AzMMZq7P+YhVdBU3pFm4wOv3d6XPS7q9te9x7k/z2k5QCUwAVEn+QfOH7E6wY32cxvMIHTK8DqhirKrlY+lGupL73D6e2dpp/wZTweZnsaju6tgFZ5j9ZW/Dbb78BgGR6qmXLll2/fj0hIYEw0IcOHdLU1CTMCAAYGBgsWrRo7dq18fHxHWffthIoaqB37dol/uzj49MynWljqBqMLhXwQQeEdWerBtaTaZ2cDO2+pupYSZaTtYyt3KNtRh4FgDWPlqeU3iPKo7GaHuWv5Wa1zCfRpEqKNSgAH8NFTPl1DDQAvNSFSk0gPYnSG7kD01RNtK+q+JxFRZ51ntjJXtT0EHuEIuwteBOYkUp83m/X95suVm3aHQCA06dPd+vWTVJi383Nrbz8sypLUlLSkCFDtLU/7yYYM2bM2rVr79y5gwx0A3ToRUIpNJjdNHDQk5Hh69znB7MBG6SssyRsEffKJ+tMkEaVY0wpArCrlW7dQMgXmzFdIVhzpa/ikAAARDy1S/0X0Ll/A2fPl76ySt459+XpH7OuPKv50EBNRJPI4dSKrTMALHn9JI/bxsLrOI7n5+c7ODgQh+Xl5UJhHVkDDodTWVkplSqPEIp4//59q/WzPdLkMLvExMSTJ0/ev3//zZs35eXlGIYxmUxbW1sXF5cZM2YMGTKkJXrZOmBkmv6EI6K4ryULdc1G0Y0GNHwhjSS9wa+zQMbQAvDJ4CIs9S7Xj9P/GEb904fXGgAGPCj/9J5qxoPsuo0Z8YBqPVZDW+1kTxwYxs8HfrcnP/li2at3HJbcOkfePwGAHe/u3O2/aHCbBnF/MaTVSD/q1BqWOVX6zUxxmp9RpbS0lMfjGRkZ7d69e9euXTk5OVQq1d3dPSIiomfPnvDJCkvl4SYyMRUVqVf6TXWjCQb66dOnixYtevDggVR5cXFxcXHx3bt3d+/ePWTIkKioqN69e6u0k60Hrbe/qfUY3ewLRbn/lL+72Kn7HHPnUJKGVqMX/u4QMv/5x5RUow0Gzubr8ivlTA1IgO/Oy1jpsKJEvyv94leGQh4AGHGBLgS2BpBxEOIAdSffvO6eph57VTC2FqAXw2i/3USAiWk1RbVCPgbYoEfyfeWR+feQgVYJNjRp9WdbmZIm0fyMKoQrIzY2Ni4uLjAwsFu3bs+fP9+zZ8/QoUPT0tLMzc3lXkWITMhNkYUQo6iBTkhI8Pb2rq2tBYChQ4eOGTPG3Ny8a9euGIa9e/fu3bt3V65cSU5Ovnv37pAhQy5dujRsWGPqlPIoLy8/evToixcvSktLzczMBg0aNHXq1FbWNSUxOuv2Xqjbe2GTrppn6j1At0d82UMTTYOvjEdQSZTMf2eUZZ2UrallOWqEw2IArMQt6s2NeURh166T2XxNHU3qh06mkLdNsr5w2EoyU9Ho47aCSLmUz/00uZNZNqzgcwS4iIwhl1pz6UnXWW5uE5H3MeHIyq62djTthi9pmOjo6Llz50oV7t27NzAwUMEWCAETgUBw9+5dsXqam5ubl5fX9u3bIyMjiSTcki5p8eEXoIjZoihkoMvKyqZPn15bWzto0KCdO3cOHTpUtk5oaGhiYmJQUNDDhw+nTp368uXLpqZnZ7FY3333XU1NzejRo01MTF68eHHixImUlJTt27cTS8M4jk+bNk1KSRYAoqKiOndu+4ABR20bR+3PltTa/aCsgdYxcunic4GwXoZ2c5kW49nlL6i6NpoM87dv35qamuoICqQMtKluG+9PURwzqu4wPYvEytzP1vmTpb5Y9ppyM3Sj1YjVJu3YCaYm7LJx9DM2f1FT1Zuh66zTtG9ZS2BgYKCpqenq6iqpbenp6amjo0O4Smg0mq6ubmFhoeRVhN+jSxf5Od4QBAoZ6MjIyOLi4l69el2/fp3BqPd9atiwYfHx8QMGDMjIyIiMjJSrDNsAJ06cYLFYa9asETuy//rrr2PHjl28eHHixIkAUFxczOPx7Ozsunat87JMoynvgGs5NCg6fefkvX/6C7vyNeAiDU09ukFvk97fSTpMyFpGOl3cJa/qRLea2Wf/n6kf9cjn9j+qU08icPVkXud+iZUSmYrqzqM3vU2wIutON+jVyr368nDR0XfRUaO9mo6OjixWHee4QCDg8/mEoxkAhg4dmpCQwGazxd/W+Ph4+BS5i6gPhQw0Efi8evXqBqwzgY6OzqpVqxYtWnT27NmmGui0tDRjY2PJZUYfH5/jx4+npaURBjovLw8Apk6d2l7+qJoMM4sm7iYHgGFW3zib+5XWvjWkd6OSm/X22vo8rJLJ/13X3RH5/j4y0F8eixcvXrJkydWrV8eOHUuUREZGcjicESNGEIcLFiy4fPnygQMHiFDompqaw4cPW1lZjR49uq363C5QyEBnZ2cDgIuLiyKVBw4cCABv3rxptKYkOI6TyeT+/etEbgkEAgzDqquricP8/HwAsLKywnGcxWLp6enJaeiLQIusa9Z+PBtiduUl7S+QXkOWmkTXCvnSFRDthBMnTjx5Ip3ffdGiRRYWFrNnz965c6e3t/eMGTO6d++ekpJy/vz5gQMHfv/9x0SaPj4+zs7OP/74Y2Zmpo2NzZ9//pmenn7kyBESCS1LNIRCBrqyspJMJtvZ2SlS2cHBgUQiSS0INAqGYRER0pPNW7duiUQicQKFvLw8DMNOnjx58+ZNPp/PYDAGDRoUEBCgq6vbpHshWoJ7rLwfMi83Wm20nroveCLqQ65YkpeXl4WFBYPBuHXr1ubNm2/fvn3mzBlbW9vQ0NDVq1eLV/gpFMq1a9dCQ0Pj4+OPHz/u4uJy6tSpKVOmtO4I2h8KZVTBMExLS4vNVjQenkKhCAQCJXK1SHLlypV9+/bRaLTIyEhDQ0MACAkJSU1