hi Yanzhu,

Firstly, we recommend in general moving to DESeq2, where we spend most of
our development effort and which will be supported going foward.

The DESeq2 workflow is very similar to DESeq and is described in detail in
the vignette. The constructor function from a count matrix and column data
is DESeqDataSetFromMatrix(), see the manual page for the required
arguments. For likelihood ratio tests, you can use the following code:

dds <- DESeqDataSetFromMatrix(counts, columndata, design = ~ A)
dds <- DESeq(dds, test="LRT", reduced = ~ 1)
res <- results(dds)
res

In order to give more detailed recommendations though, can you please tell
us more about the experimental setup and what questions you are trying to
answer? For instance, how are the samples distributed across groups A, B
and C? I would suppose they are not 8 replicates per group, as this would
not allow you to estimate any interaction terms.

Mike


On Mon, Jan 13, 2014 at 2:15 PM, Yanzhu [guest] <guest@bioconductor.org>wrote:

>
> Dear Community,
>
> I have some questions about how the DESeq r package  works for
> multi-factors expersiment. My experiment has three factors: A/B/C, and 8
> replicates per condition. I would like the test the significance of the
> main effects of factor A, B and C, the significance of the two-way
> interaction terms: A:B, A:C and B:C, and the significance of the three-way
> interaction term: A:B:C. I want the table of pvalue for each term (main
> effects, two-way interaction terms and the three-way interaction term) like
> what ANOVA does for each gene.
>
>
> I know to test the significance of the three-way interaction term, we use
> the following coding:
> fitDeSeq1<-fitNbinomGLMs(cdsFull,count~A+B+C+A:B+A:C+B:C+A:B:C)
> fitDeSeq0<-fitNbinomGLMs(cdsFull,count~A+B+C+A:B+A:C+B:C)
> modDeSeq<-nbinomGLMTest(fitDeSeq1,fitDeSeq0)
>
> My Questions are: how can I test the significance of main effects and the
> two-way interaction terms?
>
> 1. To test the main effect of A, B and C
>
> (i) To test the main effect of A:
> fitDeSeq1<-fitNbinomGLMs(cdsFull,count~A)
> fitDeSeq0<-fitNbinomGLMs(cdsFull,count~1)
> modDeSeq<-nbinomGLMTest(fitDeSeq1,fitDeSeq0)
>
> (ii) To test the main effect of B:
> Do I need to use the following coding:
> fitDeSeq1<-fitNbinomGLMs(cdsFull,count~B)
> fitDeSeq0<-fitNbinomGLMs(cdsFull,count~1)
> modDeSeq<-nbinomGLMTest(fitDeSeq1,fitDeSeq0)
>
> OR:
>
> Do I need to use the following coding:
> fitDeSeq1<-fitNbinomGLMs(cdsFull,count~A+B)
> itDeSeq0<-fitNbinomGLMs(cdsFull,count~B)
> modDeSeq<-nbinomGLMTest(fitDeSeq1,fitDeSeq0)
>
> Which one is correct?
>
> (iii) To test the main effect of C:
> Do I need to use the following coding:
> fitDeSeq1<-fitNbinomGLMs(cdsFull,count~C)
> fitDeSeq0<-fitNbinomGLMs(cdsFull,count~1)
> modDeSeq<-nbinomGLMTest(fitDeSeq1,fitDeSeq0)
>
> OR:
>
> Do I need to use the following coding:
> fitDeSeq1<-fitNbinomGLMs(cdsFull,count~A+B+C)
> itDeSeq0<-fitNbinomGLMs(cdsFull,count~A+B)
> modDeSeq<-nbinomGLMTest(fitDeSeq1,fitDeSeq0)
>
> which one is correct?
>
> 2. To test the two-way interaction terms: A:B, A:C and B:C
>
> (i) To test the two-way interaction term: A:B
> Do I need to use the following coding:
> fitDeSeq1<-fitNbinomGLMs(cdsFull,count~A+B+C+A:B)
> fitDeSeq0<-fitNbinomGLMs(cdsFull,count~A+B+C)
> modDeSeq<-nbinomGLMTest(fitDeSeq1,fitDeSeq0)
>
> Is it correct?
>
> (ii) To test the two-way interaction term: A:C
> Do I need to use the following coding:
> fitDeSeq1<-fitNbinomGLMs(cdsFull,count~A+B+C+A:B+A:C)
> fitDeSeq0<-fitNbinomGLMs(cdsFull,count~A+B+C+A:B)
> modDeSeq<-nbinomGLMTest(fitDeSeq1,fitDeSeq0)
>
> OR:
>
> Do I need to use the following coding:
> fitDeSeq1<-fitNbinomGLMs(cdsFull,count~A+B+C+A:C)
> fitDeSeq0<-fitNbinomGLMs(cdsFull,count~A+B+C)
> modDeSeq<-nbinomGLMTest(fitDeSeq1,fitDeSeq0)
>
> Which one is correct?
>
> (iii) To test the two-way interaction term: B:C
> Do I need to use the following coding:
> fitDeSeq1<-fitNbinomGLMs(cdsFull,count~A+B+C+A:B+A:C+B:C)
> fitDeSeq0<-fitNbinomGLMs(cdsFull,count~A+B+C+A:B+A:C)
> modDeSeq<-nbinomGLMTest(fitDeSeq1,fitDeSeq0)
>
> OR:
>
> Do I need to use the following coding:
> fitDeSeq1<-fitNbinomGLMs(cdsFull,count~A+B+C+B:C)
> fitDeSeq0<-fitNbinomGLMs(cdsFull,count~A+B+C)
> modDeSeq<-nbinomGLMTest(fitDeSeq1,fitDeSeq0)
>
> Which one is correct?
>
> Thank you!
>
>
>
>
>
>
>  -- output of sessionInfo():
>
>  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    LC_MONETARY=English_United States.1252
> [4] LC_NUMERIC=C                           LC_TIME=English_United
> States.1252
>
> attached base packages:
> [1] parallel  stats     graphics  grDevices utils     datasets  methods
> base
>
> other attached packages:
> [1] DESeq_1.12.1       lattice_0.20-15    locfit_1.5-9.1
> Biobase_2.20.1     BiocGenerics_0.6.0 edgeR_3.2.4
> [7] limma_3.16.8
>
> loaded via a namespace (and not attached):
>  [1] annotate_1.38.0      AnnotationDbi_1.22.6 DBI_0.2-7
>  genefilter_1.42.0    geneplotter_1.38.0
>  [6] grid_3.0.1           IRanges_1.18.4       MASS_7.3-26
>  RColorBrewer_1.0-5   RSQLite_0.11.4
> [11] splines_3.0.1        stats4_3.0.1         survival_2.37-4
>  tools_3.0.1          XML_3.98-1.1
> [16] xtable_1.7-1
>
> --
> Sent via the guest posting facility at bioconductor.org.
>
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