DiffBind exampleThe DiffBind package can be used for ChIP-seq analyses where you are interested in identifying differentially bound sites. The vignette explains in greater detail the data set we are using in this example.
## Load DiffBind
library('DiffBind')## Loading required package: GenomicRanges## Loading required package: methods## Loading required package: BiocGenerics## Loading required package: parallel##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:parallel':
##
## clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
## clusterExport, clusterMap, parApply, parCapply, parLapply,
## parLapplyLB, parRapply, parSapply, parSapplyLB## The following objects are masked from 'package:stats':
##
## IQR, mad, xtabs## The following objects are masked from 'package:base':
##
## anyDuplicated, append, as.data.frame, cbind, colnames,
## do.call, duplicated, eval, evalq, Filter, Find, get, grep,
## grepl, intersect, is.unsorted, lapply, lengths, Map, mapply,
## match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
## Position, rank, rbind, Reduce, rownames, sapply, setdiff,
## sort, table, tapply, union, unique, unsplit## Loading required package: S4Vectors## Loading required package: stats4##
## Attaching package: 'S4Vectors'## The following objects are masked from 'package:base':
##
## colMeans, colSums, expand.grid, rowMeans, rowSums## Loading required package: IRanges## Loading required package: GenomeInfoDb## Loading required package: SummarizedExperiment## Loading required package: Biobase## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.## Loading required package: limma##
## Attaching package: 'limma'## The following object is masked from 'package:BiocGenerics':
##
## plotMA## Loading required package: GenomicAlignments## Loading required package: Biostrings## Loading required package: XVector## Loading required package: Rsamtools## Loading required package: locfit## locfit 1.5-9.1 2013-03-22##
## Attaching package: 'locfit'## The following objects are masked from 'package:GenomicAlignments':
##
## left, right## Loading required package: DBI## ## Retrieve differentially bounded sites
data(tamoxifen_analysis)
regions <- dba.report(tamoxifen, th = 1)
## Explore quickly
regions## GRanges object with 2844 ranges and 6 metadata columns:
## seqnames ranges strand | Conc Conc_Resistant
## <Rle> <IRanges> <Rle> | <numeric> <numeric>
## 1291 chr18 [34597700, 34598200] * | 5.33 0.02
## 2452 chr18 [64490684, 64491184] * | 6.36 1.39
## 2571 chr18 [69433116, 69433616] * | 4.57 -0.79
## 2771 chr18 [74536113, 74536613] * | 3.93 -0.79
## 976 chr18 [26860992, 26861492] * | 7.3 3.1
## ... ... ... ... . ... ...
## 988 chr18 [27791009, 27791509] * | 2.96 2.98
## 1218 chr18 [32870115, 32870615] * | 3.14 3.12
## 2236 chr18 [58952123, 58952623] * | 4.6 4.6
## 2743 chr18 [74088136, 74088636] * | 2.7 2.7
## 1046 chr18 [29078365, 29078865] * | 2.11 2.1
## Conc_Responsive Fold p-value FDR
## <numeric> <numeric> <numeric> <numeric>
## 1291 5.97 -5.95 1.24e-10 3.21e-07
## 2452 7 -5.61 2.26e-10 3.21e-07
## 2571 5.21 -6 3.59e-09 3.41e-06
## 2771 4.57 -5.35 6.56e-09 4.67e-06
## 976 7.92 -4.82 8.74e-09 4.97e-06
## ... ... ... ... ...
## 988 2.94 0.04 0.997 0.999
## 1218 3.14 -0.02 0.998 0.999
## 2236 4.6 0 0.998 0.999
## 2743 2.7 0.01 0.998 0.999
## 1046 2.11 -0.01 0.999 0.999
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths## Note that the chromosome information is missing
seqlengths(regions)## chr18
## NA## Add chromosome length
data(hg19Ideogram, package = 'biovizBase')
seqlengths(regions) <- seqlengths(hg19Ideogram)[names(seqlengths(regions))]
## Check new length
seqlengths(regions)## chr18
## 78077248The DiffBind vignette illustrates functions for creating MA and PCA plots using results from this package. Since these are custom plots for this analysis, we can include them in our report by writing a short child R Markdown file: DiffBind_custom.Rmd.
renderReport() relies on the output from bumphunter::matchGenes(), which can take a considerable amount to compute. So you might want to run this before using renderReport() and save the information just in case you need it.
library('TxDb.Hsapiens.UCSC.hg19.knownGene')## Loading required package: GenomicFeatures## Loading required package: AnnotationDbilibrary('bumphunter')## Loading required package: foreach## Loading required package: iteratorstxdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
genes <- annotateTranscripts(txdb = txdb)## Warning: Calling species() on a TxDb object is *deprecated*.
