R/getUniverse.R
getUniverse.Rd
`getUniverse` returns a `GRanges` object with all the genomic regions in a data set, that can be used for AMR enrichment analysis
getUniverse(data.ranges, merge.window = 300, min.cpgs = 7, min.width = 1)
A `GRanges` object with genomic locations and corresponding beta values included as metadata.
A single integer >= 1. All `data.ranges` genomic locations within this distance will be merged (the default: 300).
A single integer >= 1. All genomic regions containing less than `min.cpgs` genomic locations are filtered out (the default: 7).
A single integer >= 1 (the default). Only regions with the width of at least `min.width` are returned.
The output is a `GRanges` object that contain all the genomic regions in `data.ranges` object (in other words, all potential AMRs).
In the provided data set `getUniverse` merges and outputs all the genomic regions that satisfy filtering criteria, thus creating a `GRanges` object to be used as a reference set of genomic regions for AMR enrichment analysis.
getAMR
for identification of AMRs,
plotAMR
for plotting AMRs, simulateAMR
and
simulateData
for the generation of simulated test data sets,
and `ramr` vignettes for the description of usage and sample data.
data(ramr)
universe <- getUniverse(ramr.data, min.cpgs=5, merge.window=1000)
# \donttest{
# identify AMRs
amrs <- getAMR(ramr.data, ramr.samples, ramr.method="beta", min.cpgs=5,
merge.window=1000, qval.cutoff=1e-3, cores=2)
#> Identifying AMRs
#> [7.598s]
# AMR enrichment analysis using LOLA
library(LOLA)
# download LOLA region databases from http://databio.org/regiondb
hg19.extdb.file <- system.file("LOLAExt", "hg19", package="LOLA")
if (file.exists(hg19.extdb.file)) {
hg19.extdb <- loadRegionDB(hg19.extdb.file)
runLOLA(amrs, universe, hg19.extdb, cores=1, redefineUserSets=TRUE)
}
# }