| quantifyCTSSs2 {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).
quantifyCTSSs2( 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:
quantifyCTSSs(),
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)