quantifyCTSSs {CAGEfightR} | R Documentation |
This function reads in CTSS count data from a series of BigWig-files and returns a CTSS-by-library count matrix. For efficient processing, the count matrix is stored as a sparse matrix (dgCMatrix).
quantifyCTSSs(plusStrand, minusStrand, design = NULL, genome = NULL, tileWidth = 100000000L)
plusStrand |
BigWigFileList: BigWig files with plus-strand CTSS data. |
minusStrand |
BigWigFileList: BigWig files with minus-strand CTSS data. |
design |
DataFrame or data.frame: Additional information on samples. |
genome |
Seqinfo: Genome information. If NULL the smallest common genome will be found using bwCommonGenome. |
tileWidth |
integer: Size of tiles to parallelize over. |
RangedSummarizedExperiment, where assay is a sparse matrix (dgCMatrix) of CTSS counts..
Other Quantification functions: 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 <- 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 <- quantifyCTSSs(plusStrand=bw_plus, minusStrand=bw_minus, design=exampleDesign, genome=si) ## End(Not run)