[BioC] Limma table results [time course analysis]

[Gordon K Smyth]

Dear Stefania,

You have a replicated time course experiment with three time points. 
This is dealt with in detail in the limma User's Guide in Section 9.6.1 
"Replicate time points".  You should be able to apply the advice in that 
section to your data without any problems.

The limma User's Guide tells you that spline curves are only for time 
course experiments with many time points.  You don't have many time points 
-- only three.  There is no advantage in using spline curves for a time 
course epxeriment with only three time points.  You cannot achieve 
anything that the simpler analysis presented in Section 9.6.1 would not 
achieve more explicitly.

Given that you have fitted spline curves, the test that you have conducted 
will correctly select genes whose change over time is different between 
the three genotypes.  However you cannot interpret the individual logFC 
columns in the toptable.  These are simply mathematically defined basis 
vectors for the spline curve -- they have no meaningful interpretation as 
individual columns.

Best wishes
Gordon


------ original message ------
[BioC] Limma table results

Stefania [guest] guest at bioconductor.org
Tue Apr 15 11:09:46 CEST 2014

Hi all,

I am using LIMMA package to analyse RNA-Seq data from a time course 
experiment. We have 3 time point, one control, two treatments and all the 
samples are in triplicate.

I used the spline method as suggested in the tutorial so I have:

yS<-DGEList(counts=limma.reads.counts)
SplineTargets=data.frame(File=sampleFileNames,Group=c(rep("Control",9),rep("Ofx",9),rep("MitC",9)),Time=c(rep(0,3),rep(30,3),rep(180,3),rep(0,3),rep(30,3),rep(180,3),rep(0,3),rep(30,3),rep(180,3)))
X <- ns(SplineTargets$Time, df=2)
Group <- factor(SplineTargets$Group)
spline.design <- model.matrix(~Group*X)
v <- voom(yS,spline.design,plot=FALSE)
fitSpline <- lmFit(v,spline.design)
fitSpline <- eBayes(fitSpline)

To extract the values of interest:

data.as.top.table = topTable(fitSpline,coef=6:9,n=500)

In the table result I have the columns

object..a..	GroupMitC.X1	GroupOfx.X1	GroupMitC.X2 
GroupOfx.X2	AveExpr	F	P.Value	adj.P.Val

Could you please help me in understanding the meaning of GroupMitC.X1, 
GroupOfx.X1, GroupMitC.X2, GroupOfx.X2 columns? How can I interpret this 
values? How are they associated with the behaviour of the gene?

Thanks in advance,

Stefania

  -- output of sessionInfo():

R version 3.0.1 (2013-05-16)
Platform: x86_64-apple-darwin10.8.0 (64-bit)

locale:
[1] it_IT.UTF-8/it_IT.UTF-8/it_IT.UTF-8/C/it_IT.UTF-8/it_IT.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

loaded via a namespace (and not attached):
[1] tools_3.0.1

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