estRMT: Denoising of Covariance matrix using Random Matrix Theory

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

Denoising of Covariance matrix using Random Matrix Theory

Description

Denoising of Covariance matrix using Random Matrix Theory

Usage

estRMT(
  R,
  Q = NA,
  cutoff = c("max", "each"),
  eigenTreat = c("average", "delete"),
  numEig = 1
)

Arguments

R	input matrix
Q	ratio of rows/size. Can be supplied externally or fit using data
cutoff	takes two values max/each. If cutoff is max, Q is fitted and cutoff for eigenvalues is calculated. If cutoff is each, Q is set to row/size. Individual cutoff for each eigenvalue is calculated and used for filteration.
eigenTreat	takes 2 values, average/delete. If average then the noisy eigenvalues are averged and each value is replaced by average. If delete then noisy eigenvalues are ignored and the diagonal entries of the correlation matrix are replaced with 1 to make the matrix psd.
numEig	number of eigenvalues that are known for variance calculation. Default is set to 1. If numEig = 0 then variance is assumed to be 1.

Details

This method takes in data as a matrix object. It then fits a marchenko pastur density to eigenvalues of the correlation matrix. All eigenvalues above the cutoff are retained and ones below the cutoff are replaced such that the trace of the correlation matrix is 1 or non-significant eigenvalues are deleted and diagonal of correlation matrix is changed to 1. Finally, correlation matrix is converted to covariance matrix. This function was taken and modified from the covmat package (https://github.com/cran/covmat) which has since been deprecated on CRAN.

Value

A denoised RMT object

Author(s)

Rohit Arora

Examples

rand_cor_mat <- cor(matrix(rnorm(100), nrow = 10))
denoised_rand_cor_mat <- estRMT(rand_cor_mat)$cov
       

biobenkj/compartmentalizer documentation built on Oct. 13, 2023, 5:26 p.m.