#### Libraries needed

## Bioconductor
library('IRanges')
library('GenomicRanges')
library('GenomeInfoDb')

if(hg19) {
    library('biovizBase')
    library('TxDb.Hsapiens.UCSC.hg19.knownGene')
}

## CRAN
library('ggplot2')
library('grid')
library('gridExtra')
library('knitr')
library('RColorBrewer')
library('mgcv')

## Loading required package: nlme
## 
## Attaching package: 'nlme'
## 
## The following object is masked from 'package:IRanges':
## 
##     collapse
## 
## This is mgcv 1.8-3. For overview type 'help("mgcv-package")'.

## GitHub
library('derfinder')

## Working behind the scenes
# library('knitcitations')
# library('rmarkdown')
# library('knitrBootstrap')

#### Code setup

## Use UCSC names by default
seqlevelsStyle(fullRegions) <- chrsStyle
names(fullCov) <- mapSeqlevels(names(fullCov), chrsStyle)

## For ggplot
tmp <- fullRegions
names(tmp) <- seq_len(length(tmp))
regions.df <- as.data.frame(tmp)
regions.df$width <- width(tmp)
rm(tmp)
nulls.df <- as.data.frame(fullNullSummary)

## Special subsets: need at least 3 points for a density plot
keepChr <- table(regions.df$seqnames) > 2
regions.df.plot <- subset(regions.df, seqnames %in% names(keepChr[keepChr]))

if(hasSig) {
    ## Keep only those sig
    regions.df.sig <- regions.df[idx.sig, ]
    keepChr <- table(regions.df.sig$seqnames) > 2
    regions.df.sig <- subset(regions.df.sig, seqnames %in% names(keepChr[keepChr]))
    
    if(nrow(regions.df.sig) > 0) {
        ## If there's any sig, keep those with finite areas
        if(hasArea) {
            finite.area.sig <- which(is.finite(regions.df.sig$area))
            
            regions.df.sig.area <- regions.df.sig[finite.area.sig, ]
            keepChr <- table(regions.df.sig.area$seqnames) > 2
            regions.df.sig.area <- subset(regions.df.sig.area, seqnames %in%
                names(keepChr[keepChr]))
            
            ## Save the info
            hasArea <- (nrow(regions.df.sig.area) > 0)
        }
    } else {
        hasSig <- hasArea <- FALSE
    }
}

## Get chr lengths
if(hg19) {
    data(hg19Ideogram, package = 'biovizBase')
    seqlengths(fullRegions) <- seqlengths(hg19Ideogram)[mapSeqlevels(names(seqlengths(fullRegions)),
         'UCSC')]
}

## Find which chrs are present in the data set
chrs <- levels(seqnames(fullRegions))

## Subset the fullCoverage data in case that a subset was used
colsubset <- optionsStats$colsubset
if(!is.null(fullCov) & !is.null(colsubset)) {
    fullCov <- lapply(fullCov, function(x) { x[, colsubset] })
}

## Get region coverage for the top regions
if(nBestRegions > 0) {
    if(packageVersion('derfinder') >= '0.0.60') {
        regionCoverage <- getRegionCoverage(fullCov = fullCov, 
            regions = fullRegions[seq_len(nBestRegions)],
            chrsStyle = chrsStyle, verbose = FALSE)
    } else {
        regionCoverage <- getRegionCoverage(fullCov = fullCov, 
            regions = fullRegions[seq_len(nBestRegions)],
            verbose = FALSE)
    }
    
    save(regionCoverage, file=file.path(workingDir, 'regionCoverage.Rdata'))
}

## Graphical setup: transcription database
if(hg19 & is.null(txdb)) {
    txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
} else {
    stopifnot(!is.null(txdb))
}

p1 <- ggplot(regions.df.plot, aes(x=pvalues, colour=seqnames)) +
    geom_line(stat='density') + xlim(0, 1) +
    labs(title='Density of p-values') + xlab('p-values') +
    scale_colour_discrete(limits=chrs) + theme(legend.title=element_blank())
p1

