**0**wrote:

Hello,

i am using DESeq2 to analyse an experiment with two conditions and three groups, as described in "Example 3" in the ?results help page.

I started the analysis by testing for an interaction effect with the following code:

## Example 3: two conditions, three groups, with interaction terms

dds <- makeExampleDESeqDataSet(n=100,m=18) dds$group <- factor(rep(rep(c("X","Y","Z"),each=3),2)) design(dds) <- ~ group + condition + group:condition dds <- DESeq(dds) resultsNames(dds) ## Interaction effect with LRT test group.x.cond <- nbinomLRT(dds,reduced=~group + condition) group.x.cond.res <- results(group.x.cond) tab.group.x.cond <- table(group.x.cond.res$padj < 0.05) tab.group.x.cond

In much the same way as here in the example data, i did not found any gene showing a significant interaction effect in my real data set.

Consequently, i set-up a new full model without interaction term:

## New model without interaction term: design(dds) <- ~ group + condition dds <- DESeq(dds) resultsNames(dds)

I have now three questions:

1. Is there a way to test for a condition effect in a specific group (say Z) based on the new model, as it is described in the ?results help for a model with interaction term?

2. In my own data set, if i set-up a full model with interaction term, and test for treatment effects in specific groups, the number of genes showing a significant treatment effect is much lower compared to the number of genes showing a treatment main effect? I am grateful for any comments on possible reasons for this observation.

3. Is there a way to test for a main effect for group based based on contrasts, i.e. not with a LRT test, but with a WALD test?

Many thanks for any comments!