performLolaEnrichment.diffVar: performLolaEnrichment.diffVar
In RnBeads: RnBeads

Description Usage Arguments Value Author(s) Examples

performs LOLA enrichment analysis for a given differential variability table.

performLolaEnrichment.diffVar(
  rnbSet,
  diffmeth,
  enrich.diffMeth = NULL,
  lolaDbPaths,
  rank.cuts.region = c(100, 500, 1000),
  add.auto.rank.cut = TRUE,
  rerank = TRUE,
  verbose = TRUE
)

`rnbSet`	RnBSet object for which differential variability was computed
`diffmeth`	RnBDiffMeth object. See `RnBDiffMeth-class` for details.
`enrich.diffMeth`	Enrichment object as obtained from `performLolaEnrichment.diffMeth`. If it is not provided a new object is created.
`lolaDbPaths`	LOLA database paths
`rank.cuts.region`	Cutoffs for combined ranking that are used to determine differentially variable regions
`add.auto.rank.cut`	flag indicating whether an automatically computed cut-off should also be considered.
`rerank`	For deterimining differential variability: should the ranks be ranked again or should the absolute ranks be used.
`verbose`	Enable for detailed status report

a DiffMeth.lola.enrich object (S3) containing the following attributes

`region`	Enrichment information for differential variability on the region level. A `data.table` object as returned by the `runLOLA` function from the `LOLA` package for further details. Each element will contain different user sets for different rank cutoffs and hyper/hypomethylation events(`userSet` column)
`lolaDb`	The loaded `lolaDb` object containing the merged databases as returned by `loadLolaDbs`

Michael Scherer and Fabian Mueller

library(RnBeads.hg19)
data(small.example.object)
logger.start(fname=NA)
# compute differential methylation
dm <- rnb.execute.diffVar(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.diffVar(rnb.set.example,dm,lolaDirs[["hg19"]])