Basic genomic regions exploration

Project: Example DiffBind.

Introduction

This report is meant to help explore a set of genomic regions and was generated using the regionReport (Collado-Torres, Jaffe, and Leek, 2015) package. While the report is rich, it is meant to just start the exploration of the results and exemplify some of the code used to do so. If you need a more in-depth analysis for your specific data set you might want to use the customCode argument.

Most plots were made with using ggplot2 (Wickham, 2009).

Code setup

## knitrBoostrap and device chunk options
load_install('knitr')
opts_chunk$set(bootstrap.show.code = FALSE, dev = device)
if(!outputIsHTML) opts_chunk$set(bootstrap.show.code = FALSE, dev = device, echo = FALSE)

#### Libraries needed

## Bioconductor
load_install('bumphunter')
load_install('derfinder')
load_install('derfinderPlot')
load_install('GenomeInfoDb')
load_install('GenomicRanges')
load_install('ggbio')

## Transcription database to use by default
if(is.null(txdb)) {
    load_install('TxDb.Hsapiens.UCSC.hg19.knownGene')
    txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene::TxDb.Hsapiens.UCSC.hg19.knownGene
}

## CRAN
load_install('ggplot2')
if(!is.null(theme)) theme_set(theme)
load_install('grid')
load_install('gridExtra')
load_install('knitr')
load_install('RColorBrewer')
load_install('mgcv')
load_install('whisker')
load_install('DT')
load_install('devtools')

## Working behind the scenes
# load_install('knitcitations')
# load_install('rmarkdown')
## Optionally
# load_install('knitrBootstrap')

#### Code setup

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

## 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(hasSignificant) {
    ## Keep only those sig
    regions.df.sig <- regions.df[significantVar, ]
    keepChr <- table(regions.df.sig$seqnames) > 2
    regions.df.sig <- subset(regions.df.sig, seqnames %in% names(keepChr[keepChr]))
}

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

## areaVar initialize
areaVar <- NULL

Quality checks

for(i in seq_len(length(pvalueVars))) {
    densityVarName <- names(pvalueVars[i])
    densityVarName <- ifelse(is.null(densityVarName), pvalueVars[i], densityVarName)
   cat(knit_child(text = whisker.render(templatePvalueDensityInUse, list(varName = pvalueVars[i], densityVarName = densityVarName)), quiet = TRUE), sep = '\n')
}

Q-values

p1FDR <- ggplot(regions.df.plot, aes(x=FDR, colour=seqnames)) +
    geom_histogram(bins = 50, alpha=.5, position='identity') +
    xlim(c(0, 1.0005)) +
    labs(title='Histogram of Q-values') + 
    xlab('Q-values') +
    scale_colour_discrete(limits=chrs) + theme(legend.title=element_blank())
p1FDR

This plot shows the distribution of Q-values in a histogram. It might be skewed right or left, or flat as shown in the Wikipedia examples. The shape depends on the percent of regions that are differentially expressed. For further information on how to interpret a histogram of p-values check David Robinson’s post on this topic.

summary(mcols(regions)[['FDR']])

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.0147  0.1490  0.2787  0.4860  0.9990

This is the numerical summary of the distribution of the Q-values.

FDRtable <- 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(mcols(regions)[['FDR']] <= x))
})
FDRtable <- do.call(rbind, FDRtable)
if(outputIsHTML) {
    kable(FDRtable, format = 'markdown', align = c('c', 'c'))
} else {
    kable(FDRtable)
}

Cut	Count
0.0001	123
0.0010	299
0.0100	628
0.0250	829
0.0500	1018
0.1000	1259
0.2000	1567
0.3000	1791
0.4000	1975
0.5000	2149
0.6000	2305
0.7000	2432
0.8000	2565
0.9000	2701
1.0000	2844

This table shows the number of regions with Q-values less or equal than some commonly used cutoff values.

