lolaVolcanoPlot {RnBeads} | R Documentation |
plot a volcano plot showing LOLA enrichment results: LOLA p-value against the log-odds score. Colored by rank
lolaVolcanoPlot( lolaDb, lolaRes, includedCollections = c(), signifCol = "qValue", colorBy = "maxRnk", colorpanel = c() )
lolaDb |
LOLA DB object as returned by |
lolaRes |
LOLA enrichment result as returned by the |
includedCollections |
vector of collection names to be included in the plot. If empty (default), all collections are used |
signifCol |
column name of the significance score in |
colorBy |
annotation/column in the the LOLA DB that should be used for point coloring |
colorpanel |
colors to be used for coloring the points |
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 lolaVolcanoPlot(res$lolaDb, lolaRes, signifCol="qValue")