we have a data set of knock-out vs. wild-type samples, some of them were stimulated, and some didn't. the experimental design looks like that:
name condition stimulation Vav_KO_1 KO no Vav_KO_2 KO no Vav_KO_2_C KO yes Vav_KO_4_C KO yes Vav_KO_5 KO no Vav_KO_5_C KO yes Vav_WT_1 wildtype no Vav_WT_1_C wildtype yes Vav_WT_2 wildtype no Vav_WT_2_C wildtype yes Vav_WT_4 wildtype no Vav_WT_4_C wildtype yes
We are interested in testing for two groups. The first one is for genes which are differentially regulated between the wildtype and the KO in genereal.
The second question is to look for genes that changed due to the stimulation between the two conditions (WT vs. KO).
my workflow and the used multi-factorial design is as followed:
dds <- DESeqDataSetFromMatrix(countData = countTable, colData = Phenotype, design = ~condition*stimulation) dds <- DESeq(dds) resultsNames(dds) resultsWT.KO <- results(dds, contrast=c("condition", "trippleKO", "wildtype")) resultsStimulation <- results(dds ,name = "conditionwildtype.stimulationnone")
For the first comparison I find over 7200 genes with an adjusted p-value below 0.1, but with the parameters I don't find any differentially regulated genes for the second problem.
I would like to know if construction of my design matrix is correct for this question.