## Compare the pvalues
summary(fullRegions$pvalues)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0189  0.0189  0.6230  0.4070  0.6230  0.9620

summary(fullRegions$qvalues)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0134  0.0134  0.2060  0.1340  0.2060  0.2480

qtable <- lapply(c(1e-04, 0.001, 0.01, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5,
    0.6, 0.7, 0.8, 0.9, 1), function(x) {
    data.frame('Cut' = x, 'Count' = sum(fullRegions$qvalues <= x))
})
qtable <- do.call(rbind, qtable)
kable(qtable, format = 'html', align = c('c', 'c'))

xrange <- range(log10(regions.df.plot$width))
p2a <- ggplot(regions.df.plot, aes(x=log10(width), colour=seqnames)) + 
    geom_line(stat='density') + labs(title='Density of region lengths') +
    xlab('Region width (log10)') + scale_colour_discrete(limits=chrs) +
    xlim(xrange) + theme(legend.title=element_blank())
p2b <- ggplot(regions.df.sig, aes(x=log10(width), colour=seqnames)) +
    geom_line(stat='density') +
    labs(title='Density of region lengths (significant only)') +
    xlab('Region width (log10)') + scale_colour_discrete(limits=chrs) +
    xlim(xrange) + theme(legend.title=element_blank())
grid.arrange(p2a, p2b)

xrange <- range(log10(regions.df.plot$area[finite.area]))
if(inf.area > 0) {
    print(paste('Dropping', inf.area, 'due to Inf values.'))
}
p3a <- ggplot(regions.df[finite.area, ], aes(x=log10(area), colour=seqnames)) +
    geom_line(stat='density') + labs(title='Density of region areas') +
    xlab('Region area (log10)') + scale_colour_discrete(limits=chrs) +
    xlim(xrange) + theme(legend.title=element_blank())
p3b <- ggplot(regions.df.sig.area, aes(x=log10(area), colour=seqnames)) +
    geom_line(stat='density') +
    labs(title='Density of region areas (significant only)') +
    xlab('Region area (log10)') + scale_colour_discrete(limits=chrs) +
    xlim(xrange) + theme(legend.title=element_blank())
grid.arrange(p3a, p3b)

p4 <- ggplot(nulls.df, aes(x=log10(width), colour=chr)) +
    geom_line(stat='density') + labs(title='Density of null region lengths') +
    xlab('Region width (log10)') + scale_colour_discrete(limits=chrs) +
    theme(legend.title=element_blank())
nulls.inf <- !is.finite(nulls.df$area)
if(sum(nulls.inf) > 0) {
    print(paste('Dropping', sum(nulls.inf), 'due to Inf values.'))
}
p5 <- ggplot(nulls.df[!nulls.inf, ], aes(x=log10(area), colour=chr)) +
    geom_line(stat='density') + labs(title='Density of null region areas') +
    xlab('Region area (log10)') + scale_colour_discrete(limits=chrs) +
    theme(legend.title=element_blank())
grid.arrange(p4, p5)

xrange <- range(log2(regions.df.plot$meanCoverage))
p6a <- ggplot(regions.df.plot, aes(x=log2(meanCoverage), colour=seqnames)) +
    geom_line(stat='density') + labs(title='Density of region mean coverage') +
    xlab('Region mean coverage (log2)') + scale_colour_discrete(limits=chrs) +
    xlim(xrange) + theme(legend.title=element_blank())
p6b <- ggplot(regions.df.sig, aes(x=log2(meanCoverage), colour=seqnames)) +
    geom_line(stat='density') +
    labs(title='Density of region mean coverage (significant only)') +
    xlab('Region mean coverage (log2)') + scale_colour_discrete(limits=chrs) +
    xlim(xrange) + theme(legend.title=element_blank())
grid.arrange(p6a, p6b)

for(j in grep('log2FoldChange', colnames(values(fullRegions)))) {
    ## Identify the groups
    groups <- strsplit(gsub('log2FoldChange', '',
        colnames(values(fullRegions))[j]), 'vs')[[1]]
    
