Non-parametric bootstrapping of compartments and summarization of bootstraps/compute confidence intervals

Usage

bootstrapCompartments(
  obj,
  original.obj,
  bootstrap.samples = 1000,
  chr = "chr14",
  assay = c("rna", "atac", "array"),
  parallel = TRUE,
  cores = 2,
  targets = NULL,
  res = 1000000,
  genome = c("hg19", "hg38", "mm9", "mm10"),
  q = 0.95,
  svd = NULL,
  group = FALSE,
  bootstrap.means = NULL
)

Arguments

obj: List object of computed compartments for a sample with 'pc' and 'gr' as elements
original.obj: The original, full input SummarizedExperiment of all samples/cells
bootstrap.samples: How many bootstraps to run
chr: Which chromosome to operate on
assay: What sort of assay are we working on
parallel: Whether to run the bootstrapping in parallel
cores: How many cores to use for parallel processing
targets: Targets to shrink towards
res: The compartment resolution
genome: What genome are we working on
q: What sort of confidence intervals are we computing (e.g. 0.95 for 95 percentCI)
svd: The original compartment calls as a GRanges object
group: Whether this is for group-level inference
bootstrap.means: Pre-computed bootstrap means matrix

Value

Compartment estimates with summarized bootstraps and confidence intervals

Examples


# this needs a good example