P-values

p1p.value <- ggplot(regions.df.plot, aes(x=p.value, colour=seqnames)) +
    geom_histogram(bins = 50, alpha=.5, position='identity') +
    xlim(c(0, 1.0005)) +
    labs(title='Histogram of P-values') + 
    xlab('P-values') +
    scale_colour_discrete(limits=chrs) + theme(legend.title=element_blank())
p1p.value

This plot shows the distribution of P-values in a histogram. It might be skewed right or left, or flat as shown in the Wikipedia examples. The shape depends on the percent of regions that are differentially expressed. For further information on how to interpret a histogram of p-values check David Robinson’s post on this topic.

summary(mcols(regions)[['p.value']])

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 0.0000003 0.0587000 0.2980000 0.3735000 0.6480000 0.9990000

This is the numerical summary of the distribution of the P-values.

p.valuetable <- 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(mcols(regions)[['p.value']] <= x))
})
p.valuetable <- do.call(rbind, p.valuetable)
if(outputIsHTML) {
    kable(p.valuetable, format = 'markdown', align = c('c', 'c'))
} else {
    kable(p.valuetable)
}

Cut	Count
0.0001	20
0.0010	63
0.0100	310
0.0250	494
0.0500	677
0.1000	886
0.2000	1170
0.3000	1428
0.4000	1650
0.5000	1821
0.6000	2026
0.7000	2222
0.8000	2406
0.9000	2607
1.0000	2844

This table shows the number of regions with P-values less or equal than some commonly used cutoff values.

Region width

xrange <- range(log10(regions.df.plot$width)) * c(0.95, 1.05)
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)

This plot shows the density of the region lengths for all regions. The bottom panel is restricted to significant regions.

for(i in seq_len(length(densityVars))) {
    densityVarName <- names(densityVars[i])
    densityVarName <- ifelse(is.null(densityVarName), densityVars[i], densityVarName)
   cat(knit_child(text = whisker.render(templateDensityInUse, list(varName = densityVars[i], densityVarName = densityVarName)), quiet = TRUE), sep = '\n')
}

Fold

xrange <- range(regions.df.plot[, 'Fold'])
p3aFold <- ggplot(regions.df.plot[is.finite(regions.df.plot[, 'Fold']), ], aes(x=Fold, fill=seqnames)) +
    geom_histogram(alpha=.5, position='identity', bins = 50) +
    labs(title='Histogram of Fold') +
    xlab('Fold') +
    xlim(xrange) + theme(legend.title=element_blank())
p3bFold <- ggplot(regions.df.sig[is.finite(regions.df.sig[, 'Fold']), ], aes(x=Fold, fill=seqnames)) +
    geom_histogram(alpha=.5, position='identity', bins = 50) +
    labs(title='Histogram of Fold (significant only)') +
    xlab('Fold') +
    xlim(xrange) + theme(legend.title=element_blank())
grid.arrange(p3aFold, p3bFold)

This plot shows the distribution of Fold in a histogram for all regions. The bottom panel is restricted to significant regions.

Mean concentration

xrange <- range(regions.df.plot[, 'Conc'])
p3aConc <- ggplot(regions.df.plot[is.finite(regions.df.plot[, 'Conc']), ], aes(x=Conc, fill=seqnames)) +
    geom_histogram(alpha=.5, position='identity', bins = 50) +
    labs(title='Histogram of Mean concentration') +
    xlab('Mean concentration') +
    xlim(xrange) + theme(legend.title=element_blank())
p3bConc <- ggplot(regions.df.sig[is.finite(regions.df.sig[, 'Conc']), ], aes(x=Conc, fill=seqnames)) +
    geom_histogram(alpha=.5, position='identity', bins = 50) +
    labs(title='Histogram of Mean concentration (significant only)') +
    xlab('Mean concentration') +
    xlim(xrange) + theme(legend.title=element_blank())
grid.arrange(p3aConc, p3bConc)

This plot shows the distribution of Mean concentration in a histogram for all regions. The bottom panel is restricted to significant regions.

Concentration (resistant)

xrange <- range(regions.df.plot[, 'Conc_Resistant'])
p3aConc_Resistant <- ggplot(regions.df.plot[is.finite(regions.df.plot[, 'Conc_Resistant']), ], aes(x=Conc_Resistant, fill=seqnames)) +
    geom_histogram(alpha=.5, position='identity', bins = 50) +
    labs(title='Histogram of Concentration (resistant)') +
    xlab('Concentration (resistant)') +
    xlim(xrange) + theme(legend.title=element_blank())
p3bConc_Resistant <- ggplot(regions.df.sig[is.finite(regions.df.sig[, 'Conc_Resistant']), ], aes(x=Conc_Resistant, fill=seqnames)) +
    geom_histogram(alpha=.5, position='identity', bins = 50) +
    labs(title='Histogram of Concentration (resistant) (significant only)') +
    xlab('Concentration (resistant)') +
    xlim(xrange) + theme(legend.title=element_blank())
grid.arrange(p3aConc_Resistant, p3bConc_Resistant)

This plot shows the distribution of Concentration (resistant) in a histogram for all regions. The bottom panel is restricted to significant regions.

