[BioC] Making three-way and four-way comparisons

[Ryan C. Thompson]

Are you looking for genes that have any difference at all between any of 
the three populations, or are you looking specifically for genes that 
are different in *both* the A vs B and A vs C comparisons? For the 
former case, you can pass multiple coefficients or contrasts to the 
testing functions (glmLRT or glmQLFTest) to perform an ANOVA-like test. 
For the latter, you should probably adapt the approach recommended by 
Grodon Smyth in a recent thread on this list[1]. Specifically, perform 
each test separately and take the intersection of significant genes.

-Ryan

[1] https://stat.ethz.ch/pipermail/bioconductor/2014-April/058822.html

On 04/10/2014 07:31 AM, Maria [guest] wrote:
> Hi all,
> I have read the entire edgeR user guide and some posts on here but I still do not understand if it possible to make non-pairwise contrasts in edgeR (or is it even an oxymoron)in the GLM mode. The only non-simple contrasts I could find are of the form (taken from the user guide):
> DrugvsPlacebo.2h = (Drug.2h-Drug.0h)-(Placebo.2h-Placebo.0h)
> but I have different setup.
>
> I have 3 different populations and I would like to see find genes that are significantly different between population A and B, and A and C simultaneously.
> Of course, I could make pairwise comparisons and look for intercept in DEG, but I am not sure it is the proper way?
>
> Regards,
> Maria
>
>   -- output of sessionInfo():
>
> R version 3.0.1 (2013-05-16)
> Platform: x86_64-w64-mingw32/x64 (64-bit)
>
> locale:
> [1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United States.1252
> [3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C
> [5] LC_TIME=English_United States.1252
>
> attached base packages:
> [1] splines   parallel  stats     graphics  grDevices utils     datasets  methods   base
>
> other attached packages:
>   [1] edgeR_3.2.4             limma_3.16.8            DESeq2_1.0.19           RcppArmadillo_0.4.200.0
>   [5] Rcpp_0.11.1             GenomicRanges_1.12.5    IRanges_1.18.4          pasilla_0.2.16
>   [9] DESeq_1.12.1            lattice_0.20-29         locfit_1.5-9.1          DEXSeq_1.6.0
> [13] Biobase_2.20.1          BiocGenerics_0.6.0      BiocInstaller_1.10.4
>
> loaded via a namespace (and not attached):
>   [1] annotate_1.38.0      AnnotationDbi_1.22.6 biomaRt_2.16.0       Biostrings_2.28.0    bitops_1.0-6
>   [6] DBI_0.2-7            genefilter_1.42.0    geneplotter_1.38.0   grid_3.0.1           hwriter_1.3
> [11] RColorBrewer_1.0-5   RCurl_1.95-4.1       Rsamtools_1.12.4     RSQLite_0.11.4       statmod_1.4.18
> [16] stats4_3.0.1         stringr_0.6.2        survival_2.37-7      tools_3.0.1          XML_3.98-1.1
> [21] xtable_1.7-3         zlibbioc_1.6.0
>
> --
> Sent via the guest posting facility at bioconductor.org.
>
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