bootstrap-signal-by-position {BRGenomics} | R Documentation

## Bootstrapping Mean Signal by Position for Metaplotting

### Description

These functions perform bootstrap subsampling of mean readcounts at different positions within regions of interest (`metaSubsample`), or, in the more general case of `metaSubsampleMatrix`, column means of a matrix are bootstrapped by sampling the rows. Mean signal counts can be calculated at base-pair resolution, or over larger bins.

### Usage

    metaSubsample(
      dataset.gr,
      regions.gr,
      binsize = 1,
      first.output.xval = 1,
      sample.name = deparse(substitute(dataset.gr)),
      n.iter = 1000,
      prop.sample = 0.1,
      lower = 0.125,
      upper = 0.875,
      field = "score",
      NF = NULL,
      remove.empty = FALSE,
      blacklist = NULL,
      zero_blacklisted = FALSE,
      expand_ranges = FALSE,
      ncores = getOption("mc.cores", 2L)
    )

    metaSubsampleMatrix(
      counts.mat,
      binsize = 1,
      first.output.xval = 1,
      sample.name = NULL,
      n.iter = 1000,
      prop.sample = 0.1,
      lower = 0.125,
      upper = 0.875,
      NF = 1,
      remove.empty = FALSE,
      ncores = getOption("mc.cores", 2L)
    )

### Arguments

`dataset.gr` | A GRanges object in which signal is contained in metadata (typically in the `"score"` field), or a list of such GRanges objects.
`regions.gr` | A GRanges object containing intervals over which to metaplot. All ranges must have the same width.
`binsize` | The size of bin (in basepairs, or number of columns for `metaSubsampleMatrix`) to use for counting signal. Especially important for counting signal over large or sparse regions.
`first.output.xval` | The relative start position of the first bin, e.g. if `regions.gr` begins at 50 bases upstream of the TSS, set `first.output.xval = -50`. This number only affects the x-values that are returned, which are provided as a convenience.
`sample.name` | Defaults to the name of the input dataset. This is included in the output as a convenience, as it allows row-binding outputs from different samples. If `length(field) > 1` and the default `sample.name` is left, the sample names will be inferred from the field names.
`n.iter` | Number of random subsampling iterations to perform. Default is `1000`.
`prop.sample` | The proportion of the ranges in `regions.gr` (e.g. the proportion of genes) or the proportion of rows in `counts.mat` to sample in each iteration. The default is `0.1` (10 percent).
`lower, upper` | The lower and upper quantiles of subsampled signal means to return. The defaults, `0.125` and `0.875` (i.e. the 12.5th and 85.5th percentiles) return a 75 percent confidence interval about the bootstrapped mean.
`field` | One or more metadata fields of `dataset.gr` to be counted.
`NF` | An optional normalization factor by which to multiply the counts. If given, `length(NF)` must be equal to `length(field)`.
`remove.empty` | A logical indicating whether regions (`metaSubsample`) or rows (`metaSubsampleMatrix`) without signal should be removed from the analysis. Not recommended if using multiple fields, as the gene lists will no longer be equivalent.
`blacklist` | An optional GRanges object containing regions that should be excluded from signal counting.
`zero_blacklisted` | When set to `FALSE` (the default), signal at blacklisted sites is ignored from computations. If set to `TRUE`, signal at blacklisted sites will be treated as equal to zero. For bootstrapping, the default behavior of ignoring signal at blacklisted sites should almost always be kept.
`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` | Number of cores to use for computations.
`counts.mat` | A matrix over which to bootstrap column means by subsampling its rows. Typically, a matrix of readcounts with rows for genes and columns for positions within those genes.

### Value

A dataframe containing x-values, means, lower quantiles, upper quantiles, and the sample name (as a convenience for row-binding multiple of these dataframes). If a list of GRanges is given as input, or if multiple fields are given, a single, combined dataframe is returned containing data for all fields/datasets.

### Author(s)

Mike DeBerardine

### See Also

`getCountsByPositions`

### Examples

    data("PROseq") # import included PROseq data
    data("txs_dm6_chr4") # import included transcripts

    # for each transcript, use promoter-proximal region from TSS to +100
    pr <- promoters(txs_dm6_chr4, 0, 100)

    #--------------------------------------------------#
    # Bootstrap average signal in each 5 bp bin across all transcripts,
    # and get confidence bands for middle 30% of bootstrapped means
    #--------------------------------------------------#

    set.seed(11)
    df <- metaSubsample(PROseq, pr, binsize = 5,
                        lower = 0.35, upper = 0.65,
                        ncores = 1)
    df[1:10, ]

    #--------------------------------------------------#
    # Plot bootstrapped means with confidence intervals
    #--------------------------------------------------#

    plot(mean ~ x, df, type = "l", main = "PROseq Signal",
         ylab = "Mean + 30% CI", xlab = "Distance from TSS")
    polygon(c(df$x, rev(df$x)), c(df$lower, rev(df$upper)),
            col = adjustcolor("black", 0.1), border = FALSE)

    #==================================================#
    # Using a matrix as input
    #==================================================#

    # generate a matrix of counts in each region
    countsmat <- getCountsByPositions(PROseq, pr)
    dim(countsmat)

    #--------------------------------------------------#
    # bootstrap average signal in 10 bp bins across all transcripts
    #--------------------------------------------------#

    set.seed(11)
     df <- metaSubsampleMatrix(countsmat, binsize = 10,
                               sample.name = "PROseq",
                               ncores = 1)
    df[1:10, ]

    #--------------------------------------------------#
    # the same, using a normalization factor, and changing the x-values
    #--------------------------------------------------#

    set.seed(11)
    df <- metaSubsampleMatrix(countsmat, binsize = 10,
                              first.output.xval = 0, NF = 0.75,
                              sample.name = "PROseq", ncores = 1)
    df[1:10, ]

[Package BRGenomics version 1.2.0 Index]