getMotifAnnotation {RcisTarget} | R Documentation |
Get the genes/transcription factors annotated to the given motifs
getMotifAnnotation( motifs, motifAnnot, annotCats = c("directAnnotation", "inferredBy_MotifSimilarity", "inferredBy_Orthology", "inferredBy_MotifSimilarity_n_Orthology"), idColumn = "motif", returnFormat = c("asCharacter", "subset", "list")[1], keepAnnotationCategory = TRUE )
motifs |
Motif IDs |
motifAnnot |
Motif annotation database containing the annotations of the motif to genes or transcription factors. |
annotCats |
Annotation categories to be considered: "directAnnotation" (annotated in the source database), "inferredBy_Orthology" (the motif is annotated to an homologous/ortologous gene), or inferred based on motif similarity ("inferredBy_MotifSimilarity", "inferredBy_MotifSimilarity_n_Orthology"). |
idColumn |
Annotation column containing the ID (e.g. motif, accession) |
returnFormat |
Determines the output format. Choose one of the following values: |
keepAnnotationCategory |
Include annotation type in the TF information?
|
See argument returnFormat
addMotifAnnotation
add the annotation directly to the motif enrichment results.
See the package vignette for examples and more details:
vignette("RcisTarget")
################################################## # 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) ################################################## ### (This step: Step 2) # Before starting: Setup the paralell computation library(BiocParallel); register(MulticoreParam(workers = 2)) # Select significant motifs, add TF annotation & format as table motifEnrichmentTable <- addMotifAnnotation(motifs_AUC, motifAnnot=motifAnnotation) # Alternative: Modifying some options motifEnrichment_wIndirect <- addMotifAnnotation(motifs_AUC, nesThreshold=2, motifAnnot=motifAnnotation, highlightTFs = "HIF1A", motifAnnot_highConfCat=c("directAnnotation"), motifAnnot_lowConfCat=c("inferredBy_MotifSimilarity", "inferredBy_MotifSimilarity_n_Orthology", "inferredBy_Orthology"), digits=3) # Getting TFs for a given TF: motifs <- motifEnrichmentTable$motif[1:3] getMotifAnnotation(motifs, motifAnnot=motifAnnotation) getMotifAnnotation(motifs, motifAnnot=motifAnnotation, returnFormat="list") ### Exploring the output: # Number of enriched motifs (Over the given NES threshold) nrow(motifEnrichmentTable) # Interactive exploration motifEnrichmentTable <- addLogo(motifEnrichmentTable) DT::datatable(motifEnrichmentTable, filter="top", escape=FALSE, options=list(pageLength=50)) # Note: If using the fake database, the results of this analysis are meaningless # The object returned is a data.table (for faster computation), # which has a diferent syntax from the standard data.frame or matrix # Feel free to convert it to a data.frame (as.data.frame()) motifEnrichmentTable[,1:6] ################################################## # Next step (step 3, optional): ## Not run: motifEnrichmentTable_wGenes <- addSignificantGenes(motifEnrichmentTable, geneSets=geneLists, rankings=motifRankings, method="aprox") ## End(Not run)