[BioC] limma interaction model example

[James W. MacDonald]

On 3/18/13 12:27 PM, limmauser [guest] wrote:
> Can someone please explain the example given in the limma vignette on page 45. It is an example of the classic interaction model. There are two different scenarios that are shown here, one without setting up contrasts, and one with setting up contrasts.
>
> My question is specifically regarding the adj. pvalues that are reported. The reported p-values are different for each scenario. Why is that? What is the p-value corresponding to in the first scenario? What is it corresponding to in the second scenario?

When you generate a topTable and you don't specify a contrast, then you 
get an F-test in which you are testing that any of the coefficients not 
equal to zero.

This doesn't make any sense in the first case, where you have an 
intercept, because the intercept is estimating the mean expression of 
one sample type. You don't really care if the mean expression value is 
equal to zero or not; instead you are interested in knowing if the 
difference between two sample types is equal to zero. In other words, 
microarray data are not meaningful except in the context of a comparison 
between samples.

If you do something like

topTable(fitfromscenario1, coef=2:4)

You should get the same results as scenario 2.

Best,

Jim


>
> Here are the results from my data set for scenario 1:
> ID X.Intercept.    density8       treatT density8.treatT  AveExpr        F      P.Value    adj.P.Val
> 8116520 8116520     13.62623  0.06169053  0.061607654     -0.03356050 13.67948 278969.1 2.031311e-37 1.919204e-33
> 7894098 7894098     13.87349 -0.10169570  0.042710084     -0.01660554 13.83984 276812.7 2.157184e-37 1.919204e-33
> 8153903 8153903     13.66958  0.05382805 -0.007617839     -0.02515061 13.68640 252543.6 4.391650e-37 1.919204e-33
> 8038086 8038086     13.65395  0.06105315  0.041262169     -0.06775548 13.68817 248358.7 4.998637e-37 1.919204e-33
> 8179174 8179174     13.51915 -0.03694281  0.001696369      0.04665425 13.51319 242354.9 6.042135e-37 1.919204e-33
>
> When i set up the contrasts as shown in the example, and pull out info. for the first probeset id in the list above(8116520), the p-values are different:
>
> ID TvsUinlowDensity TvsUinhighDensity       Diff  AveExpr        F   P.Value adj.P.Val
> 8116520 8116520       0.06160765        0.02804716 -0.0335605 13.67948 1.707745 0.2136743 0.3896945
>
> I also have two conditions density and treatment. Any insight/clarification will be appreciated.
>
> Thanks!
>
>
>   -- output of sessionInfo():
>
> R version 2.15.1 (2012-06-22)
> Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit)
>
> locale:
> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
>
> attached base packages:
> [1] stats     graphics  grDevices utils     datasets  methods   base
>
> other attached packages:
> [1] limma_3.14.1           hugene10stv1cdf_2.11.0 AnnotationDbi_1.20.3   affy_1.36.0            Biobase_2.18.0
> [6] BiocGenerics_0.4.0
>
> loaded via a namespace (and not attached):
>   [1] affyio_1.26.0         BiocInstaller_1.8.3   DBI_0.2-5             IRanges_1.16.4        parallel_2.15.1
>   [6] preprocessCore_1.20.0 RSQLite_0.11.2        stats4_2.15.1         tools_2.15.1          zlibbioc_1.4.0
>
>
> --
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>
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