lolaBoxPlotPerTarget {RnBeads} | R Documentation |
plot a boxplot showing LOLA enrichment results per "target" group (see getTargetFromLolaDb
for an explanation of
"target").
lolaBoxPlotPerTarget(lolaDb, lolaRes, scoreCol = "pValueLog", orderCol = scoreCol, signifCol = "qValue", includedCollections = c(), pvalCut = 0.01, maxTerms = 50, colorpanel = c(), groupByCollection = TRUE, orderDecreasing = NULL, scoreDecreasing = NULL)
lolaDb |
LOLA DB object as returned by |
lolaRes |
LOLA enrichment result as returned by the |
scoreCol |
column name in |
orderCol |
column name in |
signifCol |
column name of the significance score in |
includedCollections |
vector of collection names to be included in the plot. If empty (default), all collections are used |
pvalCut |
p-value cutoff to be employed for filtering the results |
maxTerms |
maximum number of items to be included in the plot |
colorpanel |
colors to be used for coloring the bars according to "target" (see |
groupByCollection |
facet the plot by collection |
orderDecreasing |
flag indicating whether the value in |
scoreDecreasing |
flag indicating whether the value in |
ggplot object containing the plot
Fabian Mueller
library(RnBeads.hg19) data(small.example.object) logger.start(fname=NA) # compute differential methylation dm <- rnb.execute.computeDiffMeth(rnb.set.example,pheno.cols=c("Sample_Group","Treatment")) # download LOLA DB lolaDest <- tempfile() dir.create(lolaDest) lolaDirs <- downloadLolaDbs(lolaDest, dbs="LOLACore") # perform enrichment analysis res <- performLolaEnrichment.diffMeth(rnb.set.example,dm,lolaDirs[["hg19"]]) # select the 500 most hypermethylated tiling regions in ESCs compared to iPSCs # in the example dataset lolaRes <- res$region[["hESC vs. hiPSC (based on Sample_Group)"]][["tiling"]] lolaRes <- lolaRes[lolaRes$userSet=="rankCut_500_hyper",] # plot lolaBoxPlotPerTarget(res$lolaDb, lolaRes, scoreCol="oddsRatio", orderCol="maxRnk", pvalCut=0.05)