I am analysing the dataset with two independent factors - genotype (WT, MUT) and age (pup, adult). I have 4 samples for each condition.
- mouse1 MUT pup
- mouse2 MUT adult
- mouse3 WT pup
- mouse4 WT adult etc.
What I want to check is how being the MUTANT affects gene expression across the age and if the impact of mutation is age dependent.
What I need is to build a model that shows me:
- how age affects the counts
- how genotype affects the counts
- and how the interaction age*genotype affects the counts - is it significant?
However after reading on forums I am confused and I am not sure which approach is best. Also I am not sure if I should use LRT and in which step.
My first idea is the model with interaction, as a 'baseline' I set Wild Type adult:
dds <- DESeqDataSetFromMatrix(countData=countData, colData=metaData, design= ~genotype+age+genotype:age, tidy = TRUE) dds dds$genotype dds$genotype = relevel( dds$genotype, "wt") dds$genotype dds$age dds$age = relevel( dds$age, "adult") dds$age keep <- rowSums(counts(dds)) >= 10 dds <- dds[keep,] dds <- estimateSizeFactors(dds) dds <- DESeq(dds) resultsNames(dds)
Or is it better to use 'group' design? What is more informative in my case?
dds$group <- factor(paste0(dds$genotype, dds$age)) design(dds) <- ~ group dds <- DESeq(dds) resultsNames(dds)
resultsNames(dds)  "Intercept" "group_mutpup_vs_mutadult" "group_wtadult_vs_mutadult"  "group_wtpup_vs_mutadult"
results(dds, contrast=c("group", "wtpup", "mutpup")) results(dds, contrast=c("group", "wtadult", "mutadult")) results(dds, contrast=c("group", "wtpup", "mutadult"))
In the group design how do I interpret the last contrast - is it the same as interaction from the previous model? I will be grateful for any advice, this is the first time I am trying multifactorial analysis in DESeq2.