Dear Assa,
This one will need to go to the list.  I only use topTable.

--Naomi

p.s. Please post all questions directly to the listserv.  That way 
you can get more help than any one person can give, and no-one is 
over-burdened.

At 03:29 AM 3/14/2006, you wrote:
>Hi,
>
>I was wondering if I can use decideTests()-function in my tests, 
>even if i didn't use any  contrast matrix.
>
>I didn't get the difference between decideTests() and classifyTests().
>
>I would be happy for any help you can give me.
>
>THX
>
>Assa
>
>On 3/13/06, Naomi Altman 
><<mailto:naomi@stat.psu.edu>naomi@stat.psu.edu> wrote:
>Since you used "adjust=fdr", the p-value column of the TopTable are
>the "adjusted p-values" after fdr (which I think of as q-values).
>
>You can either pick some q-value you want to use to select the
>significantly differentially expressing genes, or you can pick some
>number of genes, and report the q-value of the least significant of these.
>
>--Naomi
>
>At 10:17 AM 3/13/2006, Assa Yeroslaviz wrote:
> >Hi,
> >
> >I know this theme is an old one, but I look all over the archives and didn't
> >find any help regarding this subject.
> >Using Affymetrix chips I compared two groups (Control vs compound) with the
> >limma procedure.
> >I made an affybatch Object using ReadAffy(), normalised the data with the
> >RMA algorithm and fitted a linear model with lmFit.
> >
> > >affy <- ReadAffy(filenames=vec)
> > >eset <- rma(affy)
> > >design <- cbind(Control=1,AE0627vsCT=c(rep(0,6),rep(1,4)))
> >
> >my design matrix looks like that (I have 6 control  and 4 treated arrays):
> > > design
> >       Control AE143vsCT
> >  [1,]       1         0
> >  [2,]       1         0
> >  [3,]       1         0
> >  [4,]       1         0
> >  [5,]       1         0
> >  [6,]       1         0
> >  [7,]       1         1
> >  [8,]       1         1
> >  [9,]       1         1
> >[10,]       1         1
> >
> >so I don't need any contrast matrix.
> >The list is 22,810 genes long. But not all of them can be significant. I
> >hope!!!
> >
> >I sorted the genes with:
> > >sig_table <- topTable(fit_e, coef=2, number=6000, adjust="fdr", sort.by=
> >"P")
> >
> >I've chosen 6000 as an arbitrary value, but I still don't know how many
> >genes are siginificant.
> >
> >My question(s) is(are):
> >
> >1. How do I find out how many genes are significantly differentially
> >expressed using a define p-value or FDR?
> >     Can I use here the decideTests() function although I don't have any
> >contrasts?
> >
> >2. In SAM one can look for the false discovery rates using the different
> >delta-values.
> >    Is it possible to set a fixed FDR-Value in Limma?
> >    Where Do I find the FDR rates of my significant genes?
> >
> >3. Is there a possibility (like in SAM) to show the results in a graphic (
> >scatter plot etc.)?
> >
> >Every comment and suggestion would be appreciated!
> >
> >THX
> >
> >Assa
> >
> >--
> >Assa Yeroslaviz
> >Loetzener Str. 15
> >51373 Leverkusen
> >
> >         [[alternative HTML version deleted]]
> >
> >_______________________________________________
> >Bioconductor mailing list
> ><mailto:Bioconductor@stat.math.ethz.ch>Bioconductor@stat.math.ethz.ch
> >https://stat.ethz.ch/mailman/listinfo/bioconductor
>
>Naomi S. Altman                                814-865-3791 (voice)
>Associate Professor
>Dept. of Statistics                              814-863-7114 (fax)
>Penn State University                         814-865-1348 (Statistics)
>University Park, PA 16802-2111
>
>
>
>
>--
>Assa Yeroslaviz
>Loetzener Str. 15
>51373 Leverkusen

Naomi S. Altman                                814-865-3791 (voice)
Associate Professor
Dept. of Statistics                              814-863-7114 (fax)
Penn State University                         814-865-1348 (Statistics)
University Park, PA 16802-2111

	[[alternative HTML version deleted]]

