solveDampenedWLS: solveDampenedWLS

In DWLS: Gene Expression Deconvolution Using Dampened Weighted Least Squares

solveDampenedWLS

Description

Dampened weighted least squares (DLWS) is an estimation method for gene expression deconvolution, in which the cell-type composition of a bulk RNA-seq data set is computationally inferred. This method corrects common biases towards cell types that are characterized by highly expressed genes and/or are highly prevalent, to provide accurate detection across diverse cell types. To begin, the user must input a bulk RNA-seq data set, along with a labeled representative single-cell RNA-seq data set that will serve to generate cell-type-specific gene expression profiles. Ideally, the single-cell data set will contain cells from all cell types that may be found in the bulk data. DWLS will return the cell-type composition of the bulk data. First, solve OLS then use the solution to find a starting point for the weights. Next, the dampened weighted least squares is performed. The weights are iterated until convergence then the dampening constant for weights is found using cross-validation (with decreasing step size for convergence).

Note: The function uses solveDampenedWLSj() and findDampeningConstant().

Usage

solveDampenedWLS(S, B)

Arguments

S	List output from trimData
B	List output from trimData

Value

value (Dampened weighted least squares estimation values)

Examples

#Sig
#url <- "https://github.com/sistia01/DWLS/raw/main/inst/extdata/Sig.RData"
#dest <- "data/Sig.RData"
#download.file(url, dest)
#load("data/Sig.RData")
load(system.file("extdata", "Sig.RData", package = "DWLS"))

#dataBulk
#url <- "https://github.com/sistia01/DWLS/raw/main/inst/extdata/dataBulk.RData"
#dest <- "data/dataBulk.RData"
#download.file(url, dest)
#load("data/dataBulk.RData")
load(system.file("extdata", "dataBulk.RData", package = "DWLS"))

trimmed <- trimData(Sig, dataBulk)
S <- trimmed$sig
B <- trimmed$bulk
solveDampenedWLS(S, B)

DWLS documentation built on May 24, 2022, 5:07 p.m.