NtbKyGjlypLa2dmpqakJCQpcuXSIjIyWT5QBAaWnp06dPJUuYTKaRkRGRTlhVP9oikUiFv/9CoZBEIqlKl0AoFIo33TYTkUiE43ijrR2qfLap5G7DdVzppgeMR+uQNFU4TAzDVPVXUPChYRhWX+gYAqFalNGDbj44josdFwBApVIlLWx+fv5vv/326NEjXV3dtWvXEtYZABwcHOzs7KZPn66lpQUAo0ePNjc3j4mJOXv2rJTAnra2NhEhL4ZCodBotKqqKgBQVYakmpoaGo2mKutQXFysp6dHJqvmL1JVVaWqYbLZbA6HI7XLQBYbMIaShioM0TG/ZD9LwOfjOE6lKpq7q2HKy8u1tLRUtUqswoeGQKiEtjHQZWVl8+fPFx8GBAT4+voCAI7j58+fP3LkCJ/Pd3d3X7BggWSsnp+fn1Q7Pj4+x44dS09PlyqnUqlyw3cINb5GlzoVhM/n0+l0VU1Ui4uLaTSa1KuA0nA4HFUNUyQS8Xi8RlubTnM6Xv7icllGfRX0Nek62tqqzUnIYrGoVKqqRqrCh4ZAqARFDbRQKJQr7C0XwpPQALq6upIp1olgdRzHIyIibty4YWFhsXz5cltb20ZvRKVSdXV1peJ7EG0ClUQ+23tWTNGTBa/Oyq0wBO0kVDPCw8NDQkIuXLgglSOJ2JWmra1948aNXr0UDbmJiorau3dvRkaGhYWFv7//6tWrJV8HCwsL161bl5iYmJ+fb2dn5+vrGxwc3OiLlEgkOnLkyNGjR589e1ZdXW1tbT1o0KCQkBDJgOu4uLgJEyZIXkWn021tbWfOnLl8+XLJtysFx9uzZ8+XL1++fPnS3t5ewbG3HIoaaD6fr0JpbQqF4uTkJFV47ty5GzduuLq6fv/994QTQ5KCgoKoqKgRI0aIA3cAoLa2lsViOTs7q6pjiOagSdII6DJA1kAbaTLWWrh9ZzZI7lUIteL27dsTJkyQtM44jjMYDNklKCIeg/jcsFp/SUlJnz59Kisr58+fb21tnZiYGBoaeunSpcTExAZ8epWVlZMmTbp165aZmZm7u7uRkdHLly9///33mJiYy5cve3jU0Yt0c3Nzc3MDAJFI9ObNm+Tk5LVr1/7555/Xr18X+0gVHK+Ojg6TyVST8BKFDLQik9lmguP4uXPn9PT0goKC5L7md+nSJScn5+jRowMGDBA7Cv/8808cx9u1QtMXhlyJ0R/Mhy43R3+jdkBSUtL48eMZDIbk3Dk3N5fNZru4uEjNpsVfw0bV+jdt2lRSUhIbGzt58mTikrCwsA0bNuzdu1cchyfLwoULb926tXz5ckmZ6Xv37o0ePXrGjBmvXr2SXBcZOXLkxo0bxYcCgWDFihV79uxZs2YNoVWt+Hjv37+v6PNqeRQy0BkZ9ToWVUVBQUFxcXGXLl2ioqKkTllYWEyaNAnDsCVLlmzdunXZsmVubm6amprPnj17/vy5h4eHggHaiFaAhGGTDHucK/msjuZp0H1lV9c27BJCQZKTk728vOh0upRng9C6W7NmDbFQJEujav03btywtLQUW2cA+OGHH0JDQxMSEuoz0A8fPjx16tTUqVMllegBgHBxBAcHnz59esGCBfWNhUwm7969Ozk5OTo6Ojg4WDzTV2S8akXbLBLKQqgOFhYWSm3YB4D+/ftPmjQJAAYNGrR58+ZTp07Fx8fz+XxLS8tly5ahnUjqxi/dPGuF/H/LswDgO7PBu7uPw5qWEBzRCA9YnM3ZpRdKqicaam/s1mmATuNqi423+eCBl5cXjUaTtVavXr0CAEdHR5FIVFpaamRkJHVtw2r9OI5ramp6eXlJXsLj8TAMa2C3xM8//wwAGzZskD1F+FoJAaaGCQoK8vPzO3ny5Jo1a6RONTBePz+/y5cvN3UnRwuhLga6f//+586da7Sak5OTrPMaoVbY0Ttdc5qbx2Xpkak6GqoJp0OIqRCIXB7kEJ/Pl1SfL6mudO+uS26Ww/TRo0djx46tqam5c+eO7Fzy5cuXJBIpPDz8+PHjXC6XyWT6+Pjs2LGD8O0Sav2enp5E5fLycl1dXcnQJgzDHj16JNXmiRMnhEIhsetYLmlpaUZGRpJGX4yJicnq1asVGVffvn1B3q7mhserVqiFIxzx5WFO1UXWuSW4z5JerHvAkkkC1BQeP348ZswYNpstEAhu3LghW+HVq1cikSglJWXr1q2HDx/29vb+448/hgwZQiwbSqr1W1lZGRgYMBgMT09P2eBXMVFRUcuWLWMymfX5N4iFvm7dujVnXABAqKplZWVJFjY6XrUCGWgEoj2hoyH9ndVp3vR5w4YNZmZmz58/t7CwWLVqlWx2FTc3t+Dg4KSkpB9++GH+/PkxMTFbtmzJzMwkvMNitf5t27YtXLjw2LFjK1asSEpKGjp0KKFuJsnr16+9vLy++eYbfX398+fPExsyCc+JGDabzefz+Xy+qnYzSW1bbXS8agUy0AhEe8JZV8uz0+fdNOM6MfrrNMuQ9e7d+/r16zY2NtHR0RwOZ86cOXx+HUGrjRs3btu2TXILT1BQEIZhSUlJUFetPyQkZNasWeHh4adOnaqoqNi+fbv4EmKXg5OT09WrV2fNmvXs2TNxTo/CwkJDCf73v/9RqVQLCwupma8kaWlpsm4TWd69ewcAUiuEjY5XrUAGGoFoT1Aw7K/eppu7GU430dliY/hnb1Ny84RNwsPDiXU/Dw+P7777LiUlRXITmVzodLqhoWFxcTEooNYPADiOz507NygoyMbG5v79+8eOHZNc4jM0NPxXAkK2wd7evqCgIDc3F2RgsVj9+/cPCAhodGiEII+Uq0SJ8bYhyEAjEO0MPTJpvXWnv3qbrrPq1MzlQairHfbTTz/Z29tv3bo1OTmZKMnIyBg3blxMTIzkJSwWq6SkRLy81qhaf0RExNGjR6dOnXrv3j3ZbWVUKnWUBBYWFgCwePFiHMflasrHxsYKBIJRo0Y1OrRdu3aRyeTp06crPl51AxloBALxERqNduTIEQDw9/evqakBABsbm7S0tHXr1pWWloqrbdq0CcdxcVzz4sWL7927d/XqVXEFSbV+kUgUERFhbGx89OhRxaVOpkyZ4urqevTo0bVr10omln327NmqVasYDMaSJUsauFwgECxfvvzevXvz589vYLFRdrzqhrqE2SEQCHXAxcVlzZo1W7ZsWbFixf79+0kk0t69e7/66isnJyc/Pz8tLa2bN2/evn3b39+fyHMEAA2r9WdkZOTm5tra2n733XdS93JwcCASrMjl2LFjnp6e27ZtO378+KBBg0xNTbOysi5duoRh2KFDh8Qy8QTx8fGEHReJRNnZ2cnJydnZ2Y6Ojlu3bm3SeJV4Yi0L3pEoKSkpKSlRVWvl5eWE7LVKyM7O5nK5qmpNhcNksVj5+fmqaq22trampkZVrRUUFLBYLFW1psKH1i7YsmULAFy4cEGqnMfj9evXT/LUv//+O2rUKGNjYz09PVdX10OHDkldUlRUtHTp0j59+jAYDCcnp9DQUEK2EMfxy5frTePg5eXVcA/ZbPbOnTsHDx7MZDLpdLqjo6O/v//r168l61y4cEGqWRqN1rt37y1bttTW1iox3pkzZzKZzMYfX6ugkGD/FwPxmkasOzefiooKHR0dVcmNElm9VSU3WlpaqqphVlVVVVVVEYqDzUe1cqOFhYXa2tqqEnFW4UNDIFQC8kEjEAiEmoIMNAKBQKgpyEAjEB2U8PBwDMPi4uKkyhMSEhgMhomJyYsXLxRvLSoqysnJiU6n9+jRIzw8XDL0AgAKCwsDAgLs7OwYDEa/fv02bdrE5XIbaK20tBTDMPE6ZGsyb9482eRNfn5+jWZ9awmQgUYgEJ+RK9hPp9MxGSR3+gUFBX3zzTeamporV660trYOCQmRjNkgBPtjYmI8PDzWr19vZmYWGhrq7u4uZcRbk6dPn27atCk/P1+qPCcn5/Tp023SJbmgMDsEAvERtRLsb1GePHkSGhrq5eVlZmYGACKR6Pz58ykpKQcPHiRSS6sJyEAjEAgANRPsb2XYbHZ9o2tbkIsDgWhnsAQQ9kowK4UX/lpQpSIngYKC/YT+hhT1CfYTm/1wpQT7pfD393d0dCwoKJg2bVqXLl2sra2XLVsmFArj4+Pd3Nz09PQsLCw2bdokecnly5dHjhxpbGyspaVlbW39n//858OHD8SpMWPGEKr/gwcPJiREGAwGh8PhcDgVFRUNdIPFYgUEBNjY2Ojq6rq5uRFyUQR+fn4DBw6srKz09/fX1dV9/vy5gkNrGGSgEYj2BF8EMx7wNrzkn8gThqTzZzzkCZq9k0EsYB8fH9+AYD+dTjc2NtbX1583b15JSQlxFsfx/Px8BwcH4rC8vFwoFEpeTgj2S23Sa1SwX5aSkpIRI0aYmZlt3rzZxsYmMjJy+PDh06ZNGzly5Pbt27W1tUNDQ2NjY4nKCQkJEyZMyM3NXbJkybp163r16rV//36xKMfPP//8448/AsCePXvEMiNUKpVKpcqmqxbD5XKHDRvGYrFWr169dOnShw8fjh8/XnIHPI/HmzJlyvXr14cNG6anp6f40BoAuTgQiPbEwwrR5Q+fLeClIuGjCpGLvvIzrcePH3/99ddiAXuxqRUjKdivr69//fr1P/74486dO6mpqTQaTVKwf9euXTk5OVQq1d3dPSIiomfPnnLv2Khgv1zev38fFha2cOFCAJgzZ07nzp2Tk5MTExOHDh0KAMOGDXN0dExKSpoyZQoAHD16lEQi3blzRyyb5+/vf/z4cTabTaPR+vXrl5qaCgAuLi6DBimabJ7NZnt4eOzevZs4ZDKZwcHB9+/fHzduHFGSmpo6derUrKwsGo2m+LgaBs2gEYj2hKxPg9U8L4caCvbL7SeJRJo9ezbxmUajWVlZ2draEtYZAIjfldraWuJwx44dBQUFYuuM4ziLxRKJRDwerznPauXKleLP/fv3l7wjwfbt21VonQHNoBGI9oWLvrT6c3Omz/BJwN7IyCg6OnrUqFFz5sy5e/cuhUIRV9i4caPUJUFBQevXr5cV7BdLQru5uXl5eW3fvj0yMpIowXF89+7da9as4XK5s2bN2rlzp9h6FhYWEpaaYMeOHStWrJDtJ51Ol7R9JBJJcl++VNoUfX39wsLCf/75JzU19enTp4mJiXK9502CRqMRObTEHZCqQKFQJEWxVQKaQSMQ7QkmBbvnRvU20QCAiZ01HlN6r6IAACAASURBVLhTdZs3y1JPwf5mcuzYMTs7u/nz56elpQ0fPvzUqVOBgYHNbLPRFFwMBgNrXvIEWdAMGoFoZ7joky4MVo2oFsgI2F+5cmXr1q3jx48fPHgwAGRkZCxbtmz27Nlz5swRVyME+729vYlDxQX7o6OjZSWhCcF+VQ2HuHtgYODQoUPj4uLI5I8m7q+//lLhLVoNNINGIBAfUR/B/uaQl5dXUVFhb28vts65ubn//PMP0RnJmlKHagiaQSMQiM+oj2C/0lhaWtrb2x84cKCoqMjBweHt27exsbHdunUrLCz87rvvwsLCrK2tCX/F3r178/LyVOJUaSGQgUYgEHXYsGFDXFzcgQMHJk6c6O3t7ePjc+3atW3bth05coTL5fbu3fvQoUOSOVsZDMatW7c2b958+/btM2fO2NrahoaGrl69mlhpfPv2LQBkZmZmZmZK3cjLy6slDDShAPXjjz/Gx8fHx8f3798/NjZ2yJAhvr6+8fHxy5cvt7a29vT09PHxOXfuXHp6ujobaCTYrzxIsF8JkGA/AqE4HcVA8/n8qqoqDoeD47iqAhW5XK6mpqaq1m1ZLBaDwVCVuScC8lXSFI/H4/F42traKmmNSBImGcXVHGpqaigUiqp+1RR8aBiGtYnyJKID0lFcHBQKxcDAgJhBixeXm4lqZ9AsFktPT0+FM2hVDbOqqkokEqmqNdXOoLlcLoPBUOEMWlXDRCBUAoriQCA6KOos2C/FokWLMAx7+vQpcfjjjz9iGCYlwERw5coVDMMOHDjQpDH27NkTwzBCFkqtQAYagUB8ph0J9l+5ckUsddTMMero6DCZTNnNgW1OR3FxIBCIRmlfgv00Gi0oKMjLy8vQ0LCZY7x//77S3WhR1O4XA4FAtAmNCvb/XhdjY2OiglzBfj09vYSEBOJQrmA/iUQSV1CO0NDQkpKSoKCg5o9RbUEGGoFof2QXw50MeFuisgbVXLBfLj4+PlOnTo2Jibly5Yoi9RsYY1vlhG0UZKARiHbGn/cg7BwcugWbz8JJVbyatwvBfrlERkYymcwlS5YQG9MboOExqi3IQCMQ7YnCCrj67PPh5TR4X9msBh8/fjxmzBixYL9sBUnB/sOHD3t7e//xxx9DhgwhhJslBfutrKwMDAwYDIanp2d6enp9d1ROsF8unTt3/uWXX96+fbthw4bmjFFtQQYagWhPfGA1XtIk2otgf30sWLDA3d199+7dDx8+VHqMagsy0AhEe8JcZidN1+btrSEE+21sbKKjozkczpw5c/h8vmSFjRs3btu2TVKILigoCMMwWcH+kJCQWbNmhYeHnzp1qqKiYvv27eJLcByPiIhwcnK6evXqrFmznj17NmzYMOJUYWGhoQT/+9//mtR/DMOioqIoFMrChQvri9trdIxqCzLQCER7opM2zB7y+dB/KOg3T8JTDQX7f/3114kTJ0pOwBuOmLazswsJCXn69OmOHTtUNUY1ARloBKKdMaoX7J4NayfAnjngIT8vaxOQEuy3t7ffunVrcnIyUZKRkTFu3Dip/SCEYL94qU1xwf579+45OztLdYAQ7BdjYWGRnp5+4cKFwsJCcZ0PHz4AQAOpsletWuXo6Lhp06aMjIymjlGdQQYagWh/6GiBrQloN5KDqcmoiWA/YfrFwXOVlZWJiYlMJtPS0rK+SygUysGDB3k83vr165s6RnUG7SREIBCfUQfB/nnz5v3yyy+hoaG5ubldu3Y9efIki8Xas2dPw8qRLi4ugYGBe/bsaeoYFX00bQLekSgpKSkpKVFVa+Xl5YR4pkrIzs7mcrmqak2Fw2SxWPn5+apqrba2tqamRlWtFRQUsFgsVbWmwofWLtiyZQsAXLhwQaqcx+P169dP8tS///47atQoY2NjPT09V1fXQ4cOSV1SVFS0dOnSPn36MBgMJyen0NBQQrYQx/HLly/XZ3y8vLzq61tWVtb06dPNzMy0tbWdnZ2PHz8ueXblypUA8PLlS6mrqqqqLCwsAGD//v1NGuPMmTOZTGajT6z16Sh60ARIsF8JkGA/AtFWIB80AoFAqCnIQCMQCISaggw0AoFAqCnIQCMQHZR2lFFF5cybN8/Pz68NO6AgyEAjEIjPtKOMKkqTk5Nz+vTpNrl1U0Fx0AgE4iPtK6NKUxGJROfPn09JSTl48GBVVVWr3bc5IAONQLQ/cBx4NaDJgAa3bjSNRjOq+Pr6yr1QbkaV69evJyQkEAZabkaV0NDQhISE1jTQbDa7viGoLcjFgUC0M4pewrVwuBkB18Lhw2vVtKluGVX8/PwGDhxYWVnp7++vq6v7/PlzACgrK/v2228dHBwIT/eGDRtqa2vFl+jr669fvz4xMXHChAkGBgY9e/b8z3/+IykSwmAwOBwOh8OpqKho6vNpK5CBRiDaE9xqeHrq8+GTk8Crrb+2YqhnRhUejzdlypTr168PGzZMT08vPz/f0dHx2LFjHh4eGzZssLOzCwsLc3Nzk1xsvHnzpqenJ5VKXb58uZ2d3b59+wYMGFBdXS2uQKVSqVSqlpZW0x9S24AMNALRnqh6L13CKpRXT2HUNqNKamqqvr5+VlbWxYsXzc3NV61aVVNT8+jRo//+97/BwcHnzp3bs2dPSkqKpH707du3w8LCYmNjN2zYcPbs2Z07d2ZmZu7cubMZj6etadON5q0N0uJQAqTFoVawCvErYXX+sYqUbIrQqcAwzNHRMTMz08LCgkajpaenS1ULDQ0NDg6urq6WujA8PBzH8devXwOAnp6eiYlJWFjYsWPH1q5dq62tzWQy3717J9XUq1evPD09AcDQ0PD27dtEoVAoLJGgtrYWx/GZM2cCQFZWFlGHz+dTKBR/f/8KCUpLSxkMxrhx44g6TCazS5cufD5ffDuBQNC1a1cnJyepbnA4HACYOXOmkg+uFWlnBrq6uvrgwYPLli3z8/PbuHFjYmJiky5HBloJkIFWN56e/mydU/9Rvh3Czjo6On748AHH8fj4eAzDBgwYwOPxGr6wpqYGwzBvb28cxwl9GwaD8ebNG3EFQiApMDBQXCISiXbt2qWlpYVh2KxZs96/fy8+JZUZa8eOHTiOz5w5k0KhiEQiog7hB5fLgAEDiDpMJnPMmDFSXZ0wYQKDwZAqbEcGWo2iOHAcnzZtGo/HkyqPiorq3LkzAAiFwg0bNmRmZrq6ujo7O9+9e3f79u3ff//9qFGj2qK/CETb4OgLJj2guhi0jcHYvrmtSWUb2bNnT1hY2ObNmxu4RLmMKkePHnVwcIiOjpbS7CcyqogPu3fvTnxgMBhifVEiYnrevHnz5s2T7Yz4M5ksbdBIJJJAIBCJRJKa/e0INTLQxcXFPB7Pzs6ua9eukuU0Go34cO/evYyMjNmzZ8+YMQMAfH19f/jhhxMnTowcObJhoVgE4ksCw8CkJ5g0O5cKgVS2kStXrmzdunX8+PGDBw8GgIyMjGXLls2ePXvOnDniakRGFW9vb+JQ8Ywq0dHRspr9REaVhjtpY2OjoaFRW1vr7u4uLhSJRDdu3NDV1RWXvHjxQtIWC4XCJ0+e2NratlPrDGq1SEi86UydOvX7uojz3Fy7do1MJvv4+BCHOjo6np6eHz58SE1NbbNOIxBfEGqSUUUWKpU6bty406dPP378WFz422+/jR49+smTJ+KSnJwcIiibYM+ePbm5uWKL0R5Roxl0fn4+AFhZWeE4zmKxZPOPpaen9+jRQzJEpm/fvkeOHElPT3dycmrVviIQXyjqkFFFLjt37rxz546rq6u/v7+pqWlSUtL169fHjh07ZcoUcR0zM7Nly5b9+++/Dg4OKSkpFy5c6NatW3BwsCoeTNugRgY6Ly8Pw7CTJ0/evHmTz+czGIxBgwYFBAQQrzA8Hq+2tlZKT514h2og3B2BQDSVDRs2xMXFHThwYOLEid7e3j4+PteuXdu2bduRI0e4XG7v3r0PHToUEBAgrs9gMG7durV58+bbt2+fOXPG1tY2NDR09erVFAoFAN6+fQsAmZmZmZmZUjfy8vJS3EB379792bNnwcHBN27cyM/Pt7KyCgsLW758uaTfefjw4UuWLAkNDY2IiDAxMVmyZMlPP/2kqnwObYIaGWgiVCAzM9Pf319bWzs1NTU+Pj49PT0yMlJTU5PY/KOtrS15CXEouy9IKBRKaWWRSCQymSwSiaCxFO6KIxKJiCgOlbQGAEKhUFV9w3FchcNUYWtCoVCFreE4TvwVVNWagk3JLka1R9atW7du3TrZcgqF8ujRI8kSIt92A00ZGxv/97//lXvK09NTie/IiRMnZAtNTU0JD0wDuLu7y43mloRKparwa9uitM3/MxzHpbb3aGpqOjg42NnZTZ8+nXBijB492tzcPCYm5uzZs9OmTWugNamdSwBQWlp6//59yRIjIyNjY2Pis+Stm0llZaWqmgKA9+9lNiE0A9XKwbx7906FrakQLpdbVlamqtYUfGiSEQsIRMvRNga6rKxs/vz54sOAgABfX19ZeVYfH59jx44RW5KYTCbI2FbiUDaPnJGREREPL4mGhgbxTRYvLjeTyspKbW1tVeUkzM3N7dy5s6pyEpaVlalqmNXV1dXV1USkY/PhcDg4josjc5pJUVERg8GQeq9SGhU+NARCJbSNgdbV1Q0LCxMf1peQlEql6urqEhE8mpqadDpdaq5EeJ/19fWlLsQwjPB/yZZD3bii5oBhGIlEUmEEjwpbI/qmqqZApQ9Nha2BqkfafuOxlCA8PDwkJOTChQvigDmChIQEb29vSUloRYiKitq7d29GRoaFhYW/v//q1aslHUGFhYXr1q1LTEzMz8+3s7Pz9fUNDg6mUqmqHI8CqEk3FKdtDDSFQpGKuygoKIiKihoxYgQRmkNQW1vLYrHEYe09evR49uwZj8cTTzOJADt7+2YH6yMQCACoR7CfwWAQyhuSZGZm2tjYEJ+DgoIiIiKcnZ1Xrlz54MGDkJCQvLy8ffv2EWcJwf7Kysr58+dbW1snJiaGhoZeunQpMTFRhd78RoMFWqcbqkVdutWlS5ecnJyjR48OGDBAvOr6559/4jg+ZMgQ4nDMmDGPHj26fPnypEmTAIDD4fz777/GxsZ9+/Zts34jEF8QX7Zgv5p0o2m0+ubyeklOTp40adK8efMOHz4cExMTHBw8ceLEnTt3iisIBIKgoCBfX9/9+/efOXNmxYoVEydOjI+PV/wWSItDCZAWx5cKocVx4cIF4vDu3bs6OjrGxsbPnz+XrEYIa/zzT72qHytWrACA1NRUccnNmzeZTOa+ffuIQwcHB0tLS8lLqqurSSSSr6+vqsaiCGrSjSahRh63QYMGbd682dzcPD4+/vz58yKRaNmyZUFBQeIKGhoaYWFh48ePf/bs2V9//aWtrR0cHOzh4dGGfUYgWh9hiajyCKf8f+zKoxxhqUglbX7xgv3KdaPNURcXB4GTk1PDewIZDMaiRYtarT8IhLqB8/DKE1zeq4+hpcIKXH8JDZOzIt4ExIL9d+7caUCw//jx41wul8lk+vj47Nixw9DQED4J9ouDpsrLy3V1dSVDmwjBfqk2FRfsf/HihViw38XFpaqq6uuvv/b3909KSgoLC7t48eKdO3fEq3w3b97cuXMnsf8lJSVl3759165de/z4sba2ttLdaGPaeAbfuiAXhxIgF4dawX0lKFxaJfmPm6Hkf0LCxREWFmZgYEDYuMjISNlqI0eOBIA+ffr8+uuvhw8fnj17NgDY2toSws3EnDogICAiIsLS0hIAqFTq2LFjX7x4Ud99Dxw4oKGhIVcwWgyhBz116lTiLjiOz5o1S09PLyMjQ1xnz549ACD2ghKRuL/++qu4AiHVTyiHKNeNNgcZaOVBBloJkIFuJtzXMgY6s1kGugMK9svthnqCDLTyIAOtBMhANxMRDy/dUyu2zqV7akWNyOvXSwcU7G+gG+qJevmgEQhEw2AU0JujVX2RJ6rCNfQwxjjNZjqgO45gf8PdUE+QgUYg2hka+pjebJVtfus4gv0Nd0M9UaMwOwQC0bZ8wYL9KulG64Nm0AgE4jNfqmC/qrrR2rSxD7x1QYuESoAWCb9UpHYSiuHxeP369ZM89e+//44aNcrY2FhPT8/V1fXQoUNSlxQVFS1durRPnz4MBsPJySk0NJTNZhOniAVDuXh5edXXt5kzZzKZTKnC/Px8f3//7t270+n0Xr16bd26VRyEh+M4k8mcOXNmQkLCiBEj9PT07OzslixZUlFR0ZxutDkY3k6Eq1UC8ZomK0+qHBUVFTo6OqqSG3379q2pqamq5EZLS0tVNcyqqqqqqqr6FAebCvG9lVzYaQ6FhYXa2tqqSpmhwoeGaH309fW9vLzkKv23X5APGoFAINQUZKARCARCTUEGGoHooISHh2MYFhcXJ1WekJDAYDBMTExevHiheGtRUVFOTk6EjFF4eLhUdsfCwsKAgAA7OzsGg9GvX79NmzZJZQ1tHR49ejR+/Hhzc3NdXV1nZ+f9+/fLJsxTK5CBRiAQn5Er2E+n0zEZsrKyxFcFBQV98803mpqaK1eutLa2DgkJkQyWIJTyY2JiPDw81q9fb2ZmFhoa6u7urqpsvwTl5eUNO6Dv37/v4uLy5MmTOXPmBAcHk8nkb7/9duXKlSrsg+pp40XK1gVFcSgBiuL4UpGN4rhz5462traUJPTbt28BwMXFZV5dioqKiAovX74EgLFjx4q/DlOmTMEwTCzrERgYCACxsbHiNomdihERES0+SAk8PDxoNJpYIUQoFLq4uGAYRux0V0/QDBqBQAAAJCcne3l50el0KUlowv6uWbPm97oYGxsTFYi9ITt27BBHNC1btkxPTy8hIYE4vHHjhqWlpXhjCwD88MMPJBJJXKF1ePjw4YgRI8zNzYlDEok0adIkHMefPXvWmt1oEshAIxDtDRGOJ5XjfxfiyRUgUk2Y7Bcv2M/j8bZt2ybl0KisrAQAyc3i6gYy0AhEuwIHfH8ufvAdfqkYj8rFD7yDZptosWB/fHx8A4L9dDrd2NhYX19/3rx5JSUlH7uD4/n5+Q4ODsRheXm51LIboZS/f/9+yULFBfuvX78uFux3dHQ8duyYh4fHhg0b7OzswsLC3NzcJBcbb9686enpSaVSly9fbmdnt2/fvgEDBlRXVwOApqbm0qVLCW3rDx8+pKamRkZG7t2718PDY8CAAUo+uFagjV0srQvyQSsB8kGrF9m1wvlPJf/hObWNXyWPDivYT4iEEIMqLS1t/Em1HchAKw8y0EqADHQzET1lSRloUZqST6DDCvbfv3//77///vHHH+l0ur29vTovEiIDrTzIQCsBMtDNpZIvPYOualZGlQ4l2C/F0aNHASAkJESBp9U2IDU7BKJdoUvGlluJIt4SUvZYkDVoN0sNpoMI9mdlZV29etXT09PW1lZ8dvz48QBArECqJ8hAIxDtDKyPrsbhPlArBLoKhLo6iGB/ZWVlYGDgTz/9tHr1anH9srIyAOjatWvDd29DUBQHAtE+UYV1luILFuzv06ePvr5+VFQUEdRBQKwienh4KNerVgDNoBEIxGe+VMF+TU3NX3/9NSAgoHfv3lOnTqXT6QkJCbdv354yZYqvr68KHlwL0dZO8FYFLRIqAVok/FLpUIL9BHFxcW5ubkZGRkwmc/DgwYRYUlOeWWuDBPuVBwn2KwES7Ee0EEiwH4FAIBCtR0fxQfN4PBaLRewKFe9SbX6bfD5fHAnUfCoqKiSX1JsDl8tV1TD5fL5AIFBVa8QbpaSEQnPg8/nV1dWqUhZW/KEZGhqq5I4IRMN0FAOtqalpaGiozi6O6upqJpOpti4OVZkk5OJAIBQHuTgQiI7It99+K6vBL8mhQ4cA4Pr16w3UycnJkWp23rx5fn5+bTGgxgX72yMdZQaNQCAkGTdunORbUXh4eOfOnRcsWCAu6du3r/jzsGHDxAJDkujp6Uke5uTknD59WryBBdF8kIFGIDoikyZNmjRpkvjwp59+MjMzCwsLk1t5xIgR9Z0CAJFIdP78+ZSUlIMHD1ZVVam+rx0YZKARiHZIRS18qAITXdCjtXVXgM1mq/Vej/YMMtAIRHvjWjrEPvr4efoAGNmjTXsDDAaDw+EAAIfDIUSZEaoCLRIiEO2KkurP1hkATqZAWU3b9eYjVCqVSqVqaWm1dUe+NNAMGoFoVxRWSpcUVIKBkiJEChIfH08IfkoSGBhoZmbWovdFIAONQLQrjGWCvk1aPOdpUlJSUlKSVOHUqVORgW5pkIsDgWhXmOiCl8Pnw/G9wUi7pe8pN+eIWuda/VJAM2gEor3h2xdcrCC/Asz1oYte4/UR7RZkoBFqhkgkepMJHA5mbYMpq+/+5WPKBFMUL/Hlgww0os3AWZWi1McgFALTADi1WGdTUhdT/tHDotfpRAXKkmUka9uGG0EgvmCQgUa0DXjRe97OrVKFJMtuopw34kPhjWvIQCM6MmiRENE2CG9dly2UtM4AIHqV3lrdQSDUETSDRrQFOC7KfdtoLZJ9z5bvCgIAgM/nyy0fNWqU4kmXqFRqh8rQ1AqgGTSiDRAmJ+IfihqvV1ODvy9o+e4gEGoKMtCIVkcgEFy/okhFUV6u4OLZlu4OAqG2IAONaG2wR/ehiqVgZdGrdODzWrQ/CITaggw0orXBij807QK+tAoEAtFBQAYa0drgRsZ1DhupDbz/7RQ9ug8yYj0IxBcPMtCI1gbv70Lq0Ut82EhSdAzw4g/8v2L4v+8HDqeFu4ZAqBfIQCNaHTKZMu8b8rzFoFNXhq3BubQo87XwvrSgGgLxZYMMNKItwDCNnr0p4z/lxCNMcyNzacDLSlu0UwiEuoEMNKLNEOW8/fipMdNMgNdUA7u2xbqDQKgdyEAj2giRSJic2JQLcFHqY/7Rw8gTjeg4IAONaCNEQrnF9TuiMQAQZb3m/S+iiZYdgWivIAONaCPIFFIvR9liwtvRwHohXlQg+OekKP1ZC/UL8f/27j+mqXP/A/hzgP7AloIgym8IaO398t3YlAjSTGGUYZZsZQlgzOJNZFvmtgTEZCbuD7dIYrJkyXD+sxSXJQLCCG6BXc3cDCjiJbDpvRaQbxw4jK3fRWz5XaClfe4fJ+ttCoPqzmmf1vfrD8PznNPTD4/6zunpOc8D7GBlsiSLxXLo0KFVN4WHh3/33XeEEEppRUWF3e79XJnBYEhISBC9RBCapPzA0snBVTete1HadWcw7G//K3hJAExhJaBlMllxcbFXJ6W0t7d3y5YtfHNiYsJut6vV6tTUVM/dIiMj/VQlCEuhlBz4u6Pl3NO8VioTuhoA5rAS0Eqlsqamxqvz8uXLvb29x44d45smk4kQUl5enp+f7+/6QBxhL+RKM7Kc//rF+cP3T/TC8J15IpUEwA52r0E/ePDg7Nmzhw8fTk9P53vMZjMhJCMjg1I6PT0d0OpAMFzMxvAdu550FmE6i38AEPpYOYP2Qimtr6/XaDQ6nc7daTKZOI5ra2u7du2aw+FQKBR5eXlVVVUqlWqNQwH7uOjoiL0657Urvr+E3v0/sv1/1t8PIJgxGtBXr14dHR09ffq0Z6fZbKaUjo6OHjx4UKlUGo3Grq6ukZGRM2fOSKVSzz2XlpasVqtnj0QiiYyM5JeNmJ+fF6RIu91us9nCwgT7FLKwsPBnC1s8KYfDIdSvubS05HK5BBw0Sukq627sLeYyt4b941sf57pb7r26+FKR0+lcWloS6q/A90FTYLlx8IvABDSldG5uzt2UyWSeCbu0tNTY2KjT6TIyMjxflZ2drVarKysr5XI5IUSn06WkpDQ1NXV0dFRUVHjuabPZRkdHPXuio6M3bdq0vLxMCBHq8ojT6XQ4HBzn22NwPpibmxPqaMvLywL+mk6nU6ijuVwuQsjiqg+bqGLkmmyFz5ORzt8bc8bFLywsrLyx5+n4PmgIaPCPwAS01Wr1vKmuqqqqrKzM3bxy5crjx4/feOMNr1cdOHDAq0ev1zc3N4+MeC8tunHjxpdeemnl+1osFkJIXFzcXynebWpqKioqKjw8XJCjjY+Px8fHe30UeGoWi0WoX3N2dnZ2djYpKUmQoy0sLFBKN2zYsOpW16M03z9BxKel/e5wKpXKqKgoQWoTcNAABBGYgFapVHV1de6m539+SunFixezs7NTUlLWPY5MJlOpVDMzvi7PAYwL02SHZW1zjf26zn6UhOW8yMXFk99/90tdAIERmICWSCQ5OTmrbrp9+7bJZKqsrPTqf/jwocFgKCwsLCwsdHfabLaZmZnc3FzxSgW/kssj3qxy/vMasVq5jMywrWr6+/87zjW4t4e/mEskUi4lNTw3nwh3cQmATcx9SdjT08Nx3MrMTUxMvH//fmNj486dO90faVtbWymlu3fv9nuZIBZOoYgoefW/zbhN0trjzsF/cWHhYS/mcrG4BAHPELYCmlJ68+bN9PR0pVLptYnjuMOHD586daq6unrPnj1SqXRoaGh4eLioqGjXrl0BqRb8g0tIjEhIDHQVAAHAVkDfu3dvcnJSq9WuujUvL+/kyZPt7e1dXV0OhyM9Pb26utrzRmkAgFDCVkBnZWV1dnausUNOTs6fXbwGAAgx7D7qDQDwjENAAwAwCgENAMAoBDQAAKMQ0AAAjEJAAwAwCgENAMAoBDQAAKMQ0AAAjEJAAwAw6tkK6OnpaQEnj1YoFAKud/Xo0SOh1rsihAg1hz0hZGFhwWv9sL9CKpXKZDKhjma1Wm02m1BHE3DQAATB1lwcYhPwPzMhRCKRCHi0iYkJp9Mp1NGEWpmFELK0tDQ1NSXU0YRagIbHL2oj1NEEHDQAQTxbZ9AAAEEEAQ0AwCgENAAAoxDQAACM4iilga7Bf+bm5gghK9fTYsHjx49jYmIiIpj72nZxcXFxcTEmJibQhaxiampKLpfL5fJAFwIgimcroAEAgggucQAALlCwqgAACBlJREFUMAoBDQDAKAQ0AACjmPtKys/m5+dbW1uNRuPExIRarS4pKdFqtf5568nJycbGxjt37lgsluTk5Ly8vPLycmGfTvyLAjg4XtgfKwAxhPKXhJTSiooKu93u1W8wGBISEgghTqfz2LFjo6OjWq02MTGxr6/PZDLV1NQUFxeLXdvMzMz7778/Pz+v0+m2bNly586dX375Ra1Wf/rpp/zD0OsWL7YADo4X9scKQCShfAY9MTFht9vVanVqaqpnf2RkJP9Df3//r7/++uabb+7fv58QUlZWdvTo0ZaWlpdffpnjOFFra2lpmZmZOX78+O7du/meb775prm5+dKlS6+99povxYstgIPjhf2xAhBJKAe0yWQihJSXl+fn56+6w08//RQREaHX6/lmVFRUaWnpuXPnjEZjTk6OqLUNDg5u3rzZnTiEEL1ef/78+cHBQT501i1ebAEcHC/sjxWASEL5S0Kz2UwIycjIoJROT0+v3GFkZESj0Xg+5vDCCy/w/aIWRimNiIjYsWOHZ+fy8jLHcfyjNMSH4sUWqMHxEhRjBSCSED+D5jiura3t2rVrDodDoVDk5eVVVVWpVCpCiN1ut9lscXFxni+JjY0lhExOTopaGMdx9fX1Xp09PT0ul2vbtm2+FC+2AA6OF/bHCkA8oRzQZrOZUjo6Onrw4EGlUmk0Gru6ukZGRs6cOSOVSvk5jr0e++abAk5/7KPLly8bDAaFQsF/Zl+3eLHrYWpwvLA2VgDiCZGAppS6P/ASQmQymVQqzc7OVqvVlZWV/Od0nU6XkpLS1NTU0dFRUVGxxtEEnDj/z2pzN81mc0NDw61bt1Qq1UcffbRp0ya+/6mLF5uwg/NEgm6sAP6iEAloq9V66NAhd7OqqqqsrOzAgQNeu+n1+ubmZv4qKj/7j2d0upteH+3FqI0QQin9/vvvz50753A49u7d+9Zbb3lOSLR28WLz2+D4iOWxAhBPiAS0SqWqq6tzN5OSklbdTSaTqVQqfllCqVS6YcMGr9X2+AusGzduFLs2Sml9fX13d3daWtqRI0e2bt267nE8ixeb3wbHF4yPFYB4QiSgJRKJ171fDx8+NBgMhYWFhYWF7k6bzTYzM5Obm8s3NRrN0NCQ3W53X3MwGo2EkO3bt4taGyGks7Ozu7tbq9XW1NSsnC3Tl+LF5p/B8QX7YwUgkpC9zS4xMfH+/fuNjY2zs7PuztbWVkqp+47akpISu93+ww8/8M3FxcUrV65s3ryZv59MPJTSzs7O6Ojo2traVecy9qV4sQVqcLwExVgBiCSUH/Xu7+8/depUbGzsnj17pFLp0NDQ8PBwUVFRbW0tv4PT6fzwww9/++23ffv2JSQkXL9+/e7du7W1tUVFRaIWZjab33vvvcTExOeee85rU1pa2uuvv+5L8WIL1OB4CYqxAhBJKAc0IeT27dvt7e3j4+MOhyM9Pb2kpESn03nuMD8/f/78eaPRaLFY1Gr1K6+8UlBQIHZVt27d+uSTT1bdtGPHDvemdYsXW0AGx0uwjBWAGEI8oAEAglfIXoMGAAh2CGgAAEYhoAEAGIWABgBgFAIaAIBRCGgAAEYhoAEAGIWABgBgFAI6KG3bto1bITY2Njc39+TJk35e9GRVN27c4DjuwoULgS4EIIghoIOYRCKR/yEiImJycvLmzZsff/yxRqPp6enx3LO2tpbjOH/OFNrU1OS39wIIVQjoINbQ0LDwh8XFxaGhoa+//lqj0Tx69OjVV1/lZwcNiAsXLhgMhkC9O0DICJH5oCE8PDw7Ozs7O7u8vLy4uHhgYOCdd97p7+/nt+7bty8mJmbV6ToF1N3d3dLS0tfXNzQ0JOobATwjENChRqlUfv7551qtdmBg4MaNG1qtlhBSWlpaWloq9ltfunSpoaFB7HcBeHbgEkcIKigo4Oeqb2tr43s6Ojo4jnOvoHrx4kWO444fPz41NfXBBx8kJyfL5fLt27efPn2aELK4uHjixImsrCy5XJ6ZmVlTUzM9Pe3L+9bW1v77DyunbwaAJ4Uz6NBUUlLS19c3MDCwxj5WqzU/P39sbEyj0djt9rt37x45csRisVy9evX69euZmZnJycn37t374osvTCaTL/djJCUluVeDVCgUwvwmAM8wnEGHptTUVELI2NjYGvucPXs2Ojp6fHx8cHDQbDbza2PX1dUNDw93dXWNjY2NjY19+eWXhJBvv/3WYrH4p3IAcENAhyY+oNe+ITo8PLylpSU5OZkQIpVKT5w4wfd/9tln7nWt3n333fT0dLJe1gOAGBDQoYnjOPeff+b555/PzMx0N/kgJoTo9XrP3fh+h8MhfJUAsCZcgw5NDx48IITExMSssU9CQoJnMywsjBAik8liY2NX9rtVVlZOTEy4m1u3bsWdGwAiQUCHJj6gs7Ky1thn1fPrtU+6CSF9fX0mk8ndnJqaeqoCAWB9COjQ9OOPPxJCdu3aJfiR+egHAD/ANegQ1Nvb29fXRwjZv39/oGsBgKeHgA41c3NzR48eJYTk5eUVFBQEuhwAeHoI6BCxvLw8ODj41Vdf7dy58+eff1YoFPjuDiDY4Rp0EKuqqnr77bf5n10ul8vl4n+Oj49vb2/Hw9YAwQ4BHcQ8Q5kQEh0dnZmZqdfrq6ur/Tn1MwCIhKOUBroGAABYBa5BAwAwCgENAMAoBDQAAKMQ0AAAjEJAAwAwCgENAMAoBDQAAKMQ0AAAjEJAAwAwCgENAMAoBDQAAKMQ0AAAjEJAAwAwCgENAMCo/wAvam53Ew8mkgAAAABJRU5ErkJggg==\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": 66,
   "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": 67,
   "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": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time difference of 2.105424 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": 69,
   "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": 70,
   "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": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "sigK562 <- significantDEpeaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "sigK562Sel <- sigK562[sigK562$log2FoldChange>2,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "bedg1 = sigK562Sel\n",
    "jobg1 = submitGreatJob(bedg1, version = \"3.0\", species='hg19')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "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": 186,
   "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": "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": "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": {},
   "outputs": [],
   "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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