Re: DESeq: multi-factors testing questions

Hi Mike,

I would like to try DESeq2 package now.

My project has three factors: A with 16 levels, B with 2 levels and C with
3 levels, in total I have 16 x 3 x 2 = 96 groups, for each group, we have
8 biological replicates in our original experiment design, which ends up
with
96 x 8 =768 biosamples.

I can't understand why we can't estimate any interaction terms if there are
8 replicates per group, which was mentioned in your previous email.

Based on 8 biological replicates per group, I think we still have enough df
to test each interaction term.



Best,


Yanzhu





 Jan 15, 2014 at 11:12 AM, Michael Love <[email protected]> wrote:

>  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] <[email protected]>
> 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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>
>

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