This tutorial requires RcisTarget >= 1.11

## [1] '1.24.0'

1. Prepare/download the input regions

# download.file("", "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)

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:

Note: This example uses an old version of the database (mc9nr), we recommend to use the latest version (v10_clus).

# 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 *
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: