getDenoisedMatrix {compartmap} | R Documentation |
Wrapper to denoise a correlation matrix using a Random Matrix Theory approach
getDenoisedCorMatrix( obj, res = 1e+06, chr = "chr14", genome = c("hg19", "hg38", "mm9", "mm10"), iter = 2, targets = NULL, prior.means = NULL, assay = c("rna", "atac") )
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") |
A denoised correlation matrix object for plotting with plotCorMatrix
data("k562_scrna_chr14", package = "compartmap") denoised_cor_mat <- getDenoisedCorMatrix(k562_scrna_chr14, genome = "hg19", assay = "rna")