Concentration (responsive)

xrange <- range(regions.df.plot[, 'Conc_Responsive'])
p3aConc_Responsive <- ggplot(regions.df.plot[is.finite(regions.df.plot[, 'Conc_Responsive']), ], aes(x=Conc_Responsive, fill=seqnames)) +
    geom_histogram(alpha=.5, position='identity', bins = 50) +
    labs(title='Histogram of Concentration (responsive)') +
    xlab('Concentration (responsive)') +
    xlim(xrange) + theme(legend.title=element_blank())
p3bConc_Responsive <- ggplot(regions.df.sig[is.finite(regions.df.sig[, 'Conc_Responsive']), ], aes(x=Conc_Responsive, fill=seqnames)) +
    geom_histogram(alpha=.5, position='identity', bins = 50) +
    labs(title='Histogram of Concentration (responsive) (significant only)') +
    xlab('Concentration (responsive)') +
    xlim(xrange) + theme(legend.title=element_blank())
grid.arrange(p3aConc_Responsive, p3bConc_Responsive)

This plot shows the distribution of Concentration (responsive) in a histogram for all regions. The bottom panel is restricted to significant regions.

Genomic overview

The following plots were made using ggbio (Yin, Cook, and Lawrence, 2012) which in turn uses ggplot2 (Wickham, 2009). For more details check plotOverview in derfinderPlot (Collado-Torres, Jaffe, and Leek, 2015).

P-values

## Choose what variable to show on the top
tmp <- regions
tmp$significant <- factor(significantVar, levels = c('TRUE', 'FALSE'))
if(!'area' %in% colnames(mcols(tmp))) {
    if(hasDensityVars) {
        tmp$area <- mcols(tmp)[[densityVars[1]]]
        areaVar <- densityVars[1]
        areaVar <- ifelse(is.null(names(areaVar)), densityVars[1], names(areaVar))
    } else {
        tmp$area <- 0
        areaVar <- NULL
    }
} else {
    areaVar <- 'area'
}
plotOverview(regions=tmp, type='pval', base_size=overviewParams$base_size, areaRel=overviewParams$areaRel, legend.position=c(0.97, 0.12))

rm(tmp)

This plot shows the genomic locations of the regions found in the analysis. The significant regions are highlighted and the Fold of the regions is shown on top of each chromosome (shown in a relative scale).

for(i in seq_len(length(pvalueVars))) {
    densityVarName <- names(pvalueVars[i])
    densityVarName <- ifelse(is.null(densityVarName), pvalueVars[i], densityVarName)
   cat(knit_child(text = whisker.render(templateManhattanInUse, list(varName = pvalueVars[i], densityVarName = densityVarName)), quiet = TRUE), sep = '\n')
}

## using coord:genome to parse x scale

Manhattan Q-values

regions.manhattan <- regions
mcols(regions.manhattan)[['FDR']] <- - log(mcols(regions.manhattan)[['FDR']], base = 10)
pManFDR <- plotGrandLinear(regions.manhattan, aes(y = FDR, colour = seqnames)) + theme(axis.text.x=element_text(angle=-90, hjust=0)) + ylab('-log10 Q-values')
pManFDR

rm(regions.manhattan)

This is a Manhattan plot for the Q-values for all regions. A single dot is shown for each region, where higher values in the y-axis mean that the Q-values are closer to zero.

## using coord:genome to parse x scale

Manhattan P-values

regions.manhattan <- regions
mcols(regions.manhattan)[['p.value']] <- - log(mcols(regions.manhattan)[['p.value']], base = 10)
pManp.value <- plotGrandLinear(regions.manhattan, aes(y = p.value, colour = seqnames)) + theme(axis.text.x=element_text(angle=-90, hjust=0)) + ylab('-log10 P-values')
pManp.value

rm(regions.manhattan)

This is a Manhattan plot for the P-values for all regions. A single dot is shown for each region, where higher values in the y-axis mean that the P-values are closer to zero.

