sva_network {sva} | R Documentation |
This function corrects a gene expression matrix prior to network inference by returning the residuals after regressing out the top principal components. The number of principal components to remove can be determined using a permutation-based approach using the "num.sv" function with method = "be"
sva_network(dat, n.pc)
dat |
The raw gene expression data matrix (with variables in rows and samples in columns) |
n.pc |
The number of principal components to remove |
dat.adjusted Cleaned gene expression data matrix with the top prinicpal components removed
library(bladderbatch) data(bladderdata) dat <- bladderEset[1:5000,] pheno = pData(dat) edata = exprs(dat) mod = model.matrix(~as.factor(cancer), data=pheno) n.pc = num.sv(edata, mod, method="be") dat.adjusted = sva_network(edata, n.pc)