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Employ an eBayes shrinkage approach for bin-level estimates for A/B inference

Source: R/shrinkBins.R
shrinkBins.Rd

shrinkBins returns shrunken bin-level estimates

Usage

shrinkBins(
  x,
  original.x,
  prior.means = NULL,
  chr = NULL,
  res = 1000000,
  targets = NULL,
  jse = TRUE,
  assay = c("rna", "atac", "array"),
  genome = c("hg19", "hg38", "mm9", "mm10")
)

Arguments

x

Input SummarizedExperiment object

original.x

Full sample set SummarizedExperiment object

prior.means

The means of the bin-level prior distribution

chr

The chromosome to operate on

res

Resolution to perform the binning

targets

The column/sample/cell names to shrink towards

jse

Whether to use a James-Stein estimator (default is TRUE)

assay

What assay type this is ("rna", "atac", "array")

genome

What genome are we working with ("hg19", "hg38", "mm9", "mm10")

Value

A list object to pass to getCorMatrix

Details

This function computes shrunken bin-level estimates using a James-Stein estimator (JSE), reformulated as an eBayes procedure. JSE can be used only if at least 4 targets are provided - any less and shrinkBins will fall back to using Bayes rule which will probably not be great but it won't explode and may provide some reasonable results anyway

Examples

data("k562_scrna_chr14", package = "compartmap")
shrunken.bin.scrna <- shrinkBins(
  x = k562_scrna_chr14,
  original.x = k562_scrna_chr14,
  chr = "chr14", assay = "rna"
)
#> Number of means fewer than 4. Using Bayes instead of JSE.
#> 108 bins created...