I have tumor age (nominal variable, divided into age brackets), gene expression (in RPKM) and sex (binary) for many tumors in people at different ages (not paired..ie. not time series of same tumors but different tumors at each age). I want to find the genes that are most differentially expressed with age, controlling for sex. What would be the best way to do this?

More specifically:

1. Do I need to transform the RPKM values to rank (as per [this thread][1])? The difference is that they use regularized regression.
2. Is a linear regression appropriate? [This paper][2] seems to suggest quantile regression is better (however there are other features of their algorithm, and their age is not ordinal) to linear regression which is used in much of the aging gene expression studies with microarray.
3. Because I am repurposing data and do not know batch would it be appropriate to run sva (or something similar) on it?

Thanks!

 [1]: https://www.biostars.org/p/105937/
 [2]: http://www.biomedcentral.com/1471-2164/10/S3/S16
If possible, get the raw data, RPKMs have a LOT of issues.

1. Depending on how complex you design becomes, it can become difficult to analyze rank transformed data.
2. Give linear regression a try first, there are a lot more tools for it. I'd only deal with quantile regression if the results were unsatisfactory.
3. Absolutely. You might also try just including the batches as a factor, though odds are good that SVA will work a bit better.