I have some phosphoproteomics, time-series data that I would like to analyze. I have four conditions (Control, MutA, MutB, MutA+B) and my aim is to i) Identify which phosphosites are behaving differently across time in the double mutatant (MutA+B) when compare to MutA and MutB. Each condition has been measured across 3 time points (1hr, 8hrs, 24hrs), including the control condition. Each timepoint & condition have 4 replicates.
I was reading the limma user's guide but I am unsure about the following:
- is the approach using splines (9.6.2) possible given that I only have 3 time points?
- how to select the correct number of dfs?
- Should I use
duplicateCorrelationsince the same replicates were measured throught time?
- Is the following design matrix correct in order to answer my question?
X <- ns(as.numeric(data$time, df =3) Group <- factor(data$Treatment) design <- model.matrix(~Group*X) fit <- lmFit(dat.mat, design) fit <- eBayes(fit) Limma.results = topTable(fit,coef = 1,number = Inf) candidate_genes <- Limma.results %>% filter(adj.P.Val < 0.01)
Thank you in advance!