subsampleBySpikeIn {BRGenomics} | R Documentation

## Randomly subsample reads according to spike-in normalization

### Description

Randomly subsample reads according to spike-in normalization

### Usage

    subsampleBySpikeIn(
      dataset.gr,
      si_pattern = NULL,
      si_names = NULL,
      ctrl_pattern = NULL,
      ctrl_names = NULL,
      batch_norm = TRUE,
      RPM_units = FALSE,
      field = "score",
      sample_names = NULL,
      expand_ranges = FALSE,
      ncores = getOption("mc.cores", 2L)
    )

### Arguments

`dataset.gr, si_pattern, si_names, ctrl_pattern, ctrl_names, batch_norm, field, sample_names, expand_ranges, ncores` | See `getSpikeInNFs`
`RPM_units` | If set to `TRUE`, the final readcount values will be converted to units equivalent to/directly comparable with `RPM` for the negative control(s). If `field = NULL`, the GRanges objects will be converted to disjoint `"basepair-resolution"` GRanges objects, with normalized readcounts contained in the "score" metadata column.

### Details

Note that if `field = NULL`,

### Value

An object parallel to `dataset.gr`, but with fewer reads. E.g. if `dataset.gr` is a list of GRanges, the output is a list of the same GRanges, but in which each GRanges has fewer reads.

### Author(s)

Mike DeBerardine

### See Also

`getSpikeInCounts`, `getSpikeInNFs`

### 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

    #--------------------------------------------------#
    # (The simple spike-in NFs)
    #--------------------------------------------------#

    # see examples for getSpikeInNFs for more
    getSpikeInNFs(grl, si_pattern = "spike", ctrl_pattern = "gr1",
                  method = "SNR", ncores = 1)

    #--------------------------------------------------#
    # Subsample the GRanges according to the spike-in NFs
    #--------------------------------------------------#

    ss <- subsampleBySpikeIn(grl, si_pattern = "spike", ctrl_pattern = "gr1",
                             ncores = 1)
    ss

    lapply(ss, function(x) sum(score(x))) # total reads in each

    # Put in units of RPM for the negative control
    ssr <- subsampleBySpikeIn(grl, si_pattern = "spike", ctrl_pattern = "gr1",
                              RPM_units = TRUE, ncores = 1)

    ssr

    lapply(ssr, function(x) sum(score(x))) # total signal in each

[Package BRGenomics version 1.0.3 Index]