Annotation

## Annotate regions with bumphunter
if(is.null(annotation)) {
    genes <- annotateTranscripts(txdb = txdb)
    annotation <- matchGenes(x = regions, subject = genes)
}


## Make the plot
plotOverview(regions=regions, annotation=annotation, type='annotation', base_size=overviewParams$base_size, areaRel=overviewParams$areaRel, legend.position=c(0.97, 0.12))

This genomic overview plot shows the annotation region type for the regions as determined using bumphunter (Jaffe, Murakami, Lee, Leek, et al., 2012). Note that the regions are shown only if the annotation information is available. Below is a table of the actual number of results per annotation region type.

annoReg <- table(annotation$region, useNA='always')
annoReg.df <- data.frame(Region=names(annoReg), Count=as.vector(annoReg))
if(outputIsHTML) {
    kable(annoReg.df, format = 'markdown', align=rep('c', 3))
} else {
    kable(annoReg.df)
}

Region	Count
upstream	726
promoter	41
overlaps 5’	32
inside	1333
overlaps 3’	4
close to 3’	0
downstream	707
covers	1
NA	0

Annotation (significant)

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

This genomic overview plot shows the annotation region type for the statistically significant regions. Note that the regions are shown only if the annotation information is available.

Best regions

Genomic states

Below is a table summarizing the number of genomic states per region as determined using derfinder (Collado-Torres, Frazee, Love, Irizarry, et al., 2015).

## Construct genomic state object
genomicState <- makeGenomicState(txdb = txdb, chrs = chrs, verbose = FALSE)

## 'select()' returned 1:1 mapping between keys and columns

## Annotate regions by genomic state
annotatedRegions <- annotateRegions(regions, genomicState$fullGenome, verbose = FALSE)

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

Number of Overlapping States	Frequency	State
0	2667	exon
1	167	exon
2	10	exon
0	1360	intergenic
1	1482	intergenic
2	2	intergenic
0	1497	intron
1	1256	intron
2	89	intron
3	2	intron

The following is a venn diagram showing how many regions overlap known exons, introns, and intergenic segments, none of them, or multiple of these groups.

## Venn diagram for all regions
venn <- vennRegions(annotatedRegions, counts.col = 'blue', 
    main = 'Regions overlapping genomic states')

The following plot is the genomic states venn diagram only for the significant regions.

## Venn diagram for all regions
vennSig <- vennRegions(annotatedRegions, counts.col = 'blue', 
    main = 'Significant regions overlapping genomic states',
    subsetIndex = significantVar)

Region information

Below is an interactive table with the top 100 regions (out of 2844) as ranked by p-value . Inf and -Inf are shown as 1e100 and -1e100 respectively. Use the search function to find your region of interest or sort by one of the columns.

## Add annotation information
regions.df <- cbind(regions.df, annotation)

## Rank by p-value (first pvalue variable supplied)
if(hasPvalueVars){
    topRegions <- head(regions.df[order(regions.df[, pvalueVars[1]], 
        decreasing = FALSE), ], nBestRegions)
    topRegions <- cbind(data.frame('pvalueRank' = seq_len(nrow(topRegions))), 
        topRegions)
} else {
    topRegions <- head(regions.df, nBestRegions)
}

## 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)
}
topRegions <- replaceInf(topRegions, which(sapply(topRegions, function(x) {
    class(x) %in% c('numeric', 'integer')})))

## Make the table
greptext <- 'value$|area$|mean|log2FoldChange'
greppval <- 'pvalues$|qvalues$|fwer$'
if(hasPvalueVars) {
    greppval <- paste0(paste(pvalueVars, collapse = '$|'), '$|', greppval)
}
if(hasDensityVars) {
    greptext <- paste0(paste(densityVars, collapse = '$|'), '$|', greptext)
}

for(i in which(grepl(greppval, colnames(topRegions)))) topRegions[, i] <- format(topRegions[, i], scientific = TRUE)

if(outputIsHTML) {
    datatable(topRegions, options = list(pagingType='full_numbers', pageLength=10, scrollX='100%'), rownames = FALSE) %>% formatRound(which(grepl(greptext, colnames(topRegions))), digits)
} else {
    ## Only print the top part if your output is a PDF file
    df_top <- head(topRegions, 20)
    for(i in which(grepl(greptext, colnames(topRegions)))) df_top[, i] <- round(df_top[, i], digits)
    kable(df_top)
}