## Please use organism() instead.## No annotationPackage supplied. Trying org.Hs.eg.db.## Loading required package: org.Hs.eg.db## ## Getting TSS and TSE.## Getting CSS and CSE.## Getting exons.## Annotating genes.system.time( annotation <- matchGenes(x = regions, subject = genes) )## ............................## user system elapsed
## 146.944 2.040 156.441## Save for future use
save(annotation, file = 'annotation-DiffBind.Rdata')renderReport() allows you to change templates for the distribution plots. By default these are density plots, but lets say that you prefer plotting histograms. Density plots can be useful when you have regions from many chromosomes, but in this case there’s only data for chromosome 18. First, lets explore the default templates.
## Load regionReport
library('regionReport')
cat(templateDensity)##
## ## {{{densityVarName}}}
##
## ```{r density-{{{varName}}}, fig.width=14, fig.height=14, eval=hasSignificant, echo=hasSignificant}
## xrange <- range(regions.df.plot[, '{{{varName}}}']) * c(0.95, 1.05)
## p3a{{{varName}}} <- ggplot(regions.df.plot[is.finite(regions.df.plot[, '{{{varName}}}']), ], aes(x={{{varName}}}, colour=seqnames)) +
## geom_line(stat='density') + labs(title='Density of {{{densityVarName}}}') +
## xlab('{{{densityVarName}}}') + scale_colour_discrete(limits=chrs) +
## xlim(xrange) + theme(legend.title=element_blank())
## p3b{{{varName}}} <- ggplot(regions.df.sig[is.finite(regions.df.sig[, '{{{varName}}}']), ], aes(x={{{varName}}}, colour=seqnames)) +
## geom_line(stat='density') +
## labs(title='Density of {{{densityVarName}}} (significant only)') +
## xlab('{{{densityVarName}}}') + scale_colour_discrete(limits=chrs) +
## xlim(xrange) + theme(legend.title=element_blank())
## grid.arrange(p3a{{{varName}}}, p3b{{{varName}}})
## ```
##
## ```{r density-solo-{{{varName}}}, fig.width=10, fig.height=10, eval=!hasSignificant, echo=!hasSignificant}
## p3a{{{varName}}} <- ggplot(regions.df.plot[is.finite(regions.df.plot[, '{{{varName}}}']), ], aes(x={{{varName}}}, colour=seqnames)) +
## geom_line(stat='density') + labs(title='Density of {{{densityVarName}}}') +
## xlab('{{{densityVarName}}}') + scale_colour_discrete(limits=chrs) +
## theme(legend.title=element_blank())
## p3a{{{varName}}}
## ```
##
## This plot shows the density of the {{{densityVarName}}} for all regions. `r ifelse(hasSignificant, 'The bottom panel is restricted to significant regions.', '')`cat(templatePvalueDensity)##
## ## {{{densityVarName}}}
##
## ```{r pval-density-{{{varName}}}, fig.width=10, fig.height=10}
## p1{{{varName}}} <- ggplot(regions.df.plot, aes(x={{{varName}}}, colour=seqnames)) +
## geom_line(stat='density') + xlim(0, 1) +
## labs(title='Density of {{{densityVarName}}}') + xlab('{{{densityVarName}}}') +
## scale_colour_discrete(limits=chrs) + theme(legend.title=element_blank())
## p1{{{varName}}}
## ```
##
##
## ```{r 'pval-summary-{{{varName}}}'}
## summary(mcols(regions)[['{{{varName}}}']])
## ```
##
##
## This is the numerical summary of the distribution of the {{{densityVarName}}}.
##
## ```{r pval-tableSummary-{{{varName}}}, results='asis'}
## {{{varName}}}table <- 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)[['{{{varName}}}']] <= x))
## })
## {{{varName}}}table <- do.call(rbind, {{{varName}}}table)
## if(outputIsHTML) {
## kable({{{varName}}}table, format = 'markdown', align = c('c', 'c'))
## } else {
## kable({{{varName}}}table)
## }
## ```
##
## This table shows the number of regions with {{{densityVarName}}} less or equal than some commonly used cutoff values.regionReport includes other templates you might want to use such as histogram templates to replace the density plots.
