DCrr: Perform robust regression

In federicomarini/quantiseqr: Quantification of the Tumor Immune contexture from RNA-seq data

Perform robust regression

Description

Perform robust regression

Usage

DCrr(b, A, method = c("hampel", "huber", "bisquare"), scaling = NULL)

Arguments

b	Numeric vector containing the right-hand side of the quadratic function to be minimized.
A	Numeric matrix containing the coefficients of the quadratic function to be minimized.
method	Character specifying the robust regression method to be used among deconvolution methods: "hampel", "huber", or "bisquare". Default: "hampel".
scaling	A vector of scaling factors to by applied to the estimates. Its length should equal the number of columns of A.

Details

The MASS::rlm() function is used as underlying framework. Please refer to that function for more details.

Value

A vector containing robust least-square estimates.

Examples

data(dataset_racle)
mixture <- dataset_racle$expr_mat
signature.file <- system.file(
  "extdata", "TIL10_signature.txt", package = "quantiseqr", mustWork = TRUE)
signature <- read.table(signature.file, header = TRUE, sep = "\t", row.names = 1)
scaling.file <- system.file(
  "extdata", "TIL10_mRNA_scaling.txt", package = "quantiseqr", mustWork = TRUE)
scaling <- as.vector(
  as.matrix(read.table(scaling.file, header = FALSE, sep = "\t", row.names = 1)))

cgenes <- intersect(rownames(signature), rownames(mixture))
b <- as.vector(as.matrix(mixture[cgenes,1, drop=FALSE]))
A <- as.matrix(signature[cgenes,])

# cellfrac <- quantiseqr:::DCrr(b = b, A = A, scaling = scaling)

federicomarini/quantiseqr documentation built on May 7, 2024, 9:50 a.m.