This tutorial requires RcisTarget >= 1.11

packageVersion("RcisTarget")
## [1] '1.15.3'

1. Prepare/download the input regions

download.file("https://gbiomed.kuleuven.be/apps/lcb/i-cisTarget/examples/input_files/human/peaks/Encode_GATA1_peaks.bed", "Encode_GATA1_peaks.bed")
regionsList <- rtracklayer::import.bed("Encode_GATA1_peaks.bed")
regionSets <- list(GATA1_peaks=regionsList)

2. Load the RcisTarget databases

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/

library(RcisTarget)
# Motif rankings
featherFilePath <- "~/databases/hg19-regions-9species.all_regions.mc9nr.feather"

# Motif-TF annotations
data("motifAnnotations_hgnc")
motifAnnotation <- motifAnnotations_hgnc

# Regions location *
data(dbRegionsLoc_hg19)
dbRegionsLoc <- dbRegionsLoc_hg19
  • Note: The region location is only needed for human and mouse databases. For drosophila, the region ID contains the location, and can be obtained with:
dbRegionsLoc <- getDbRegionsLoc(featherFilePath)

3. Run the analysis

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:

  • For Human: aucMaxRank= 0.005
  • For Mouse: aucMaxRank= 0.005
  • For Fly: 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)