Dear Gordon,

Thank you very much for your information.

You are right-I am comparing each cell type to the average of all the
others. Ideally, I want to compare each cell type to the others pairwisely
and find the signature genes as you suggested. I tried this before, but I am
afraid that I did not take the full advantages of limma as I am new here.
Here is my problem. I am comparing 24 blood cell types (92 arrays in total).
Following are the steps that I took. The pairwise comparison take dozens of
ligands. Then I used topTable to find overexpressed genes from each
comparison, and finally do the 'intersect'. I believe that there is an easy
way to do all the pairwise comparisons and use decideTests(). Would you mind
giving me some hints on that?

Thank you very much.
Wendy

f<-factor(samplenames)   #sampelenames = colnames of 92 arrays with
replicates have the same name
design<-model.matrix(~0+f)
fit<-lmFit(es.mx,design)
fit<-eBayes(fit)

contrast.matrix<-makeContrasts(fBASO1-fBCELLA1, fBASO1-fBCELLA2.....



   fBASO1 fBCELLA1 fBCELLA2 fBCELLA3 ...
1       1        0        0        0     ...
2       1        0        0        0    ...
3       1        0        0        0      ...
4       0        1        0        0     ...
...
92      0        0        0        0   ...


On 10 April 2011 18:30, Gordon K Smyth <smyth@wehi.edu.au> wrote:

> Dear Wendy,
>
> From your email, I assume that you have found signature genes by comparing
> each cell type to all the other cell types treated as one group.  As you
> have correctly observed, this does not take account of consistency within
> the other cell types.  Another way to find signature genes, that I think is
> superior, is to choose signature genes to be those genes that are uniquely
> higher or lower in the relevant cell type with respect to each of the other
> cell types individually.  In other words, a positive signature gene is
> higher in the relevant cell type against every other cell type, not just
> against the average of the other cell types.  This was the method used in:
>
> Lim E, Vaillant F, Wu D, Forrest NC, Pal B, Hart AH, Asselin-Labat ML,
> Gyorki DE, Ward T, Partanen A, Feleppa F, Huschtscha LI, Thorne HJ; kConFab,
> Fox SB, Yan M, French JD, Brown MA, Smyth GK, Visvader JE, Lindeman GJ.
> Aberrant luminal progenitors as the candidate target population for basal
> tumor development in BRCA1 mutation carriers.  Nature Medicine 2009.
>
> to find stem cell signature genes.  If you do it this way, consistency
> within the cell types is automatically taken care off, because the t-tests
> will only choose genes with consistent behaviour.   limma can do all the
> relevant pairwise tests for you in a couple of lines, then use decideTests()
> to choose the signature genes.
>
> Best wishes
> Gordon
>
> ---------------------------------------------
> Professor Gordon K Smyth,
> NHMRC Senior Research Fellow,
> Bioinformatics Division,
> Walter and Eliza Hall Institute of Medical Research,
> 1G Royal Parade, Parkville, Vic 3052, Australia.
> Tel: (03) 9345 2326, Fax (03) 9347 0852,
> smyth@wehi.edu.au
> http://www.wehi.edu.au
> http://www.statsci.org/smyth
>
>
>  Date: Sat, 9 Apr 2011 19:57:25 -0400
>> From: Wendy Qiao <wendy2.qiao@gmail.com>
>> To: bioconductor@r-project.org
>> Subject: [BioC] identifying consistently expressed genes between
>>        replicates
>>
>> Hi all,
>>
>> I am comparing a number of cell types, and am wanting to find the
>> signature
>> genes of each cell type. I used the limma package to do this. The
>> signature
>> genes of a given cell type are found by the fold different between the
>> given
>> cell type and grand mean of all the cell types, as well as the BH-adjusted
>> p-values. I want to add another condition to test the consistency of
>> expression levels of the selected genes for each cell type. I can do this
>> by
>> looking at the standard deviations of gene expressions between replicates.
>> I
>> am just wondering if there is any function in limma or other BioConductor
>> package to do this.
>>
>> Thank you in advance,
>> Wendy
>>
>
> ______________________________________________________________________
> The information in this email is confidential and inte...{{dropped:10}}