MA plot

The following is a MA plot of the resistant-responsive contrast.

library('DiffBind')
dba.plotMA(tamoxifen)

PCA plot

All

The following graphic is a PCA plot using the affinity data for all sites.

dba.plotPCA(tamoxifen, DBA_TISSUE, label = DBA_CONDITION)

Only DB

The following graphic is a PCA plot only using the differentially bound sites.

dba.plotPCA(tamoxifen, contrast = 1, th = .05, label = DBA_TISSUE)

The MA and PCA plots are further described in the DiffBind vignette.

Reproducibility

This report was generated in path /Users/lcollado/Dropbox/JHSPH/Code/regionReportSupp using the following call to renderReport():

## renderReport(regions = regions, project = "Example DiffBind", 
##     pvalueVars = c(`Q-values` = "FDR", `P-values` = "p-value"), 
##     densityVars = c(Fold = "Fold", `Mean concentration` = "Conc", 
##         `Concentration (resistant)` = "Conc_Resistant", `Concentration (responsive)` = "Conc_Responsive"), 
##     significantVar = regions$FDR < 0.1, annotation = annotation, 
##     nBestRegions = 100, customCode = file.path(getwd(), "DiffBind_custom.Rmd"), 
##     outdir = "DiffBind-example", output = "index", densityTemplates = densityTemplates)

Date the report was generated.

## [1] "2016-04-12 07:37:53 EDT"

Wallclock time spent generating the report.

## Time difference of 1.933 mins

R session information.

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

##  setting  value                                    
##  version  R version 3.3.0 alpha (2016-03-23 r70368)
##  system   x86_64, darwin13.4.0                     
##  ui       X11                                      
##  language (EN)                                     
##  collate  en_US.UTF-8                              
##  tz       America/New_York                         
##  date     2016-04-12

