This tutorial requires RcisTarget >= 1.7.1.

## [1] '1.13.0'

1. Select the gene/region set to analyze & the “background”

# Genes to analyze:
txtFile <- paste(file.path(system.file('examples', package='RcisTarget')),"hypoxiaGeneSet.txt", sep="/")
geneSets <- list(hypoxia=read.table(txtFile, stringsAsFactors=FALSE)[,1])

# Background: 
txtFile <- paste(file.path(system.file('examples', package='RcisTarget')),"randomGeneSet.txt", sep="/") # for the toy example we will use a few random genes
background <- read.table(txtFile, stringsAsFactors=FALSE)[,1]

The background should contain the target genes/regions.

If for any reason that is not the case, you can add the target genes to the background, or remove the target genes missing from the background (depending on what makes more sense in your specific analysis).

# A: Add
background <- unique(c(geneSets$hypoxia, background))
# B: Intersect
# geneSets$hypoxia <- intersect(geneSets$hypoxia, background)
gplots::venn(list(background=background, geneLists=unlist(geneSets)))

2. Create the background-ranking

Select the appropriate ranking-database:

dbPath <- "~/databases/hg19-500bp-upstream-10species.mc9nr.feather"

Load the database and re-rank the genes/motifs (e.g. only within the “background+foreground”)

rankingsDb <- importRankings(dbPath, columns=background)
bgRanking <- reRank(rankingsDb) 

3. Run RcisTarget with this new ranking

Once the “background-ranking” is ready, just use it to run RcisTarget as usual:

motifEnrichmentTable <- cisTarget(geneSets, bgRanking, aucMaxRank=0.03*getNumColsInDB(bgRanking))