empirical.controls: A function for estimating the probability that each gene is...
In steveneschrich/msva: Surrogate Variable Analysis

Description Usage Arguments Value Examples

This function uses the iteratively reweighted surrogate variable analysis approach to estimate the probability that each gene is an empirical control.

empirical.controls(
  dat,
  mod,
  mod0 = NULL,
  n.sv,
  B = 5,
  type = c("norm", "counts")
)

`dat`	The transformed data matrix with the variables in rows and samples in columns
`mod`	The model matrix being used to fit the data
`mod0`	The null model being compared when fitting the data
`n.sv`	The number of surogate variables to estimate
`B`	The number of iterations of the irwsva algorithm to perform
`type`	If type is norm then standard irwsva is applied, if type is counts, then the moderated log transform is applied first

pcontrol A vector of probabilites that each gene is a control.

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")
pcontrol <- empirical.controls(edata,mod,mod0=NULL,n.sv=n.sv,type="norm")