    ## Calculate the mean coverage only using the 2 groups in question
    j.mean <- which(colnames(values(fullRegions)) %in% paste0('mean', groups))
    groups.n <- sapply(groups, function(x) { sum(optionsStats$groupInfo == x) })
    ma.mean.mat <- as.matrix(values(fullRegions)[, j.mean])
    ## Weighted means
    ma.mean <- drop(ma.mean.mat %*% groups.n) / sum(groups.n) +
        optionsStats$scalefac
    ma.fold2 <- drop(log2(ma.mean.mat + optionsStats$scalefac) %*% c(1, -1))
    
    ma <- data.frame(mean=ma.mean, log2FoldChange=ma.fold2)
    ma2 <- ma[is.finite(ma$log2FoldChange), ]
    fold.mean <- data.frame(foldMean=mean(ma2$log2FoldChange, na.rm=TRUE))
    
    p.ma <- ggplot(ma, aes(x=log2(mean), y=log2FoldChange)) +
        geom_point(size=1.5, alpha=1/5) + 
        ylab("Fold Change [log2(x + sf)]\nRed dashed line at mean; blue line is GAM fit: y ~ s(x, bs = 'cs')") +
        xlab(paste('Mean coverage [log2(x + sf)] using only groups', groups[1], 'and',
            groups[2])) + labs(title=paste('MA style plot:', groups[1], 'vs ', 
            groups[2])) + geom_hline(aes(yintercept=foldMean), data=fold.mean, 
            colour='#990000', linetype='dashed') +
        geom_smooth(aes(y=log2FoldChange, x=log2(mean)), data=subset(ma2,
            mean > 0), method = 'gam', formula = y ~ s(x, bs = 'cs'))
    print(p.ma)
}

plotOverview(regions=fullRegions, type='qval', base_size=overviewParams$base_size, areaRel=overviewParams$areaRel, legend.position=c(0.97, 0.12))

plotOverview(regions=fullRegions, annotation=fullRegions, type='annotation', base_size=overviewParams$base_size, areaRel=overviewParams$areaRel, legend.position=c(0.97, 0.12))

annoReg <- table(fullRegions$region, useNA='always')
annoReg.df <- data.frame(Region=names(annoReg), Count=as.vector(annoReg))
kable(annoReg.df, format = 'html', align=rep('c', 3))

plotOverview(regions=fullRegions[idx.sig], annotation=fullRegions[idx.sig], type='annotation', base_size=overviewParams$base_size, areaRel=overviewParams$areaRel, legend.position=c(0.97, 0.12))

if(nBestRegions > 0) {
    plotRegionCoverage(regions = fullRegions, regionCoverage = regionCoverage,
        groupInfo = optionsStats$groupInfo, nearestAnnotation = regions.df,
        annotatedRegions = fullAnnotatedRegions, 
        whichRegions = seq_len(min(nBestRegions, length(fullRegions))),
        colors = NULL, scalefac = optionsStats$scalefac, ask = FALSE, 
        verbose = TRUE, txdb = txdb) 
}

