

Hi

I am analysing an Illumina data set and I am currently using the lumi package. In the document that follows this package there is one example and I have followed this example but it is one thing that I am worried about. After the QC and normalization the genes that are unexpressed are removed:

presentCount <- pData(featureData(lumi.N))$presentCount
selDataMatrix <- dataMatrix[presentCount > 0, ]

and selDataMatrix is used together with the limma package to find differentially expressed genes.

When I apply this to my data my data matrix goes from 

> dim(dataMatrix)
[1] 46713    23

to

> dim(selDataMatrix)
[1] 14194    23

Which I think is a big reduction in the number of rows. 

When I look for differentially expressed genes I will get 743 from the small data set and 441 from the big data set, and they have 424 in common. 

My question is: Is it a good idea to do this filtering, how does it affect the statistics done later? And is there more genes found as differentially expressed in the small data set because it is now a smaller “area” to look for differentially expressed genes?

Regards,
Ingrid

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