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

##  package                           * version  date       source                                   
##  acepack                             1.3-3.3  2014-11-24 CRAN (R 3.3.0)                           
##  amap                                0.8-14   2014-12-17 CRAN (R 3.3.0)                           
##  annotate                            1.49.1   2016-02-06 Bioconductor                             
##  AnnotationDbi                     * 1.33.8   2016-04-10 Bioconductor                             
##  AnnotationForge                     1.13.13  2016-04-01 Bioconductor                             
##  AnnotationHub                       2.3.16   2016-03-25 Bioconductor                             
##  assertthat                          0.1      2013-12-06 CRAN (R 3.3.0)                           
##  backports                           1.0.2    2016-03-18 CRAN (R 3.3.0)                           
##  base64enc                           0.1-3    2015-07-28 CRAN (R 3.3.0)                           
##  BatchJobs                           1.6      2015-03-18 CRAN (R 3.3.0)                           
##  BBmisc                              1.9      2015-02-03 CRAN (R 3.3.0)                           
##  bibtex                              0.4.0    2014-12-31 CRAN (R 3.3.0)                           
##  Biobase                           * 2.31.3   2016-01-14 Bioconductor                             
##  BiocGenerics                      * 0.17.4   2016-04-07 Bioconductor                             
##  BiocInstaller                       1.21.4   2016-03-23 Bioconductor                             
##  BiocParallel                        1.5.21   2016-03-23 Bioconductor                             
##  biomaRt                             2.27.2   2016-01-14 Bioconductor                             
##  Biostrings                        * 2.39.12  2016-02-21 Bioconductor                             
##  biovizBase                          1.19.6   2016-04-06 Bioconductor                             
##  bitops                              1.0-6    2013-08-17 CRAN (R 3.3.0)                           
##  brew                                1.0-6    2011-04-13 CRAN (R 3.3.0)                           
##  BSgenome                            1.39.4   2016-02-21 Bioconductor                             
##  bumphunter                        * 1.11.5   2016-03-29 Bioconductor                             
##  Category                            2.37.1   2016-01-14 Bioconductor                             
##  caTools                             1.17.1   2014-09-10 CRAN (R 3.3.0)                           
##  checkmate                           1.7.4    2016-04-08 CRAN (R 3.3.0)                           
##  cluster                             2.0.3    2015-07-21 CRAN (R 3.3.0)                           
##  codetools                           0.2-14   2015-07-15 CRAN (R 3.3.0)                           
##  colorout                          * 1.1-2    2016-03-24 Github (jalvesaq/colorout@f96c00c)       
##  colorspace                          1.2-6    2015-03-11 CRAN (R 3.3.0)                           
##  DBI                               * 0.3.1    2014-09-24 CRAN (R 3.3.0)                           
##  DEFormats                           0.99.8   2016-03-31 Bioconductor                             
##  derfinder                         * 1.5.30   2016-03-25 Bioconductor                             
##  derfinderHelper                     1.5.3    2016-03-23 Bioconductor                             
##  derfinderPlot                     * 1.5.7    2016-03-23 Bioconductor                             
##  DESeq2                              1.11.42  2016-04-10 Bioconductor                             
##  devtools                          * 1.10.0   2016-01-23 CRAN (R 3.3.0)                           
##  dichromat                           2.0-0    2013-01-24 CRAN (R 3.3.0)                           
##  DiffBind                          * 1.99.16  2016-03-17 Bioconductor                             
##  digest                              0.6.9    2016-01-08 CRAN (R 3.3.0)                           
##  doRNG                               1.6      2014-03-07 CRAN (R 3.3.0)                           
##  dplyr                               0.4.3    2015-09-01 CRAN (R 3.3.0)                           
##  DT                                * 0.1      2015-06-09 CRAN (R 3.3.0)                           
##  edgeR                               3.13.8   2016-04-08 Bioconductor                             
##  ensembldb                           1.3.19   2016-04-03 Bioconductor                             
##  evaluate                            0.8.3    2016-03-05 CRAN (R 3.3.0)                           
##  fail                                1.3      2015-10-01 CRAN (R 3.3.0)                           
##  foreach                           * 1.4.3    2015-10-13 CRAN (R 3.3.0)                           
##  foreign                             0.8-66   2015-08-19 CRAN (R 3.3.0)                           
##  formatR                             1.3      2016-03-05 CRAN (R 3.3.0)                           
##  Formula                             1.2-1    2015-04-07 CRAN (R 3.3.0)                           
##  gdata                               2.17.0   2015-07-04 CRAN (R 3.3.0)                           
##  genefilter                          1.53.3   2016-03-23 Bioconductor                             
##  geneplotter                         1.49.0   2016-01-14 Bioconductor                             
##  GenomeInfoDb                      * 1.7.6    2016-01-29 Bioconductor                             
##  GenomicAlignments                 * 1.7.20   2016-02-25 Bioconductor                             
##  GenomicFeatures                   * 1.23.29  2016-04-05 Bioconductor                             
##  GenomicFiles                        1.7.9    2016-02-22 Bioconductor                             
##  GenomicRanges                     * 1.23.25  2016-03-31 Bioconductor                             
##  GGally                              1.0.1    2016-01-14 CRAN (R 3.3.0)                           
##  ggbio                             * 1.19.13  2016-04-03 Bioconductor                             
##  ggplot2                           * 2.1.0    2016-03-01 CRAN (R 3.3.0)                           
##  GO.db                               3.3.0    2016-04-11 Bioconductor                             
##  GOstats                             2.37.0   2016-01-14 Bioconductor                             
##  gplots                              3.0.1    2016-03-30 CRAN (R 3.3.0)                           
##  graph                               1.49.1   2016-01-14 Bioconductor                             
##  gridExtra                         * 2.2.1    2016-02-29 CRAN (R 3.3.0)                           
##  GSEABase                            1.33.0   2016-01-14 Bioconductor                             
##  gtable                              0.2.0    2016-02-26 CRAN (R 3.3.0)                           
##  gtools                              3.5.0    2015-05-29 CRAN (R 3.3.0)                           
##  highr                               0.5.1    2015-09-18 