sva_network: A function to adjust gene expression data before network...
In sva: Surrogate Variable Analysis

Description Usage Arguments Value Examples

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"

1	sva_network(dat, n.pc)

`dat`	The uncorrected normalized gene expression data matrix with samples in rows and genes 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,]

edata = exprs(dat)
mod = matrix(1, nrow = dim(dat)[2], ncol = 1)

n.pc = num.sv(edata, mod, method="be")
dat.adjusted = sva_network(t(edata), n.pc)