[BioC] Evaluating differentiall expressed genes

[Holger Schwender]

This is a bug in the example section of the help files for the function sam.
It should actually be


# The matrix containing the d-values, q-values etc. of the
# differentially expressed genes can be obtained by
sum.sam3 at mat.sig


and not sam.out at mat.sig. So in your case,

> sam.sum at mat.sig

will give you the above matrix.

Sorry for this bug. I will fix it pretty soon. You can find a correct (and
tested) description in the vignette of siggenes. See

> vignette("siggenes")

or if you use Windows click on Vignettes --> siggenes --> siggenes.

Best,
Holger


> --- Ursprüngliche Nachricht ---
> Von: Tim Smith <tim_smith_666 at yahoo.com>
> An: Sean Davis <sdavis2 at mail.nih.gov>, Bioconductor
> <bioconductor at stat.math.ethz.ch>
> Betreff: Re: [BioC] Evaluating differentiall expressed genes
> Datum: Wed, 7 Dec 2005 15:17:34 -0800 (PST)
> 
> 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 at 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 at 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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