[BioC] time course differential analysis - design matrix
guest at bioconductor.org
Fri Mar 28 10:18:18 CET 2014
I am doing differential expression analysis and I have a question concerning time course experiments (Single-Channel Experimental Designs).
I have one cell line that was treated in 4 different ways. I want to check which genes respond dierently over time for different treatments. I did 4 different comparisons.
I have treatment A, B, C and D, and I compared groups: A-B, A-C, C-D and B-D. For all my data I created ONE design matrix, and FOUR contrast.diff.matrices. For the fit() function I have used the esetPROC with all my data. This was followed by contrast.fit() and eBayes() functions. At the end I got top differentially expressed genes (from topTableF() function).
Additionally, I did almost the same thing, but I created FOUR different design matrices and FOUR contrast.diff.matrices for all my comparisons. I extracted the subset of esetPROC only with the data I needed for the comparison, and continued as described above.
I got different results for those two approaches. The adj.p.values were much smaller for the first approach than for the second one. I assume it is because of the eBayes function. Could you please explain me which approach is the correct/better one and why?
-- output of sessionInfo():
R version 3.0.2 (2013-09-25)
Platform: x86_64-unknown-linux-gnu (64-bit)
attached base packages:
 parallel stats graphics grDevices utils datasets methods
other attached packages:
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 mgcv_1.7-26 nlme_3.1-111 corpcor_1.6.6
 vsn_3.30.0 marray_1.40.0 hgug4112a.db_2.10.1
 org.Hs.eg.db_2.10.1 Agi4x44PreProcess_1.22.0 genefilter_1.44.0
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 biomaRt_2.18.0 limma_3.18.12 WriteXLS_3.4.0
loaded via a namespace (and not attached):
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 survival_2.37-7 tools_3.0.2 xtable_1.7-1
 "Gordon Smyth <smyth at wehi.edu.au>"
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