[BioC] Limma: All probes come out as significant

[Daniel Brewer]

Sean Davis wrote:
> On Thursday 12 April 2007 06:24, Daniel Brewer wrote:
>> Hello,
>>
>> I have a curious problem involving Limma.  I have an ExpressionSet
>> object (called Seminoma) that contains the results of 18 samples (12
>> tumours and 6 normals).  The only strange thing I have done is to join
>> Affymetrix U133A and B results (renaming the probes so that there is no
>> overlap).
>> ...
>> As you can see, all the probes appear to be significantly differentially
>> expressed.  I am sure this should not be the case, especially after
>> examining a number of different probes.  For example, on probe "117_at"
>> if I run a t-test() it produces a p-value of 0.1034 (no adjustment)
>> whereas limma suggests it is 8.4e-07 (or 2.45e-06).
>>
>> My only thought is that something must be happening in the eBayes step.
>>  Can anyone help me on what I might be doing wrong?
> 
> Your design vector will test that the probes are significantly different from 
> 0.  You probably want to include an intercept term or (I find it more natural 
> for two groups) define the two groups explicitly and then use a contrast 
> matrix to get the difference between the two.  In either case, your design 
> matrix needs at least two columns.
> 
> Sean

Thanks for that.  You are completely correct and it seems I have made a
fundamental error with limma when working with 1-channel arrays (I
learnt it using 2-channel).  So was  my design matrix basically
detecting probes that are different from zero in only the tumour
samples? i.e. the normal samples were ignored.

Many thanks again

Daniel

-- 
**************************************************************

Daniel Brewer, Ph.D.
Institute of Cancer Research
Email: daniel.brewer at icr.ac.uk

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The Institute of Cancer Research: Royal Cancer Hospital, a charitable Company Limited by Guarantee, Registered in England under Company No. 534147 with its Registered Office at 123 Old Brompton Road, London SW7 3RP.

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