A function to adjust gene expression data before network inference
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 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)
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