## load example data
data(example.lumi)
## get the gene expression mean for one chip
u <- exprs(example.lumi)[,1]
## get the gene standard deviation for one chip
std <- se.exprs(example.lumi)[,1]
## do variance stabilizing transform
transformedU <- vst(u, std)
## do inverse transform and recover the raw data
parameter <- attr(transformedU, 'parameter')
transformFun <- attr(transformedU, 'transformFun')
recoveredU <- inverseVST(transformedU, fun=transformFun, parameter=parameter)
## compare with the raw data
print(u[1:5])
print(recoveredU[1:5])
## do inverse transform of the VST + RSN processed data
lumi.N <- lumiExpresso(example.lumi[,1:2])
## Inverse transform.
## Note: as the normalization is involved, the processed data will be different from the raw data.
lumi.N.raw <- inverseVST(lumi.N)
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