Dear all,

Thank you for your feedback!
Everyone can guess that I am not a statistician....
:)

Here is the results I get with my samples running SAM.
> sam.out<-sam(normalized, sam.c1, B=100,var.equal=TRUE, med=TRUE)

SAM Analysis for the Two-Class Unpaired Case Assuming Equal Variances 
 
Delta   p0 False Called    FDR
1    0.1 0.78 16417  26195 0.4887
2    1.3 0.78    78    751 0.0810
3    2.5 0.78     9    335 0.0209
4    3.7 0.78     3    146 0.0160
5    4.9 0.78     1     71 0.0110
6    6.1 0.78     0     21      0
7    7.3 0.78     0      6      0
8    8.5 0.78     0      4      0
9    9.8 0.78     0      3      0
10  11.0 0.78     0      0      0

For me having 335 genes with only 9 false positve is already satisfactory. Even 20% FDR would be ok as I will have to confirm the microarray data by other methods...
Do you think I can use those data as a first way to select candidate genes? I would say yes, but I would like advise from the specialists to see if I am not doing something wrong!!
Is it better to use the limma package that you suggested??

Cheers

Olivier

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