I went through the vignette about interaction terms and would like to understand if I am applying interaction terms correctly and biologically, if I am extracting the correct the terms. Here is the example code from DESeq2 R documentation for two conditions (A, B) and three genotypes (I,II,III). Let A and B be 2 conditions as: placebo and treatment respectively and in case of genotype: I is KO (where, gene X is knock out), II is WT and III is Tg (where, extra copy of gene X is present).
> dds <- makeExampleDESeqDataSet(n=100,m=18) > dds$genotype <- factor(rep(rep(c("I","II","III"),each=3),2)) > dds class: DESeqDataSet dim: 100 18 metadata(1): version assays(1): counts rownames(100): gene1 gene2 ... gene99 gene100 rowData names(3): trueIntercept trueBeta trueDisp colnames(18): sample1 sample2 ... sample17 sample18 colData names(2): condition genotype > dds$genotype <- factor(rep(rep(c("I","II","III"),each=3),2)) > design(dds) <- ~ genotype + condition + genotype:condition > dds <- DESeq(dds) estimating size factors estimating dispersions gene-wise dispersion estimates mean-dispersion relationship final dispersion estimates fitting model and testing > resultsNames(dds)  "Intercept" "genotype_II_vs_I" "genotype_III_vs_I"  "condition_B_vs_A" "genotypeII.conditionB" "genotypeIII.conditionB"
If we go through each and every terms under
genotype_II_vs_Iwill be the comparison between WT and KO across treatment and placebo,
genotype_II_vs_Iwill be the comparison between Tg and KO across treatment and placebo,
condition_B_vs_Awill be the comparison between treatment vs control for genotype I,
genotypeII.conditionBwill be the interaction effect of WT and treatment across genotype III vs genotype I.
genotypeIII.conditionBwill be the interaction effect of WT and treatment across genotype III vs genotype I.
I am interested to get the list of genes that are affected by both gene X and treatment ? How can I better model all the effects like treatment and genotype effect on Tg/WT vs KO ?