[BioC] LIMMA, SAM & clustering

[James W. MacDonald]

daniela marconi wrote:
> 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)

When you say you compared UM vs (M1+M2), is that what you used in your 
call to makeContrasts() (e.g., makeContrasts(UM - (M1 + M2))? If so, you 
are comparing UM to the *sum* of M1 and M2 instead of the *mean* of M1 
and M2, which would probably explain the differences.

Best,

Jim


> 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
> 
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-- 
James W. MacDonald
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
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