[BioC] LIMMA, SAM & clustering

[daniela marconi]

Hi,
I have analyzed a data set with 2 different classes UM and M(with subcklasses M1 and M2)
.
I have fitted the linear model  with limma for the coefficients UM, M1 and M2 and I have compared UM vs (M1+M2).I found a significant change (adjuste p-value<0.0001 and B>2) for 236 genes
I did the analysis also with SAM (with the function samrocNboot in the package SAGx)comparing UM vs M.I found a significant change(adjusted p-value <0.001) for 285 genes.

I had also 29 genes in common between the two anlalysis.

For visualization pouposes for both results I used, on normalized data matrix, a hierarchical clustering (with pearson correlation as distance and average as method). 
But with the SAM's genes  I obtained a good clustering, with a good separation between the two classes. For LIMMA's genes I couldn't succed to obtain a good separation between the two classes.
Have you any idea about? May be is SAM closer to a mesure of correlation, withou fitting any linear model, than LIMMA?
Thanks for any suggestion
Daniela