[BioC] DESeq: Hypothesis testing in multifactor design
guest at bioconductor.org
Tue Jun 10 17:21:46 CEST 2014
I have a question about the hypothesis testing of the two-way interaction terms in a multifactor design which includes three factors: A, B and C.
When I tested the three-way interaction I used the full and reduced models as below for nbinomGLMTest():
Full: count ~ A+B+C+A:B+A:C+B:C+A:B:C
Reduced: count ~ A+B+C+A:B+A:C+B:C
Now comes my question, when I want to test the effect of two-way interaction terms, i.e., A:B, A:C or B:C, what should be my full and reduced models? For example, when I want to the test the effect of A:B, what should be my full and reduced models for nbinomGLMTest() using DESeq pacakge?
-- output of sessionInfo():
R version 3.1.0 (2014-04-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
 LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252 LC_MONETARY=English_United States.1252
 LC_NUMERIC=C LC_TIME=English_United States.1252
attached base packages:
 parallel stats graphics grDevices utils datasets methods base
other attached packages:
 DESeq_1.16.0 lattice_0.20-29 locfit_1.5-9.1 Biobase_2.24.0 BiocGenerics_0.10.0 edgeR_3.6.1 limma_3.20.1
loaded via a namespace (and not attached):
 annotate_1.42.0 AnnotationDbi_1.26.0 DBI_0.2-7 genefilter_1.46.0 geneplotter_1.42.0 GenomeInfoDb_1.0.2
 grid_3.1.0 IRanges_1.22.6 MASS_7.3-31 RColorBrewer_1.0-5 RSQLite_0.11.4 splines_3.1.0
 stats4_3.1.0 survival_2.37-7 tools_3.1.0 XML_3.98-1.1 xtable_1.7-3
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