Abstract
Example using RcisTarget to analyze genomic regions (instead of genes).
This tutorial requires RcisTarget >= 1.11
packageVersion("RcisTarget")
## [1] '1.26.0'
# download.file("https://gbiomed.kuleuven.be/apps/lcb/i-cisTarget/examples/input_files/human/peaks/Encode_GATA1_peaks.bed", "Encode_GATA1_peaks.bed")
txtFile <- paste(file.path(system.file('examples', package='RcisTarget')),"Encode_GATA1_peaks.bed", sep="/")
regionsList <- rtracklayer::import.bed(txtFile)
regionSets <- list(GATA1_peaks=regionsList)
This analysis requires the region-based databases (for the appropriate organism): - Region-based motif rankings - Motif-TF annotations (same for region- & gene-based analysis) - Region location (i.e. conversion from region ID to genomic location)
The databases can be downloaded from: https://resources.aertslab.org/cistarget/
Note: This example uses an old version of the database (mc9nr), we recommend to use the latest version (v10_clus).
library(RcisTarget)
# Motif rankings
featherFilePath <- "~/databases/hg19-regions-9species.all_regions.mc9nr.feather"
## Motif - TF annotation:
data(motifAnnotations_hgnc_v9) # human TFs (for motif collection 9)
motifAnnotation <- motifAnnotations_hgnc_v9
# Regions location *
data(dbRegionsLoc_hg19)
dbRegionsLoc <- dbRegionsLoc_hg19
dbRegionsLoc <- getDbRegionsLoc(featherFilePath)
The main difference with a gene-based analysis is that the regions need to be converted to the database IDs first, and the parameter aucMaxRank
should be adjusted:
aucMaxRank= 0.005
aucMaxRank= 0.005
aucMaxRank= 0.01
# Convert regions
regionSets_db <- lapply(regionSets, function(x) convertToTargetRegions(queryRegions=x, targetRegions=dbRegionsLoc))
# Import rankings
allRegionsToImport <- unique(unlist(regionSets_db)); length(allRegionsToImport)
motifRankings <- importRankings(featherFilePath, columns=allRegionsToImport)
# Run RcisTarget
motifEnrichmentTable <- cisTarget(regionSets_db, motifRankings, aucMaxRank=0.005*getNumColsInDB(motifRankings))
# Show output:
showLogo(motifEnrichmentTable)