Hello everyone :)

I have a three treatments (each treatment has 3 samples) gene expression matrix and my advisor wants me to do time course analysis with this data, while 3 treatments represents control, acute and chronic (0, 24h, 72h) respectively. I put the raw counts into R software ImpluseDE2 and get the time course result. Meanwhile, I also used DESeq2 to rlog normalize the same raw count, then directly compared the numerical size of the rlogged values of the mean of 3 samples each treatment. I got different result. In order to get time tendency result, is it OK to use DESeq2-rlogged raw counts to compare the numeric value directly? While the rlog values represent gene expression level.

However, I don't think it an easy job. If so, why so many developers develop R softwares specialized in time course analysis? ImpulseDE2, Mfuzz, MasigPro, EBSeq-HMM...

If rlog values cannot directly reflect the change tendency of gene expression level, why?

Thank you for your patience!

Briefly, because one usually assumes that time course experiments follow certain patterns. Like gradual increases, or sinusoidal rhythmicity. In contrast, a pattern like:

...would probably be significant in something like a LRT test in DESeq2, but is no meaningful timecourse pattern. Specialized tools try to focus and identify meaningful patterns.

Thank you:)