[BioC] extracting significant genes using limma

[James W. MacDonald]

Naomi Altman wrote:
> 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.

Yes you can. However, depending on how you set up your design matrix you 
may or may not get the comparisons you are looking for.

>>
>>I didn't get the difference between decideTests() and classifyTests().

decideTests() is a wrapper function that calls classifyTestsP() or 
classifyTestsF() if you select method = "heirarchical" or "nestedF", 
respectively. decideTests() also uses different default p-value cutoffs 
and multiplicity corrections. Looking at the code for decideTests() may 
help you understand the differences.

HTH,

Jim


>>
>>I would be happy for any help you can give me.
>>
>>THX
>>
>>Assa
>>
>>On 3/13/06, Naomi Altman 
>><<mailto:naomi at stat.psu.edu>naomi at 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 at stat.math.ethz.ch>Bioconductor at 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]]
> 
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-- 
James W. MacDonald, M.S.
Biostatistician
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
1500 E. Medical Center Drive
7410 CCGC
Ann Arbor MI 48109
734-647-5623