lolaBarPlot {RnBeads} | R Documentation

## lolaBarPlot

### Description

plot a barplot of LOLA enrichment results

### Usage

    lolaBarPlot(lolaDb, lolaRes, scoreCol = "pValueLog",
      orderCol = scoreCol, signifCol = "qValue",
      includedCollections = c(), pvalCut = 0.01, maxTerms = 50,
      colorpanel = sample(rainbow(maxTerms, v = 0.5)),
      groupByCollection = TRUE, orderDecreasing = NULL)

### 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
`scoreCol` | column name in `lolaRes` to be plotted
`orderCol` | column name in `lolaRes` which is used for sorting the results
`signifCol` | column name of the significance score in `lolaRes`. Should be one of `c("pValueLog", "qValue")`
`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 `getTargetFromLolaDb`). An empty vector indicates that black will be used for all bars.
`groupByCollection` | facet the plot by collection
`orderDecreasing` | flag indicating whether the value in `orderCol` should be considered as decreasing (as opposed to increasing). `NULL` (default) for automatic determination.

### Value

ggplot object containing the plot

### Author(s)

Fabian Mueller

### Examples

    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
    lolaBarPlot(res$lolaDb, lolaRes, scoreCol="oddsRatio", orderCol="maxRnk", pvalCut=0.05)

[Package RnBeads version 2.2.0 Index]