getSpikeInCounts {BRGenomics} | R Documentation

## Filtering and counting spike-in reads

### Description

Filtering and counting spike-in reads

### Usage

    getSpikeInCounts(
      dataset.gr,
      si_pattern = NULL,
      si_names = NULL,
      field = "score",
      sample_names = NULL,
      expand_ranges = FALSE,
      ncores = getOption("mc.cores", 2L)
    )

    removeSpikeInReads(
      dataset.gr,
      si_pattern = NULL,
      si_names = NULL,
      field = "score",
      ncores = getOption("mc.cores", 2L)
    )

    getSpikeInReads(
      dataset.gr,
      si_pattern = NULL,
      si_names = NULL,
      field = "score",
      ncores = getOption("mc.cores", 2L)
    )

### Arguments

`dataset.gr` | A GRanges object or a list of GRanges objects.
`si_pattern` | A regular expression that matches spike-in chromosomes. Can be used in addition to, or as an alternative to `si_names`.
`si_names` | A character vector giving the names of the spike-in chromosomes. Can be used in addition to, or as an alternative to `si_pattern`.
`field` | The metadata field in `dataset.gr` that contains readcounts. If each range is an individual read, set `field = NULL`.
`sample_names` | An optional character vector used to rename the datasets in `dataset.gr`
`expand_ranges` | Logical indicating if ranges in `dataset.gr` should be treated as descriptions of single molecules (`FALSE`), or if ranges should be treated as representing multiple adjacent positions with the same signal (`TRUE`). See `getCountsByRegions`.
`ncores` | The number of cores to use for computations.

### Value

A dataframe containing total readcounts, experimental (non-spike-in) readcounts, and spike-in readcounts.

### Author(s)

Mike DeBerardine

### Examples

    #--------------------------------------------------#
    # Make list of dummy GRanges
    #--------------------------------------------------#

    gr1_rep1 <- GRanges(seqnames = c("chr1", "chr2", "spikechr1", "spikechr2"),
                        ranges = IRanges(start = 1:4, width = 1),
                        strand = "+")
    gr2_rep2 <- gr2_rep1 <- gr1_rep2 <- gr1_rep1

    # set readcounts
    score(gr1_rep1) <- c(1, 1, 1, 1) # 2 exp + 2 spike = 4 total
    score(gr2_rep1) <- c(2, 2, 1, 1) # 4 exp + 2 spike = 6 total
    score(gr1_rep2) <- c(1, 1, 2, 1) # 2 exp + 3 spike = 5 total
    score(gr2_rep2) <- c(4, 4, 2, 2) # 8 exp + 4 spike = 12 total

    grl <- list(gr1_rep1, gr2_rep1,
                gr1_rep2, gr2_rep2)

    names(grl) <- c("gr1_rep1", "gr2_rep1",
                    "gr1_rep2", "gr2_rep2")

    grl

    #--------------------------------------------------#
    # Count spike-in reads
    #--------------------------------------------------#

    # by giving names of all spike-in chromosomes
    getSpikeInCounts(grl, si_names = c("spikechr1", "spikechr2"), ncores = 1)

    # or by matching the string/regular expression "spike" in chromosome names
    getSpikeInCounts(grl, si_pattern = "spike", ncores = 1)

    #--------------------------------------------------#
    # Filter out spike-in reads
    #--------------------------------------------------#

    removeSpikeInReads(grl, si_pattern = "spike", ncores = 1)

    #--------------------------------------------------#
    # Return spike-in reads
    #--------------------------------------------------#

    getSpikeInReads(grl, si_pattern = "spike", ncores = 1)

[Package BRGenomics version 1.2.0 Index]