quantifyCTSSs {CAGEfightR} | R Documentation

## Quantify CAGE Transcriptions Start Sites (CTSSs)

### Description

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.

### Usage

    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)

### Arguments

`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.

### Value

RangedSummarizedExperiment, where assay is a sparse matrix (dgCMatrix) of CTSS counts and design stored in colData.

### See Also

Other Quantification functions: `quantifyCTSSs2()`, `quantifyClusters()`, `quantifyGenes()`

### Examples

    ## 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)

[Package CAGEfightR version 1.10.0 Index]