CRAN (R 3.3.0)                           
##  Hmisc                               3.17-3   2016-04-03 CRAN (R 3.3.0)                           
##  htmltools                           0.3.5    2016-03-21 CRAN (R 3.3.0)                           
##  htmlwidgets                         0.6      2016-02-25 CRAN (R 3.3.0)                           
##  httpuv                              1.3.3    2015-08-04 CRAN (R 3.3.0)                           
##  httr                                1.1.0    2016-01-28 CRAN (R 3.3.0)                           
##  hwriter                             1.3.2    2014-09-10 CRAN (R 3.3.0)                           
##  interactiveDisplayBase              1.9.0    2016-01-14 Bioconductor                             
##  IRanges                           * 2.5.43   2016-04-10 Bioconductor                             
##  iterators                         * 1.0.8    2015-10-13 CRAN (R 3.3.0)                           
##  jsonlite                            0.9.19   2015-11-28 CRAN (R 3.3.0)                           
##  KernSmooth                          2.23-15  2015-06-29 CRAN (R 3.3.0)                           
##  knitcitations                       1.0.7    2015-10-28 CRAN (R 3.3.0)                           
##  knitr                             * 1.12.3   2016-01-22 CRAN (R 3.3.0)                           
##  knitrBootstrap                      1.0.0    2016-03-24 Github (jimhester/knitrBootstrap@cdaa4a9)
##  labeling                            0.3      2014-08-23 CRAN (R 3.3.0)                           
##  lattice                             0.20-33  2015-07-14 CRAN (R 3.3.0)                           
##  latticeExtra                        0.6-28   2016-02-09 CRAN (R 3.3.0)                           
##  limma                             * 3.27.14  2016-03-23 Bioconductor                             
##  locfit                            * 1.5-9.1  2013-04-20 CRAN (R 3.3.0)                           
##  lubridate                           1.5.6    2016-04-06 CRAN (R 3.3.0)                           
##  magrittr                            1.5      2014-11-22 CRAN (R 3.3.0)                           
##  markdown                            0.7.7    2015-04-22 CRAN (R 3.3.0)                           
##  Matrix                              1.2-4    2016-03-02 CRAN (R 3.3.0)                           
##  matrixStats                         0.50.1   2015-12-15 CRAN (R 3.3.0)                           
##  memoise                             1.0.0    2016-01-29 CRAN (R 3.3.0)                           
##  mgcv                              * 1.8-12   2016-03-03 CRAN (R 3.3.0)                           
##  mime                                0.4      2015-09-03 CRAN (R 3.3.0)                           
##  munsell                             0.4.3    2016-02-13 CRAN (R 3.3.0)                           
##  nlme                              * 3.1-126  2016-03-14 CRAN (R 3.3.0)                           
##  nnet                                7.3-12   2016-02-02 CRAN (R 3.3.0)                           
##  org.Hs.eg.db                      * 3.3.0    2016-04-11 Bioconductor                             
##  OrganismDbi                         1.13.6   2016-04-05 Bioconductor                             
##  pheatmap                            1.0.8    2015-12-11 CRAN (R 3.3.0)                           
##  pkgmaker                            0.22     2014-05-14 CRAN (R 3.3.0)                           
##  plyr                                1.8.3    2015-06-12 CRAN (R 3.3.0)                           
##  qvalue                              2.3.2    2016-01-14 Bioconductor                             
##  R6                                  2.1.2    2016-01-26 CRAN (R 3.3.0)                           
##  RBGL                                1.47.0   2016-01-14 Bioconductor                             
##  RColorBrewer                      * 1.1-2    2014-12-07 CRAN (R 3.3.0)                           
##  Rcpp                                0.12.4   2016-03-26 CRAN (R 3.3.0)                           
##  RCurl                               1.95-4.8 2016-03-01 CRAN (R 3.3.0)                           
##  RefManageR                          0.10.13  2016-04-04 CRAN (R 3.3.0)                           
##  regionReport                      * 1.5.47   2016-04-12 Bioconductor                             
##  registry                            0.3      2015-07-08 CRAN (R 3.3.0)                           
##  reshape                             0.8.5    2014-04-23 CRAN (R 3.3.0)                           
##  reshape2                            1.4.1    2014-12-06 CRAN (R 3.3.0)                           
##  rjson                               0.2.15   2014-11-03 CRAN (R 3.3.0)                           
##  RJSONIO                             1.3-0    2014-07-28 CRAN (R 3.3.0)                           
##  rmarkdown                           0.9.5    2016-02-22 CRAN (R 3.3.0)                           
##  rngtools                            1.2.4    2014-03-06 CRAN (R 3.3.0)                           
##  rpart                               4.1-10   2015-06-29 CRAN (R 3.3.0)                           
##  Rsamtools                         * 1.23.8   2016-04-10 Bioconductor                             
##  RSQLite                           * 1.0.0    2014-10-25 CRAN (R 3.3.0)                           
##  rtracklayer                         1.31.10  2016-04-07 Bioconductor                             
##  S4Vectors                         * 0.9.46   2016-04-07 Bioconductor                             
##  scales                              0.4.0    2016-02-26 CRAN (R 3.3.0)                           
##  sendmailR                           1.2-1    2014-09-21 CRAN (R 3.3.0)                           
##  shiny                               0.13.2   2016-03-28 CRAN (R 3.3.0)                           
##  ShortRead                           1.29.5   2016-03-16 Bioconductor                             
##  stringi                             1.0-1    2015-10-22 CRAN (R 3.3.0)                           
##  stringr                             1.0.0    2015-04-30 CRAN (R 3.3.0)                           
##  SummarizedExperiment              * 1.1.23   2016-04-06 Bioconductor                             
##  survival                            2.38-3   2015-07-02 CRAN (R 3.3.0)                           
##  systemPipeR                         1.5.9    2016-02-02 Bioconductor                             
##  TxDb.Hsapiens.UCSC.hg19.knownGene * 3.2.2    2016-03-24 Bioconductor                             
##  VariantAnnotation                   1.17.23  2016-04-07 Bioconductor                             
##  whisker                           * 0.3-2    2013-04-28 CRAN (R 3.3.0)                           
##  XML                                 3.98-1.4 2016-03-01 CRAN (R 3.3.0)                           
##  xtable                              1.8-2    2016-02-05 CRAN (R 3.3.0)                           
##  XVector                           * 0.11.8   2016-04-06 Bioconductor                             
##  yaml                                2.1.13   2014-06-12 CRAN (R 3.3.0)                           
##  zlibbioc                            1.17.1   2016-03-19 Bioconductor

