Hi, I am working on a study where I have control vs drug exposed samples to compare. An important part of the study design is the presence of different time points (controls at one day vs drug exposure at one day, controls at4 days vs drug exposure at 4 days etc.) The metadata info looks something like this below.
time_days<-(factor(c(10,10,10,10,10,10,4,4,4,4,4,4,1,1,1,1,1,1))) coldata <- data.frame(dose, condition = factor(c( rep("control", 3), rep("low", 3), rep("control", 3), rep("low", 3), rep("control", 3), rep("low", 3))),time_days) coldata$Groups_of_Interest <- paste(coldata$condition,coldata$time_days,sep = "_") coldata
I want suggestion on should I keep all samples to be compared together or split them based on time of exposure. In other words, should there be only one DESeq2 object for all or multiple DESeq2 objects for samples based on time points. In general, I do understand that splitting the DESeq2 object may compromise the normalization step.
Is there any suggestions please to look into?