Hi,

We're working on a RNA-Seq data set, where we used spike In to control for transcriptional amplification. After running the analyses in the standard way and not getting too many significantly regulated genes (~70), we adjusted the normalization according to the spike Ins and got over 2000 genes with significant adj. p-value.

We first calculated the size factors for the Spike In subset and gave it to the DESeq object before running the analysis.

I have a question about the number of reads we are getting after the ercc normalization.

when calculating the sum of reads before and after the ercc normalization we are getting the following numbers

```
sample         before          after
ctrl1     24762847.36    33353900.56
ctrl2     24973604.51    30803080.63
ctrl3     24727427.27     22561350.5
treat1    24875474.53    18880174.62
treat2    24911409.33    23560508.28
treat3       24588778    21308227.81
```

as you can see there is a clear trend of less reads in the treated samples after taking the spike Ins into account and correcting for the transcriptional bias. This goes perfectly in accord with our expectations.

Our assumption is that the reason for that is that there are less polyA transcripts in the treated data set (an assumption which we would like to verify).

therefore I have two questions -

Are we correct in assuming that the changes in the number of reads is might to less polyA transcripts in the treated samples?

Secondly, is there a statistical way to calculate this diffenrence/change in numbers and give it some kind of a statistical value to it ,so that we can say how significant these results are?

thanks

Assa
Changes in polyA levels (whether that's due to transcription or turn-over is a different question (enjoy doing the pulse-chase experiment)) are certainly one possibility. There are, of course, others. If this is RNAseq from bulk tissue, then you have to worry about things like ensuring you had highly comparable amounts of starting tissue (in terms of weight, cell number, cell volume, etc.) and that the extraction efficiency is comparable between conditions. If these are single-cell experiments (you should probably have more replicates and have used/counted unique molecular identifiers!), you still have issues of cell size/shape. After all, talking about transcriptional amplification is less interesting if it's just because the cells are huge (or tiny, for the opposite).

Regarding a statistical test, you could just do a T-test between the summed normalized counts. That's less telling (and not significant, given the N of 3) than mentioning ~2000 DE genes, but it's a single number at least. I would suggest looking at the balance of the direction of change of the DE genes. If you really do have a difference due to transcriptional amplification, then the fold-changes should be almost entirely in a single direction.