[BioC] RNA-seq: interaction effect with DESeq

[galeb abu-ali]

Hi,

this is my first post on BiocR, I learned a lot by reading existing posts.
I am trying to use DESeq to infer differential gene expression in a
2-factor design of an RNA-seq expt.  Factor 1 (strain) and Factor 2
(condition) each have 4 levels; each level has 3 bioreps totaling to 48
RNA-seq samples.  I got good results from within-factor analysis, and am
now trying to identify an interaction effect of factor1 and factor2 on gene
expression.  This is the code i used:

> fit1 = fitNbinomGLMs( cdsFull, count ~ strain + condition + strain :
condition )
> fit0 = fitNbinomGLMs( cdsFull, count ~ strain + condition )
> pvalsGLM = nbinomGLMTest( fit1, fit0 )
> padjGLM = p.adjust( pvalsGLM, method='BH' )
> DEresults <- transform( fit1, pval=pvalsGLM, padj=padjGLM )
> head( DEresults[ order( DEresults$padj ), ] )

Attached is the output of head.
What I am asking help with is how to determine which of these interaction
comparisons are significant.  Do I need to compare each factor1 level with
each factor2 level, ie pairwise strain : condition comparisons, or is there
a way to extract this from the existing data?  Can this be done in DESeq or
should I be looking at another package?

Your insight is greatly appreciated.

thanks

galeb
-------------- next part --------------
	X.Intercept.	strain2	strain3	strain4	condition2	condition3	condition4	strain2.condition2	strain3.condition2	strain4.condition2	strain2.condition3	strain3.condition3	strain4.condition3	strain2.condition4	strain3.condition4	strain4.condition4	deviance	converged	pval	padj
gene_0477	9.3980690498	-1.6791314581	-7.002798797	-1.7106317853	0.1292043759	1.004851704	0.9593594984	0.4460683887	1.3333587254	0.5430856124	1.0467756694	4.3566446059	1.4022027955	1.0247376795	4.0497624618	1.5042551291	66.9551796174	TRUE	0	0
gene_0899	8.8283772691	1.3201257541	3.6966878444	2.0598916209	-0.2863640307	-3.1862934861	-3.6320668852	-0.433159713	0.1183208245	-0.3313504214	-1.3089286975	1.1287940418	-1.8929916603	-0.6393882394	1.6738458015	-1.0571110161	36.5611791438	TRUE	0	0
gene_3193	9.504666246	-0.1170027097	-4.1303583211	-0.6022719781	0.09498906	0.526477533	0.574614423	0.0826986394	0.1735329192	0.2855168364	0.2909434165	2.5168302886	0.6268837601	0.1252186408	2.4662137035	0.5265930419	20.2189071609	TRUE	0	0
gene_3190	9.7134519192	-1.2708138946	-4.6197170035	-1.4720599481	-0.2505335899	0.3404216725	0.5225965378	0.4759426442	0.0465311861	0.536341491	0.833730625	2.3164668939	0.8533719617	0.5648146485	2.0587035777	1.0514146972	32.5110294464	TRUE	2.19E-014	2.22E-011
gene_3192	8.3830050576	-0.6638001747	-4.1436143915	-0.952456834	0.0808601394	0.5629341824	0.4775418862	0.2657938573	-0.1631299939	0.3539415549	0.4834241261	2.2290320256	0.7099500394	0.4530668099	2.3089386737	0.7734138303	29.3662998474	TRUE	8.77E-014	7.13E-011
gene_0031	9.3983124954	0.4919132644	-0.1754119696	0.5397965786	0.3580852136	-2.7711159019	-3.7408103493	0.1876451807	-0.0149511116	-0.1168719638	-0.6959492519	1.3440430005	-0.6036571287	-0.159308005	1.8053857034	-0.4823270854	49.8376020899	TRUE	1.22E-013	8.30E-011