quantifyCTSSs {CAGEfightR} | R Documentation |
This function reads in CTSS count data from a series of BigWig-files (or bedGraph-files) and returns a CTSS-by-library count matrix. For efficient processing, the count matrix is stored as a sparse matrix (dgCMatrix from the Matrix package), and CTSSs are compressed to a GPos object if possible.
quantifyCTSSs(plusStrand, minusStrand, design = NULL, genome = NULL, ...) ## S4 method for signature 'BigWigFileList,BigWigFileList' quantifyCTSSs( plusStrand, minusStrand, design = NULL, genome = NULL, nTiles = 1L ) ## S4 method for signature 'character,character' quantifyCTSSs(plusStrand, minusStrand, design = NULL, genome = NULL)
plusStrand |
BigWigFileList or character: BigWig/bedGraph files with plus-strand CTSS data. |
minusStrand |
BigWigFileList or character: BigWig/bedGraph files with minus-strand CTSS data. |
design |
DataFrame or data.frame: Additional information on samples which will be added to the ouput |
genome |
Seqinfo: Genome information. If NULL the smallest common genome will be found using bwCommonGenome when BigWig-files are analyzed. |
... |
additional arguments passed to methods. |
nTiles |
integer: Number of genomic tiles to parallelize over. |
RangedSummarizedExperiment, where assay is a sparse matrix (dgCMatrix) of CTSS counts and design stored in colData.
Other Quantification functions:
quantifyCTSSs2()
,
quantifyClusters()
,
quantifyGenes()
## Not run: # Load the example data data('exampleDesign') # Use the BigWig-files included with the package: bw_plus <- system.file('extdata', exampleDesign$BigWigPlus, package = 'CAGEfightR') bw_minus <- system.file('extdata', exampleDesign$BigWigMinus, package = 'CAGEfightR') # Create two named BigWigFileList-objects: bw_plus <- BigWigFileList(bw_plus) bw_minus <- BigWigFileList(bw_minus) names(bw_plus) <- exampleDesign$Name names(bw_minus) <- exampleDesign$Name # Quantify CTSSs, by default this will use the smallest common genome: CTSSs <- quantifyCTSSs(plusStrand=bw_plus, minusStrand=bw_minus, design=exampleDesign) # Alternatively, a genome can be specified: si <- seqinfo(bw_plus[[1]]) si <- si['chr18'] CTSSs_subset <- quantifyCTSSs(plusStrand=bw_plus, minusStrand=bw_minus, design=exampleDesign, genome=si) # Quantification can be speed up by using multiple cores: library(BiocParallel) register(MulticoreParam(workers=3)) CTSSs_subset <- quantifyCTSSs(plusStrand=bw_plus, minusStrand=bw_minus, design=exampleDesign, genome=si) # CAGEfightR also support bedGraph files, first BigWig is converted bg_plus <- replicate(n=length(bw_plus), tempfile(fileext="_plus.bedGraph")) bg_minus <- replicate(n=length(bw_minus), tempfile(fileext="_minus.bedGraph")) names(bg_plus) <- names(bw_plus) names(bg_minus) <- names(bw_minus) convertBigWig2BedGraph(input=sapply(bw_plus, resource), output=bg_plus) convertBigWig2BedGraph(input=sapply(bw_minus, resource), output=bg_minus) # Then analyze: Note a genome MUST be supplied here! si <- bwCommonGenome(bw_plus, bw_minus) CTSSs_via_bg <- quantifyCTSSs(plusStrand=bg_plus, minusStrand=bg_minus, design=exampleDesign, genome=si) # Confirm that the two approaches yield the same results all(assay(CTSSs_via_bg) == assay(CTSSs)) ## End(Not run)