solveSVR {DWLS}

solveSVR

Description

Performs a support vector regression (SVR). First, the data is scaled then it solves for the SVR. An svm model is used with the following specifications nu=0.5,scale = TRUE, type = "nu-regression", kernel ="linear",cost = 1.

Nu-support vector regression was performed using the svm function in the e1071 package in R. Parameters were set to nu = 0.5, type = “nu-regression”, kernel = “linear”, cost = 1, and all others to the default values. Bulk data and signature matrices were scaled to -1, 1. These parameter and scaling choices match those specified in Schelker et al. in their MATLAB code, accessed through https://figshare.com/s/865e694ad06d5857db4b. As in Newman et al., model coefficients are extracted from the svm model using t(model$coefs) model$SV, and any negative coefficients are set to zero. The coefficients are then scaled by the sum of the coefficients, such that the scaled coefficients will sum to one.

Citations: Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).

Schelker, M. et al. Estimation of immune cell content in tumor tissue using single-cell RNA-seq data. Nat. Commun. 8, 2032 (2017).

Usage

solveSVR(S, B)

Arguments

S

List output from trimData$sig (S)

B

List output from trimData$bulk (B)

Value

Value (SVR)

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
solveSVR(S, B)