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I strongly suggest that you take your normalized counts, get the averages of different groups, and compare those ratios to the different designs. That said, you might not be able to replicate the fold changes from ~ genotype + time well in Excel. What that design does is basically say "Lots of the variance within each genotype/time point is due to the ...


condition_IT_vs_control gives the effect of condition, conditionIT.time is the interaction of condition and time. If you wanted to test the effect of time, use name="time".


It looks good to me. One think I could suggest is to see the distribution of your data/model (with limma/voom). After you get the list of your genes with: top.table <- topTable(fit.eBayes, = "P", n = Inf) Maybe you can check your multidimensional scaling (MDS): plotMDS(your_data, col = as.numeric(group)) Then you can also check with ...

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