Hello,
I want to use DESeq2 for DE analysis. I tried to perform a nested comparison. My data has two different factors: the mice are either 1. Uninfected and Infected or 2. Susceptible and Resistant strain. I would like to know how to set the design and contrasts to determine the cell-line specific effect, which consists of:
Resistant.Infected - Resistant.Uninfected vs Susceptible.Infected - Susceptible.Uninfected.
Just to clarify, I am interested in determining those genes which have different gene expression response to infection depending on the mouse strain.
I tried
colData = data.frame(rownames(dataSet$fst.cls), dataSet$sec.cls, dataSet$cls) view(colData) condition type condition_type 1 Infected Resistant Infected.Resistant 2 Infected Resistant Infected.Resistant 3 Infected Resistant Infected.Resistant 4 Uninfected Resistant Uninfected.Resistant 5 Uninfected Resistant Uninfected.Resistant 6 Uninfected Resistant Uninfected.Resistant 7 Infected Susceptible Infected.Susceptible 8 Infected Susceptible Infected.Susceptible 9 Infected Susceptible Infected.Susceptible 10 Uninfected Susceptible Uninfected.Susceptible 11 Uninfected Susceptible Uninfected.Susceptible 12 Uninfected Susceptible Uninfected.Susceptible colnames(colData) = c("condition", "type", "condition_type") dds = DESeqDataSetFromMatrix(countData=dataSet$data.anot, colData = colData, design = ~condition_type) dds = DESeq(dds) resultsNames(dds) [1] "Intercept" "condition_typeInfected.Resistant" [3] "condition_typeInfected.Susceptible" "condition_typeUninfected.Resistant" [5] "condition_typeUninfected.Susceptible" res = results(dds, contrasts = c(0,1,-1,-1,1))
But when I look into the res file, the only analysis it performed is
"condition_typeInfected.Resistant vs condition_typeUninfected.Susceptible"
I know that this way works but I want to know how to use a more flexible method for design and contrast:
dds = DESeqDataSetFromMatrix(countData=dataSet$data.anot, colData = colData, design = ~type+condition + condition:type)
Thank you in advance.