RMA Transformed Data: How to Determine Low/High Expression

Question

[menon_ankita]

I am working on a survival analysis using the RMA normalized/transformed data of some cancer patients.

Here is a quick overview on RMA: http://www.molmine.com/magma/loading/rma.htm

As mentioned in the link above, RMA is already log-2 transformed and quantile normalized(skewed), so when I tried to calculate z-scores using these RMA values, it was not working (I am not getting a mean of zero when I check the summary of the global z-scores, although I have re-calculated them multiple times). Hence, I did my survival analysis (Cox regression) with the RMA values itself.

But the problem I am now running into is that without calculating z-scores, I am not sure how to establish what constitutes of high/low expression using just the RMA data, which I need to do in order to create Kaplan-Meier curves and do further analysis. Do I establish this cutoff/range using the mean/median along with the hazard ratio, or is there some other more statistically accurate way?

Any help would be greatly appreciated, thank you!

Answer 1

[Kevin Blighe]

You should clarify what you mean by "not working".

Survival analysis does not have to be performed via Z-scores - Z-scores are just quite intuitive to understand. You can also simply divide your RMA expression range into tertiles, quartiles, quintiles, etc., and see how that fairs in the survival model.

As you implied, others also simply 'binarise' the expression range based on the median or mean.

Kevin

[menon_ankita]

Dr. Blighe - thank you for the response. I have clarified what I mean by "not working", sorry for the ambiguity. If I am dividing the RMA into expression ranges of tertiles, quartiles, etc., that is something I can do after the Cox regression and need only when I am plotting the survival graph, correct? Or would I have to redo everything I have done so far with the Cox regressions?

[Kevin Blighe]

You can user either approach. There are no strict rules regarding this.

[menon_ankita]

Ok, thank you for all the help!