Dear Gordon,

I apologize about the lack of clarity in my initial post.  In my
experiment, I'm working with three pairs of twins that differ in sleep
duration.  In my targets file, the twins are paired by "Source," and are
grouped by sleep duration - either "short" or "long."  I want try to
identify significantly differentially expressed probes between the "short"
and "long" groups.

> targets
   Sample Group Source
1 short.A short      A
2 short.B short      B
3 short.C short      C
4  long.A  long      A
5  long.B  long      B
6  long.C  long      C


"d" is a data.frame that contains quantile normalized, log2 scaled signal
intensity values, with samples represented in columns.  The data set has
undergone signal intensity and variance filtering steps, resulting in
13,597 rows of values that were used with limma.

> head(d)
           long.A    long.B    long.C  short.A  short.B  short.C
1690577  9.717804 10.068060  9.969054 11.74844 10.84940 12.46522
2120040  9.234160  9.415976  9.463955 10.95052 10.42986 11.84757
3180438  9.724018  9.370251  9.758478 11.32368 10.51315 11.97926
3130324  8.980456  9.163279  9.203548 10.57555 10.13772 11.71771
6380255  9.624958  9.118592  9.364389 11.02301 10.20351 11.85628
6560164 10.097376  9.852433 10.089217 11.55226 10.78285 12.20962


design<-model.matrix(~targets$Source+targets$Group)
fit <- lmFit(d, design)
fit <- eBayes(fit)
results<-topTable(fit, coef="targets$Groupshort", number=nrow(d))

What I'm puzzled about are instances of probes with very small p-values,
when the sample paired ratios vary quite a bit.  Below are a few examples.

> exampleProbes<-c(1450390,1230376,3420451)
> results[results$ID %in% exampleProbes,]
        ID     logFC   AveExpr         t      P.Value    adj.P.Val        B
13 1450390 -1.373309 10.210502 -9.807005 4.353149e-17 4.553059e-14 28.26206
14 1230376  1.439599 10.006936  9.785405 4.905855e-17 4.764637e-14 28.14655
18 3420451 -1.258524  8.028813 -8.903744 6.262297e-15 4.730470e-12 23.45921


> signalIntensities<-d[row.names(d) %in% exampleProbes,]
> signalIntensities
          long.A    long.B    long.C   short.A   short.B   short.C
1450390 9.561842  9.508576  9.501124 10.839129 10.179997 11.672343
1230376 9.108591 10.499186 12.572430  8.752280  8.271599 10.837532
3420451 7.534823  7.447136  7.216693  8.519415  8.080696  9.374113

> ratios<-signalIntensities[1:3]-signalIntensities[4:6]
> ratios
            long.A     long.B    long.C
1450390 -1.2772877 -0.6714210 -2.171219
1230376  0.3563107  2.2275875  1.734898
3420451 -0.9845919 -0.6335606 -2.157420


Thank you very much for any assistance with this.

Best,

Ryan




On Fri, Nov 16, 2012 at 10:36 PM, Gordon K Smyth <smyth@wehi.edu.au> wrote:

> Dear Ryan,
>
> The limma algorithm is very well understood by now, and there are many
> bioinformatications at the FHCRC who could probably answer your question.
>
> I find it hard to make a response to your email because I just see a
> jumble of numbers.  I don't have a firm understanding of what your
> experimental design is, what model you've fitted, what the numbers are that
> you've calculated, or what you want me to see.  To comment more, I'd need
> to see your experimental design and the complete pipeline of commands used
> to generate the output given.
>
> Best wishes
> Gordon
>
> I don't a firm idea of what your experimental design
>
>  Date: Wed, 14 Nov 2012 17:23:43 -0800
>> From: Ryan Basom <rbasom@gmail.com>
>> To: bioconductor@r-project.org
>> Subject: [BioC] limma: instances of highly variable paired ratios, but
>>         very small p-values
>>
>> Hi,
>>
>> I've performed a limma analysis on paired samples that were run on
>> Illumina HT12 arrays, with three replicates in each condition.  I'm a bit
>> troubled by the results though, as there are several probes that have very
>> small adjusted p-values, though when looking at the paired ratio values,
>> they vary quite a bit.  Here are a few examples where the comparison is
>> long-short, and the samples are paired by the letters A,B,C.  After the
>> adj.P.Val column, I've calculated the paired sample ratio values, these
>> three columns are followed by the signal intensities from each sample:
>>
>>    ProbeID TargetID logFC AveExpr P.Value adj.P.Val long-short.A
>> long-short.B long-short.C long.A long.B long.C short.A short.B short.C
>> 1450390 RPL17 -1.3733092649 10.2105020267 4.35314891863083e-17
>> 4.55305891127872e-14 -1.277287712 -0.6714209686 -2.1712191142 9.5618416199
>> 9.5085763086 9.5011242541 10.8391293319 10.1799972771 11.6723433683 1230376
>> ALAS2 1.4395987013 10.0069363572 4.9058551374517e-17 4.76463659313792e-14
>> 0.356310701 2.2275874983 1.7348979044 9.1085909827 10.4991863428
>> 12.5724297981 8.7522802817 8.2715988445 10.8375318936 3420451 RSL24D1
>> -1.2585240828 8.0288125742 6.26229691539969e-15 4.73046950881609e-12
>> -0.9845918613 -0.6335605827 -2.1574198045 7.5348228063 7.4471358372
>> 7.2166929547 8.5194146676 8.0806964199 9.3741127592
>>
>>
>> I'd assumed that limma would have been more sensitive to this and am
>> wondering if anyone could please explain why this may be occurring.
>>
>> Thanks,
>>
>> Ryan
>>
>> __
>> Ryan Basom
>> Systems Analyst/Programmer
>> Genomics Resource
>> Fred Hutchinson Cancer Research Center
>>
>>
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