[BioC] Limma question

[Niccolò Bassani]

Actually, this happens only for one of the investigated contrasts. I
know that's strange, and I'm aware that some mistake must have
happened when collecting and/or importing data but it seemed quite
strange because also "viewing" data I could see no difference at all
between intensities.

I'll try to figure out what's happened with this crazy dataset and
I'll let you know, by the way thanx for the answer!

2011/12/19 Gordon K Smyth <smyth at wehi.edu.au>:
> 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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