getDenoisedMatrix: Wrapper to denoise a correlation matrix using a Random Matrix...

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

Wrapper to denoise a correlation matrix using a Random Matrix Theory approach

Description

Wrapper to denoise a correlation matrix using a Random Matrix Theory approach

Usage

getDenoisedCorMatrix(
  obj,
  res = 1e+06,
  chr = "chr14",
  genome = c("hg19", "hg38", "mm9", "mm10"),
  iter = 2,
  targets = NULL,
  prior.means = NULL,
  assay = c("rna", "atac", "array")
)

Arguments

obj	SummarizedExperiment object with rowRanges for each feature and colnames
res	The resolution desired (default is a megabase 1e6)
chr	Which chromosome to perform the denoising
genome	Which genome (default is hg19)
iter	How many iterations to perform denoising
targets	Samples/cells to shrink towards
prior.means	The means of the bin-level prior distribution (default will compute them for you)
assay	What assay type this is ("rna", "atac")

Value

A denoised correlation matrix object for plotting with plotCorMatrix

Examples

data("k562_scrna_chr14", package = "compartmap")
denoised_cor_mat <- getDenoisedCorMatrix(k562_scrna_chr14, genome = "hg19", assay = "rna")

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