num.sv: A function for calculating the number of surrogate variables...
In sva: Surrogate Variable Analysis

Description Usage Arguments Value Examples

This function estimates the number of surrogate variables that should be included in a differential expression model. The default approach is based on a permutation procedure originally prooposed by Buja and Eyuboglu 1992. The function also provides an interface to the asymptotic approach proposed by Leek 2011 Biometrics.

1	num.sv(dat, mod, method = c("be", "leek"), vfilter = NULL, B = 20, seed = NULL)

`dat`	The transformed data matrix with the variables in rows and samples in columns
`mod`	The model matrix being used to fit the data
`method`	One of "be" or "leek" as described in the details section
`vfilter`	You may choose to filter to the vfilter most variable rows before performing the analysis
`B`	The number of permutaitons to use if method = "be"
`seed`	Set a seed when using the permutation approach

n.sv The number of surrogate variables to use in the sva software

library(bladderbatch)
data(bladderdata)
dat <- bladderEset[1:5000,]

pheno = pData(dat)
edata = exprs(dat)
mod = model.matrix(~as.factor(cancer), data=pheno)

n.sv = num.sv(edata,mod,method="leek")