Pandoc version used: 1.17.0.3.

Bibliography

This report was created with regionReport (Collado-Torres, Jaffe, and Leek, 2015) using rmarkdown (Allaire, Cheng, Xie, McPherson, et al., 2016) while knitr (Xie, 2014) and DT (Xie, 2015) were running behind the scenes. whisker (de Jonge, 2013) was used for creating templates for the pvalueVars and densityVars.

Citations made with knitcitations (Boettiger, 2015). The BibTeX file can be found here.

[1] J. Allaire, J. Cheng, Y. Xie, J. McPherson, et al. rmarkdown: Dynamic Documents for R. R package version 0.9.5. 2016. URL: https://CRAN.R-project.org/package=rmarkdown.

[2] C. Boettiger. knitcitations: Citations for ‘Knitr’ Markdown Files. R package version 1.0.7. 2015. URL: https://CRAN.R-project.org/package=knitcitations.

[3] L. Collado-Torres, A. C. Frazee, M. I. Love, R. A. Irizarry, et al. “derfinder: Software for annotation-agnostic RNA-seq differential expression analysis”. In: bioRxiv (2015). DOI: 10.1101/015370. URL: http://www.biorxiv.org/content/early/2015/02/19/015370.abstract.

[4] L. Collado-Torres, A. E. Jaffe and J. T. Leek. derfinderPlot: Plotting functions for derfinder. https://github.com/leekgroup/derfinderPlot - R package version 1.5.7. 2015. URL: http://www.bioconductor.org/packages/derfinderPlot.

[5] L. Collado-Torres, A. E. Jaffe and J. T. Leek. “regionReport: Interactive reports for region-based analyses”. In: F1000Research 4 (2015), p. 105. DOI: 10.12688/f1000research.6379.1. URL: http://f1000research.com/articles/4-105/v1.

[6] E. de Jonge. whisker: mustache for R, logicless templating. R package version 0.3-2. 2013. URL: https://CRAN.R-project.org/package=whisker.

[7] A. E. Jaffe, P. Murakami, H. Lee, J. T. Leek, et al. “Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies”. In: International journal of epidemiology 41.1 (2012), pp. 200–209. DOI: 10.1093/ije/dyr238.

[8] H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2009. ISBN: 978-0-387-98140-6. URL: http://ggplot2.org.

[9] Y. Xie. DT: A Wrapper of the JavaScript Library ‘DataTables’. R package version 0.1. 2015. URL: https://CRAN.R-project.org/package=DT.

[10] 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.

[11] 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.