I am working with Deseq2 on RNA seq time-series data. I have RNA seq data at baseline before any intervention (timepoint0), RNA seq data with two different interventions (Placebo, and Condition) at TP1 and TP2. I went through the time-series tutorial for Deseq2 and don't think the same example applies to my dataset. Below is the sample info
# Phenotypes (minimal reprex) colData <- data.frame( "patient" = c(rep(c("sam_1", "sam_2", "sam_3", "sam_4", "sam_5", "sam_6"), 3)), "timepoint" = c(rep("tp0", 6), rep("tp1", 6), rep("tp2", 6)), "treatment" = c(rep("none", 6), rep(c("placebo","condition"), 6)) )
# Here is the deseq2 formula I was thinking to try but not sure if its correct ddsMat <- DESeqDataSetFromMatrix(countData = countData, colData = colData, design = ~ treatment + timepoint + treatment:timepoint) # Likelihood ratio test to get genes that have shown to be DE over time deseq_res <- DESeq(ddsMat, test="LRT", reduced = ~ treatment + timepoint)
I don't have any treatment at time point 0 so I am not sure if I can use the above formula. Could you please point me in the right direction, please?