## 2014-09-29 22:33:17 plotRegionCoverage: extracting Tx info
## 2014-09-29 22:33:24 plotRegionCoverage: getting Tx plot info

info <- do.call(rbind, lapply(fullAnnotatedRegions$countTable, function(x) { data.frame(table(x)) }))
colnames(info) <- c('Number of Overlapping States', 'Frequency')
info$State <- gsub('\\..*', '', rownames(info))
rownames(info) <- NULL
kable(info, format = 'html', align=rep('c', 4))

topArea <- head(regions.df, 2000)
topArea <- data.frame('areaRank'=order(topArea$area, decreasing=TRUE), topArea)
## Clean up -Inf, Inf if present
## More details at https://github.com/ramnathv/rCharts/issues/259
replaceInf <- function(df, colsubset=seq_len(ncol(df))) {
    for(i in colsubset) {
        inf.idx <- !is.finite(df[, i])
        if(any(inf.idx)) {
            inf.sign <- sign(df[inf.idx, i])
            df[inf.idx, i] <- inf.sign * 1e100
        }
    }
    return(df)
}
topArea <- replaceInf(topArea, grep('log2FoldChange|value|area',
    colnames(topArea)))

## Make the table
kable(topArea, format = 'html', table.attr='id="regions_table"')

## Select clusters by cluster area
df <- data.frame(area = fullRegions$area,
    clusterChr = paste0(as.integer(fullRegions$cluster), 
    chr = as.character(seqnames(fullRegions))))
regionClustAreas <- tapply(df$area, df$clusterChr, sum)
bestArea <- sapply(names(head(sort(regionClustAreas, decreasing=TRUE),
    nBestClusters)), function(y) { which(df$clusterChr == y)[[1]]})

## Graphical setup: ideograms 
if(hg19 & is.null(p.ideos)) {
    ## Load ideogram info
    data(hg19IdeogramCyto, package = 'biovizBase')
    ideos.set <- as.character(unique(seqnames(fullRegions[bestArea])))
    p.ideos <- lapply(ideos.set, function(xx) { 
        plotIdeogram(hg19IdeogramCyto, mapSeqlevels(xx, 'UCSC'))
    })
    names(p.ideos) <- ideos.set
} else {
    stopifnot(!is.null(p.ideos))
}

## Graphical setup: main plotting function
regionClusterPlot <- function(idx, tUse='qval') {
    ## Chr specific selections
    chr <- as.character(seqnames(fullRegions[idx]))
    p.ideo <- p.ideos[[chr]]
    covInfo <- fullCov[[chr]]
    
    ## Make the plot
    p <- plotCluster(idx, regions = fullRegions, annotation = regions.df,
        coverageInfo = covInfo, groupInfo = optionsStats$groupInfo,
        titleUse = tUse, txdb = txdb, p.ideogram = p.ideo)
    print(p)
    rm(p.ideo, covInfo)
    
    return(invisible(TRUE))
}

## Genome plots
for(idx in bestArea) {
    regionClusterPlot(idx, ifelse(nullExist, 'qval', 'none'))
}

## Get the permutation information
nSamples <- seq_len(length(optionsStats$groupInfo))
permuteInfo <- lapply(seeds, function(x) {
    set.seed(x)
    idx <- sample(nSamples)
    data.frame(optionsStats$groupInfo[idx])
})
permuteInfo <- cbind(data.frame(optionsStats$groupInfo), do.call(cbind, permuteInfo))
colnames(permuteInfo) <- c('original', paste0('perm', seq_len(optionsStats$nPermute)))
## The raw information
# permuteInfo

n <- names(table(permuteInfo[, 2]))
permuteDetail <- data.frame(matrix(NA, nrow=optionsStats$nPermute * length(n),
    ncol = 2 + length(n)))
permuteDetail[, 1] <- rep(seq_len(optionsStats$nPermute), each=length(n))
permuteDetail[, 2] <- rep(n, optionsStats$nPermute)
colnames(permuteDetail) <- c('permutation', 'group', as.character(n))
l <- 1
m <- 3:ncol(permuteDetail)
for(j in n) {
    k <- which(permuteInfo[, 1] == j)
    for(i in 2:(optionsStats$nPermute + 1)) {
        permuteDetail[l, m] <- table(permuteInfo[k, i])
        l <- l + 1
    }
}

