aucScores-class {RcisTarget} | R Documentation |
Contains the AUC scores for each gene- or region-set.
They can be accessed through getAUC()
and the regular methods
(i.e. nrow, rownames... ) available for SummarizedExperiment objects.
## S4 method for signature 'aucScores' show(object) ## S4 method for signature 'aucScores' getAUC(object)
object |
Results from |
show: Prints a summary of the object
getAUC: Returns the matrix containing the AUC scores
################################################## # Setup & previous steps in the workflow: #### Gene sets # As example, the package includes an Hypoxia gene set: txtFile <- paste(file.path(system.file('examples', package='RcisTarget')), "hypoxiaGeneSet.txt", sep="/") geneLists <- list(hypoxia=read.table(txtFile, stringsAsFactors=FALSE)[,1]) #### Databases ## Motif rankings: Select according to organism and distance around TSS ## (See the vignette for URLs to download) # motifRankings <- importRankings("hg19-500bp-upstream-7species.mc9nr.feather") ## For this example we will use a SUBSET of the ranking/motif databases: library(RcisTarget.hg19.motifDBs.cisbpOnly.500bp) data(hg19_500bpUpstream_motifRanking_cispbOnly) motifRankings <- hg19_500bpUpstream_motifRanking_cispbOnly ## Motif - TF annotation: data(motifAnnotations_hgnc) # human TFs (for motif collection 9) motifAnnotation <- motifAnnotations_hgnc ### Run RcisTarget # Step 1. Calculate AUC motifs_AUC <- calcAUC(geneLists, motifRankings) ################################################## #Exploring the output: motifs_AUC class(motifs_AUC) # Extracting the AUC matrix: getAUC(motifs_AUC)[,1:5] # Subsetting and regular manipulation methods are also available: motifs_AUC[1,] motifs_AUC[,3:4] dim(motifs_AUC) nrow(motifs_AUC) ncol(motifs_AUC) colnames(motifs_AUC) rownames(motifs_AUC)