Description Usage Arguments Value See Also Examples
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 | 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()
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | ## 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)
|
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