## Print the summary
summary(permuteDetail[, m])

##       CEU             YRI     
##  Min.   : 6.00   Min.   :4.0  
##  1st Qu.: 8.25   1st Qu.:4.5  
##  Median :10.50   Median :5.0  
##  Mean   :10.50   Mean   :5.0  
##  3rd Qu.:12.75   3rd Qu.:5.5  
##  Max.   :15.00   Max.   :6.0

kable(permuteDetail, format = 'html', table.attr='id="permutation_table"')

## analyzeChr(chrnum = "21", coverageInfo = genomeData, models = models, 
##     cutoffFstat = 1, cutoffType = "manual", seeds = 20140330, 
##     groupInfo = group, mc.cores = 1, writeOutput = TRUE, returnOutput = FALSE)

## mergeResults(chrs = "chr21", prefix = "report", genomicState = genomicState$fullGenome)

## derfinderReport(prefix = "report", outdir = "html", browse = FALSE, 
##     nBestRegions = 15, makeBestClusters = TRUE, fullCov = list(`21` = genomeDataRaw$coverage), 
##     optionsStats = optionsStats)

## [1] "2014-09-29 22:33:49 EDT"

## Time difference of 42.33 secs

## Session info--------------------------------------------------------------

##  setting  value                       
##  version  R version 3.1.1 (2014-07-10)
##  system   x86_64, darwin10.8.0        
##  ui       X11                         
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  tz       America/New_York

