Thanks for the replies Sean and Naomi. I feel I'm on the right track.
   
  I did the following:


    sam.out <- sam(zz, ncl, method = "d.stat", delta = NULL, n.delta = 10, p0 = NA,
  lambda = seq(0, 0.95, 0.05), ncs.value = "max", ncs.weights = NULL,
  gene.names = dimnames(zz)[[1]])
  
  
  sam.sum <-summary(sam.out,2,ll=FALSE)
   
   
  Now, I wanted to get to the d-value matrix. The documentation says that I can get this by the line:
   
         # The matrix containing the d-values, q-values etc. of the
       # differentially expressed genes can be obtained by
     

  sam.outATmat.sig     # sam.out@mat.sig
   
  but when I do this, I get :

  Error: no slot of name "mat.sig" for this object of class "SAM"
   
  How can I access the matrix to get at the underlying d-values etc..??
   
  many thanks.
  
Sean Davis <sdavis2@mail.nih.gov> wrote:

  


On 12/7/05 12:49 PM, "Tim Smith" wrote:

> Hi all,
> 
> A newbie to Bioconductor, so please forgive the naive question..
> 
> I had two sets of data (normalized affy data). The first set contains
> cancerous expression data for 40 patients, and the second set contains
> expression data for 30 'normal' (i.e non cancerous) patients.
> 
> I wanted to find out the genes that are 'differentially expressed' in the
> two sets. Which package, and which functions can I use? Any reasonably popular
> method/package would work for me.

Welcome to bioconductor.

See here for a full list. Best to do a little reading, as each method has
potentially different assumptions, etc.

http://www.bioconductor.org/packages/bioc/1.7/src/contrib/html/

That said, look at siggenes which contains the SAM method; the multtest
package for traditional t-tests; and the limma package.

And just to be sure, when you say you have two sets of normalized data, you
mean that they were normalized together or separately?

Sean


  


			
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