Question: RNA-seq: Time-course data normalisation with TPM
0
gravatar for heikki.sarin
19 months ago by
heikki.sarin0 wrote:

I have RNA-seq time-course data (3 TP) with control/case design I'd like to do TPM normalization for the dataset but not quite sure what's the best way to apply it. Would it be most wise to 1) TPM normalize each time point individually and after that 2) normalize the whole dataset in one step? If so, how one can in practise do this - as TPM normalizes per sample? How to use TPM to normalize a certain group?

Aiming to do this as we've used DESeq2 for the analysis but what want apply GEE statistics for this and see how the results compare. Manual data normalization is needed for the use of GEE.

Really appreciate the input if someone could give an answer to this.

ADD COMMENTlink modified 19 months ago by Michael Love25k • written 19 months ago by heikki.sarin0
Answer: RNA-seq: Time-course data normalisation with TPM
2
gravatar for Michael Love
19 months ago by
Michael Love25k
United States
Michael Love25k wrote:

You can use a transcript quantification method to generate TPM expression estimates, e.g. I have worked on the Salmon method that handles a number of technical biases while estimating TPM. You can import these into R with the tximport package and you will find the TPM values in txi$abundance.

ADD COMMENTlink written 19 months ago by Michael Love25k

Is there a way to implement this on a data frame with raw read counts (e.g. similar needed to DESeq2)? Or is it mandatory to go back on the original files?

Not really that familiar with Salmon and how to use it (do you know there's a vignette manual but it's always a time consuming path to learn new methods).

ADD REPLYlink written 19 months ago by heikki.sarin0

I guess there’s a lot of details here but, no you can’t get abundance estimates from counts alone. I’m just trying to answer the main question which is that you want to model TPMs directly. The point of DESeq2 is to model counts directly.

ADD REPLYlink written 19 months ago by Michael Love25k
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