## Packages------------------------------------------------------------------

##  package                           * version  date      
##  acepack                             1.3.3.3  2013-05-03
##  AnnotationDbi                     * 1.27.15  2014-09-24
##  base64enc                           0.1.2    2014-06-26
##  BatchJobs                           1.3      2014-07-10
##  BBmisc                              1.7      2014-06-21
##  bibtex                              0.3.6    2013-07-29
##  Biobase                           * 2.25.0   2014-04-12
##  BiocGenerics                      * 0.11.5   2014-09-13
##  BiocParallel                        0.99.22  2014-09-24
##  biomaRt                             2.21.1   2014-07-08
##  Biostrings                          2.33.14  2014-09-09
##  biovizBase                        * 1.13.11  2014-09-16
##  bitops                              1.0.6    2013-08-17
##  brew                                1.0.6    2011-04-13
##  BSgenome                            1.33.9   2014-09-12
##  bumphunter                          1.5.5    2014-08-29
##  Cairo                               1.5.5    2013-12-24
##  checkmate                           1.4      2014-09-03
##  cluster                             1.15.3   2014-09-04
##  codetools                           0.2.9    2014-08-21
##  colorspace                          1.2.4    2013-09-30
##  DBI                                 0.3.1    2014-09-24
##  derfinder                         * 0.99.2   2014-09-30
##  derfinderHelper                     0.99.2   2014-09-29
##  derfinderPlot                       0.99.2   2014-09-30
##  devtools                            1.6      2014-09-23
##  dichromat                           2.0.0    2013-01-24
##  digest                              0.6.4    2013-12-03
##  doRNG                               1.6      2014-03-07
##  evaluate                            0.5.5    2014-04-29
##  fail                                1.2      2013-09-19
##  foreach                             1.4.2    2014-04-11
##  foreign                             0.8.61   2014-03-28
##  formatR                             1.0      2014-08-25
##  Formula                             1.1.2    2014-07-13
##  futile.logger                       1.3.7    2014-01-23
##  futile.options                      1.0.0    2010-04-06
##  GenomeInfoDb                      * 1.1.20   2014-09-25
##  GenomicAlignments                   1.1.30   2014-09-23
##  GenomicFeatures                   * 1.17.15  2014-09-18
##  GenomicFiles                        1.1.19   2014-08-26
##  GenomicRanges                     * 1.17.42  2014-09-23
##  GGally                              0.4.8    2014-08-26
##  ggbio                               1.13.13  2014-09-12
##  ggplot2                           * 1.0.0    2014-05-21
##  graph                               1.43.0   2014-04-12
##  gridExtra                         * 0.9.1    2012-08-09
##  gtable                              0.1.2    2012-12-05
##  Hmisc                               3.14.5   2014-09-12
##  htmltools                           0.2.6    2014-09-08
##  httr                                0.5      2014-09-02
##  IRanges                           * 1.99.28  2014-09-10
##  iterators                           1.0.7    2014-04-11
##  knitcitations                       1.0.2    2014-09-30
##  knitr                             * 1.6      2014-05-25
##  knitrBootstrap                      1.0.0    2014-09-30
##  labeling                            0.3      2014-08-23
##  lambda.r                            1.1.6    2014-01-23
##  lattice                             0.20.29  2014-04-04
##  latticeExtra                        0.6.26   2013-08-15
##  locfit                              1.5.9.1  2013-04-20
##  lubridate                           1.3.3    2013-12-31
##  markdown                            0.7.4    2014-08-24
##  MASS                                7.3.34   2014-08-21
##  Matrix                              1.1.4    2014-06-15
##  matrixStats                         0.10.0   2014-06-08
##  memoise                             0.2.1    2014-04-22
##  mgcv                              * 1.8.3    2014-08-29
##  mime                                0.1.2    2014-07-25
##  munsell                             0.4.2    2013-07-11
##  nlme                              * 3.1.117  2014-03-31
##  nnet                                7.3.8    2014-03-28
##  OrganismDbi                         1.7.5    2014-07-31
##  pkgmaker                            0.22     2014-05-14
##  plyr                                1.8.1    2014-02-26
##  proto                               0.3.10   2012-12-22
##  qvalue                              1.39.1   2014-05-27
##  R.methodsS3                         1.6.1    2014-01-05
##  RBGL                                1.41.1   2014-08-14
##  RColorBrewer                      * 1.0.5    2011-06-17
##  Rcpp                                0.11.2   2014-06-08
##  RCurl                               1.95.4.3 2014-07-29
##  RefManageR                          0.8.34   2014-08-19
##  regionReport                      * 0.99.3   2014-09-30
##  registry                            0.2      2012-01-24
##  reshape                             0.8.5    2014-04-23
##  reshape2                            1.4      2014-04-23
##  RJSONIO                             1.3.0    2014-07-28
##  rmarkdown                           0.3.3    2014-09-17
##  rngtools                            1.2.4    2014-03-06
##  rpart                               4.1.8    2014-03-28
##  Rsamtools                           1.17.34  2014-09-20
##  RSQLite                             0.11.4   2013-05-26
##  rstudioapi                          0.1      2014-03-27
##  rtracklayer                         1.25.16  2014-09-10
##  S4Vectors                         * 0.2.4    2014-09-14
##  scales                              0.2.4    2014-04-22
##  sendmailR                           1.2.1    2014-09-21
##  stringr                             0.6.2    2012-12-06
##  survival                            2.37.7   2014-01-22
##  TxDb.Hsapiens.UCSC.hg19.knownGene * 2.14.0   2014-03-20
##  VariantAnnotation                   1.11.35  2014-09-18
##  XML                                 3.98.1.1 2013-06-20
##  xtable                              1.7.4    2014-09-12
##  XVector                           * 0.5.8    2014-09-07
##  yaml                                2.1.13   2014-06-12
##  zlibbioc                            1.11.1   2014-04-19
##  source                                      
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##  Github (lcolladotor/derfinder@cc7af0a)      
##  Github (lcolladotor/derfinderHelper@54a9721)
##  Github (lcolladotor/derfinderPlot@50e8f51)  
##  CRAN (R 3.1.1)                              
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##  CRAN (R 3.1.1)                              
##  Bioconductor                                
##  CRAN (R 3.1.0)                              
##  Github (cboettig/knitcitations@f567713)     
##  CRAN (R 3.1.0)                              
##  Github (jimhester/knitrBootstrap@76c41f0)   
##  CRAN (R 3.1.1)                              
##  CRAN (R 3.1.0)                              
##  CRAN (R 3.1.0)                              
##  CRAN (R 3.1.0)                              
##  CRAN (R 3.1.0)                              
##  CRAN (R 3.1.0)                              
##  CRAN (R 3.1.1)                              
##  CRAN (R 3.1.1)                              
##  CRAN (R 3.1.0)                              
##  CRAN (R 3.1.0)                              
##  CRAN (R 3.1.0)                              
##  CRAN (R 3.1.1)                              
##  CRAN (R 3.1.1)                              
##  CRAN (R 3.1.0)                              
##  CRAN (R 3.1.0)                              
##  CRAN (R 3.1.0)                              
##  Bioconductor                                
##  CRAN (R 3.1.0)                              
##  CRAN (R 3.1.0)                              
##  CRAN (R 3.1.0)                              
##  Bioconductor                                
##  CRAN (R 3.1.0)                              
##  Bioconductor                                
##  CRAN (R 3.1.0)                              
##  CRAN (R 3.1.0)                              
##  CRAN (R 3.1.1)                              
##  CRAN (R 3.1.1)                              
##  Github (lcolladotor/regionReport@c980423)   
##  CRAN (R 3.1.0)                              
##  CRAN (R 3.1.0)                              
##  CRAN (R 3.1.0)                              
##  CRAN (R 3.1.1)                              
##  CRAN (R 3.1.1)                              
##  CRAN (R 3.1.0)                              
##  CRAN (R 3.1.0)                              
##  Bioconductor                                
##  CRAN (R 3.1.0)                              
##  CRAN (R 3.1.0)                              
##  Bioconductor                                
##  Bioconductor                                
##  CRAN (R 3.1.0)                              
##  CRAN (R 3.1.1)                              
##  CRAN (R 3.1.0)                              
##  CRAN (R 3.1.0)                              
##  Bioconductor                                
##  Bioconductor                                
##  CRAN (R 3.1.0)                              
##  CRAN (R 3.1.1)                              
##  Bioconductor                                
##  CRAN (R 3.1.0)                              
##  Bioconductor

