# Doubt about using TPM for statistics

We designed an experiment to explore the potential role of carbon dioxide on algae physiology using RNA-Seq. We analyse the differential gene expression using DESeq2 but now we are interested into analyse a few genes in detail.

My PI say to make the statistics over the ration response (expression_treatment/expression_control - 1) and my question is the following: Can I use TPM to calculate this ratio and make following statistics? I read in the documentation of DESeq2 that TPM we can't use this normalisation method to differential expression and my my other question is Why shouldn't?

My advisor ask to re-do the statistical analysis using the ratio response (rr) so i need the rr for each sample. I think this doesnt make sense cause the result will be or should be the same that using DESeq2.

Update: I'm using the fold-change calculated by DESeq2 and I'm not sure if I have transformed the data correctly. I took the two columns log2FoldChange and lfcSE (both are log-transformed variables) so I went back to the non-transformed variable as follow: FC=2^log2FoldChange and SE=lfcSE*2^log2FoldChange. I saw this approximation in this post. Is that correct? I have obtained a very broad range of error/

## 1 Answer

This is a common question, and the answer is while you can calculate this, it's not statistically robust. You're likely to arrive at false conclusions.

Why is this? Because while TPM is something you can calculate from a single sample, your question involves multiple replicates and those replicates vary between each other. Counts in units of TPM are missing information about the variation between samples. This extra information affects fold change estimates, and is why differential analysis of sequencing data is complicated.

See this answer to a related question, and this often-cited blog post for more information.