[BioC] Limma question

[Gordon K Smyth]

Oops, correcting a typo: if the normalized intensities were all equal for 
any given miRNA, limma would produce t-stat=0 and p-value=1.

Gordon

On Mon, 19 Dec 2011, Gordon K Smyth wrote:

> Dear Niccolo,
>
> I have to tell you that what you claim to have observed is not possible. If 
> the normalized intensities were all equal, then limma would produce t-stat=0 
> and p-value=0 for any contrast between conditions.  So it would seem that 
> you've made a mistake somewhere in collating results.
>
> Your email does not contain complete code, so there isn't any way for me to 
> help you find the error.
>
> Best wishes
> Gordon
>
>> Date: Fri, 16 Dec 2011 16:58:55 +0100
>> From: Niccol? Bassani <biostatistica at gmail.com>
>> To: <bioconductor at stat.math.ethz.ch>
>> Subject: [BioC] Limma question
>> 
>> Dear users,
>> I'm having some troubles in figuring out what's going on in limma.
>> I've got some expression data from Agilent microRNA platform, I've
>> pre-processed them, and wanted to do some easy differential expression
>> analysis. Out of 1368 miRNAs (no filtering performed) there are 758 of
>> them which show EXACTLY the same value on all of the 24 arrays
>> involved. Arrays are divided in 3 groups, 8 arrays in each group.
>> Data look like this (in matrix form, first rows and columns):
>>
>>        LN9      LN10      LN11      LN12      LN13      LN14
>> 1 12.431022 12.186179 13.136163 12.121403 12.643895 12.756163
>> 2  1.137504  1.137504  1.137504  1.137504  1.137504  1.137504
>> 3  1.137504  1.137504  1.137504  1.137504  1.137504  1.137504
>> 4  1.137504  1.137504  1.137504  1.137504  1.137504  1.137504
>> 5  1.137504  1.137504  1.137504  1.137504  1.137504  1.137504
>> 6  1.137504  1.137504  1.137504  1.137504  1.137504  1.137504
>> 
>> I specify the design matrix, and run easy differential expression code:
>> 
>> contrasts = cbind(AvsB = c(-1,1,0),AvsC = c(1,0,-1),AvsB_C =
>> c(1,-1/2,-1/2),A_BvsC = c(1/2,1/2,-1))
>> contrasts
>>     AvsB AvsC AvsB_C A_BvsC
>> [1,]   -1    1    1.0    0.5
>> [2,]    1    0   -0.5    0.5
>> [3,]    0   -1   -0.5   -1.0
>> 
>> fit = lmFit(agilent,design)
>> fit.contrasts = contrasts.fit(fit,contrasts)
>> test = eBayes(fit.contrasts)
>> 
>> The strange (or absurd) thing is that invariant microRNAs appear to be
>> differentially expressed throughout all of the contrasts but the last
>> one!
>> 
>> test
>> $p.value
>>           AvsB       AvsC     AvsB_C    A_BvsC
>> [1,] 0.53958575 0.42970445 0.41866547 0.5748925
>> [2,] 0.03471306 0.03471306 0.01644463 1.0000000
>> [3,] 0.03471306 0.03471306 0.01644463 1.0000000
>> [4,] 0.03471306 0.03471306 0.01644463 1.0000000
>> [5,] 1.00000000 0.23359101 0.48667557 0.1713666
>> 1363 more rows ...
>> 
>> I've drilled into the various limma functions code, but it seems that
>> there's some problem with my data, maybe some kind of
>> approximation...my point is that the last contrast correctly
>> identifies no microRNA differentially expressed, whereas the remaining
>> 3 return me t statistic which are non 0 for invariant miRNAs!!
>> 
>> $t
>>             AvsB       AvsC     AvsB_C     A_BvsC
>> [1,] 6.236028e-01 -0.8051982 -0.8249186 -0.5697255
>> [2,] 2.257614e+00 -2.2576137 -2.6068677  0.0000000
>> [3,] 2.257614e+00 -2.2576137 -2.6068677  0.0000000
>> [4,] 2.257614e+00 -2.2576137 -2.6068677  0.0000000
>> [5,] 1.588357e-14 -1.2263878 -0.7080553 -1.4161107
>> 1363 more rows ...
>> 
>> Any suggestions? I've tried to round the dataset to 4 digits but the
>> problem's still there, only changes the contrast with consistently
>> non-differentially expressed genes...
>> 
>> Thanx, and merry xmas everybody (know it's early, but who knows what
>> will be next...)
>> Niccol?
>

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