getDenoisedMatrix {compartmap} | R Documentation

## 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")
    )

### 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")

[Package compartmap version 1.10.0 Index]