An update (6th October 2018):
You should abandon RPKM / FPKM. They are not ideal where cross-sample differential expression analysis is your aim; indeed, they render samples incomparable via differential expression analysis:
Please read this: A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis
The Total Count and RPKM [FPKM] normalization methods, both of which are still widely in use, are ineffective and should be definitively abandoned in the context of differential analysis.
Also, by Harold Pimental: What the FPKM? A review of RNA-Seq expression units
The first thing one should remember is that without between sample normalization (a topic for a later post), NONE of these units are comparable across experiments. This is a result of RNA-Seq being a relative measurement, not an absolute one.
Here we go again:
You should know that the old 'Tuxedo' pipeline of Tophat(2) and Cufflinks is no longer the "advisable" tool for RNA-seq analysis. The software is deprecated/ in low maintenance and should be replaced by HISAT2, StringTie and ballgown. See this paper: Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. (If you can't get access to that publication, let me know and I'll -cough- help you.) There are also other alternatives, including alignment with STAR and bbmap, or pseudo-alignment using salmon.