lolaVolcanoPlot: lolaVolcanoPlot

In demuellae/muRtools: Mueller's R tools

lolaVolcanoPlot

Description

plot a volcano plot showing LOLA enrichment results: LOLA p-value against the log-odds score. Colored by rank

Usage

lolaVolcanoPlot(
  lolaDb,
  lolaRes,
  includedCollections = c(),
  signifCol = "qValue",
  recalc = TRUE,
  colorBy = "maxRnk",
  colorpanel = c()
)

Arguments

lolaDb	LOLA DB object as returned by LOLA::loadRegionDB or loadLolaDbs
lolaRes	LOLA enrichment result as returned by the runLOLA function from the LOLA package
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 lolaRes. Should be one of c("pValueLog", "qValue").
recalc	recalculate adjusted p-value/q-value and ranks after the specified subsetting (by includedCollections)
colorBy	annotation/column in the the LOLA DB that should be used for point coloring
colorpanel	colors to be used for coloring the points

Value

ggplot object containing the plot

Author(s)

Fabian Mueller

Examples

# example taken from RnBeads
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")

demuellae/muRtools documentation built on Sept. 8, 2023, 4:32 p.m.