Hi there,

Fairly new to this area so will try to explain as clearly as possible. I have two RNA-Seq data sets - one corresponds to a series of cancer cell lines, the other to cell lines we are using as a model of 'normal' epithelium. The expression units of one data set are in FPKM the other RPKM. I think I superficially understand the difference between these units, in that one is used in mapping transcripts in single end sequencing the other paired end sequencing. 

My question is are these units directly comparable? The analysis I wish to carry out is fairly straight forward - I have a predefined list of around 500 genes, and simply want to compare differences in expression between the non-cancer/cancer background, in terms of which of these genes they are expressing at all as well as the relative expression levels. For which transcripts are expressed I had intended to use any value over 0 (FPKM or RPKM) as denoting expression of a transcript, but am unsure if I can compare relative abundance.

I should add that I only have access to the raw data of one dataset, the other is as a results table sent by collaborators. 
In theory RPKM should just be twice FPKM, but that is unlikely to actually be the case, as is noted by the other commenters, differences in processing protocol, are likely to mean they are not comparable, and would not be even if they were both FPKM or both RPKM. 

You **might** be able to get some meaning from the dataset if you are just doing presence/absence calls, but I wouldn't use a 0 threshold. Instead convert your values to TPM (transcripts per million) but normalising each value to the sum of expression for that sample and multiplying by 1 million. E.g. if I had RPKMs from 4 genes of 0, 1, 5 and 4 the TPMs would be (0*x10^6)/10, (1*10^6)/10, (5*10^6)/10 and (4*10^6)/10. Transcripts Per Million is much easier to reason with and more comparable between samples. As a very approximate rule of thumb, a human cell has around 200,000 mRNAs in it at any one time, so a TPM of 5 corresponds to one molecule per cell and can serve as a handy rule of thumb for calling present/absent. 

Be careful though as differences in processing might lead to false positive differences - e.g. if one set was processed allowing multimapping and the other not, then you will be a present call for paralogs in one sample and an absent call in the other, irrespective of whether there is actually an expression difference. 