Hi,

I have the following matrix :

I need to compare : del(env1 versus env2) versus wt(env1 versus env2)

>design(dds) <- ~0+Status:Env

>resultsNames(dds)

[1] "Envenv1.Statusdel" "Envenv2.Statusdel"  "Envenv1.Statuswt" "Envenv2.Statuswt"

>res <- results(dds,contrast=list(c("Envenv1.Statusdel","Envenv2.Statusdel"),c("Envenv1.Statuswt","Envenv2.Statuswt")))

>res

log2 fold change (MLE): Envenv1.Statusdel+Envenv2.Statusdel vs Envenv1.Statuswt+Envenv2.Statuswt

Wald test p-value: Envenv1.Statusdel+Envenv2.Statusdel vs Envenv1.Statuswt+Envenv2.Statuswt

DataFrame with 21272 rows and 6 columns

Please, how to make contrast model to obtain this comparaison : del(env1 versus env2) versus wt(env1 versus env2) ?

Is it possible ?

Thanks for your help and advices,

Sam

"I need to compare : del(env1 versus env2) versus wt(env1 versus env2)"

This can be accomplished with a single interaction term. See this section:

https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#interactions

You actually want to add an interaction to the design, so skip the first few paragraphs suggesting to use ~ group.

You can use a design of ~Status + Env + Status:Env, and then either use a Wald test or LRT to test the interaction term. An LRT would look like:

dds <- DESeq(dds, test="LRT", reduced=~Status + Env)
res <- results(dds)