bootstrapCompartments: Non-parametric bootstrapping of compartments and...

In biobenkj/compartmap: Higher-order chromatin domain inference in single samples from methylation arrays and single cells from scRNA-seq and scATAC-seq

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

Description

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 = 1e+06,
  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

biobenkj/compartmap documentation built on Oct. 18, 2023, 11:11 a.m.