## Lets check the alternative templates
cat(templateHistogram)##
##
## ## {{{densityVarName}}}
##
## ```{r histogram-{{{varName}}}, fig.width=14, fig.height=14, eval=hasSignificant, echo=hasSignificant}
## xrange <- range(regions.df.plot[, '{{{varName}}}'])
## p3a{{{varName}}} <- ggplot(regions.df.plot[is.finite(regions.df.plot[, '{{{varName}}}']), ], aes(x={{{varName}}}, fill=seqnames)) +
## geom_histogram(alpha=.5, position='identity', bins = 50) +
## labs(title='Histogram of {{{densityVarName}}}') +
## xlab('{{{densityVarName}}}') +
## xlim(xrange) + theme(legend.title=element_blank())
## p3b{{{varName}}} <- ggplot(regions.df.sig[is.finite(regions.df.sig[, '{{{varName}}}']), ], aes(x={{{varName}}}, fill=seqnames)) +
## geom_histogram(alpha=.5, position='identity', bins = 50) +
## labs(title='Histogram of {{{densityVarName}}} (significant only)') +
## xlab('{{{densityVarName}}}') +
## xlim(xrange) + theme(legend.title=element_blank())
## grid.arrange(p3a{{{varName}}}, p3b{{{varName}}})
## ```
##
## ```{r histogram-solo-{{{varName}}}, fig.width=10, fig.height=10, eval=!hasSignificant, echo=!hasSignificant}
## p3a{{{varName}}} <- ggplot(regions.df.plot[is.finite(regions.df.plot[, '{{{varName}}}']), ], aes(x={{{varName}}}, fill=seqnames)) +
## geom_histogram(alpha=.5, position='identity', bins = 50) +
## labs(title='Histogram of {{{densityVarName}}}') +
## xlab('{{{densityVarName}}}') +
## theme(legend.title=element_blank())
## p3a{{{varName}}}
## ```
##
## This plot shows the distribution of {{{densityVarName}}} in a histogram for all regions. `r ifelse(hasSignificant, 'The bottom panel is restricted to significant regions.', '')`cat(templatePvalueHistogram)##
## ## {{{densityVarName}}}
##
## ```{r histPval-{{{varName}}}, fig.width=10, fig.height=10}
## p1{{{varName}}} <- ggplot(regions.df.plot, aes(x={{{varName}}}, colour=seqnames)) +
## geom_histogram(bins = 50, alpha=.5, position='identity') +
## xlim(c(0, 1.0005)) +
## labs(title='Histogram of {{{densityVarName}}}') +
## xlab('{{{densityVarName}}}') +
## scale_colour_discrete(limits=chrs) + theme(legend.title=element_blank())
## p1{{{varName}}}
## ```
##
## This plot shows the distribution of {{{densityVarName}}} in a histogram. It might be skewed right or left, or flat as shown in the [Wikipedia examples](https://en.wikipedia.org/wiki/Histogram#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](http://varianceexplained.org/statistics/interpreting-pvalue-histogram/).
##
##
## ```{r 'summary-{{{varName}}}'}
## summary(mcols(regions)[['{{{varName}}}']])
## ```
##
##
## This is the numerical summary of the distribution of the {{{densityVarName}}}.
##
## ```{r tableSummary-{{{varName}}}, results='asis'}
## {{{varName}}}table <- 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)[['{{{varName}}}']] <= x))
## })
## {{{varName}}}table <- do.call(rbind, {{{varName}}}table)
## if(outputIsHTML) {
## kable({{{varName}}}table, format = 'markdown', align = c('c', 'c'))
## } else {
## kable({{{varName}}}table)
## }
## ```
##
## This table shows the number of regions with {{{densityVarName}}} less or equal than some commonly used cutoff values.## Define our list of templates
densityTemplates <- list(
Pvalue = templatePvalueHistogram,
Common = templateHistogram,
Manhattan = templateManhattan
)Now that we have identified a set of differentially bounded regions, annotated them, written a file with custom code, and changed the distribution plot templates we can proceed to creating the HTML report.
## The output will be saved in the 'DiffBind-example' directory
dir.create('DiffBind-example', showWarnings = FALSE, recursive = TRUE)## Create the report
report <- renderReport(regions, '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, nBestRegions = 100,
outdir = 'DiffBind-example', output = 'index',
customCode = file.path(getwd(), 'DiffBind_custom.Rmd'),
annotation = annotation, densityTemplates = densityTemplates)## Warning: replacing previous import 'ggplot2::Position' by
## 'BiocGenerics::Position' when loading 'ggbio'## Writing 11 Bibtex entries ...## OK
## Results written to file 'DiffBind-example/index.bib'##
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## ordinary text without R code## output file: index.knit.md## /usr/local/bin/pandoc +RTS -K512m -RTS index.utf8.md --to html --from markdown+autolink_bare_uris+ascii_identifiers+tex_math_single_backslash-implicit_figures --output index.html --smart --email-obfuscation none --self-contained --standalone --section-divs --table-of-contents --toc-depth 3 --variable toc_float=1 --variable toc_selectors=h1,h2,h3 --variable toc_collapsed=1 --variable toc_smooth_scroll=1 --template /Library/Frameworks/R.framework/Versions/3.3/Resources/library/rmarkdown/rmd/h/default.html --variable 'theme:spacelab' --include-in-header /var/folders/cx/n9s558kx6fb7jf5z_pgszgb80000gn/T//Rtmp2AY30e/rmarkdown-str3a68150f2baa.html --mathjax --variable 'mathjax-url:https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --no-highlight --variable highlightjs=/Library/Frameworks/R.framework/Versions/3.3/Resources/library/rmarkdown/rmd/h/highlight --variable navigationjs=/Library/Frameworks/R.framework/Versions/3.3/Resources/library/rmarkdown/rmd/h/navigation-1.0 --variable code_folding=hide##
## Output created: index.htmlYou can view the final report at DiffBind-example.
## Date generated:
Sys.time()## [1] "2016-04-12 07:37:58 EDT"## Time spent making this page:
proc.time()## user system elapsed
## 302.477 8.879 329.579## R and packages info:
options(width = 120)
devtools::session_info()## 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)
## rstudioapi 0.5 2016-01-24 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