[1] J. Allaire, J. McPherson, Y. Xie, H. Wickham, et al. rmarkdown: Dynamic Documents for R. R package version 0.3.3. 2014. URL: http://CRAN.R-project.org/package=rmarkdown.

[2] C. Boettiger. knitcitations: Citations for knitr markdown files. R package version 1.0.2. 2014. URL: https://github.com/cboettig/knitcitations.

[3] L. Collado-Torres, A. Frazee, A. Jaffe and J. Leek. derfinder: Fast differential expression analysis of RNA-seq data at base-pair resolution. R package version 0.99.2. 2014. URL: https://github.com/lcolladotor/derfinder.

[4] L. Collado-Torres, A. Jaffe and J. Leek. regionReport: Generate HTML reports for exploring a set of regions. R package version 0.99.3. 2014. URL: https://github.com/lcolladotor/regionReport.

[5] J. Hester. knitrBootstrap: Knitr Bootstrap framework. R package version 1.0.0. 2014. URL: https://github.com/jimhester/.

[6] H. Wickham. ggplot2: elegant graphics for data analysis. Springer New York, 2009. ISBN: 978-0-387-98140-6. URL: http://had.co.nz/ggplot2/book.

[7] Y. Xie. “knitr: A Comprehensive Tool for Reproducible Research in R”. In: Implementing Reproducible Computational Research. Ed. by V. Stodden, F. Leisch and R. D. Peng. ISBN 978-1466561595. Chapman and Hall/CRC, 2014. URL: http://www.crcpress.com/product/isbn/9781466561595.

[8] T. Yin, D. Cook and M. Lawrence. “ggbio: an R package for extending the grammar of graphics for genomic data”. In: Genome Biology 13.8 (2012), p. R77.