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I have cufflinks data from 18 different RNA-seq experiments.

I've noticed that if the sum all the RPKM values for a particular experiment, are vastly different between experiments. How could this be? I was under the impression that RPKM was normalized by the total number of reads. If that is true then the sum of RPKM values for a single experiment should be the same?

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I recommend that you ask this question in biostar. This is a valid question and neither biostar nor seqanswers have addressed this issue yet. – WYSIWYG Jun 17 '14 at 10:51
I think it might have something to do with reads that do not get aligned. unaligned reads might affect the normalization factor in RPKM, but since it is not aligned it dosen't contribute to the sum of RPKM values. – kamula Jun 19 '14 at 21:51
up vote 3 down vote accepted

I guess this arises because of the default cufflinks option --total-hits-norm in which it normalizes the FPKMs with total reads including the ones that are not mapped to a known gene or a predicted gene from the assembly. In the genes.fpkm_tracking file the FPKM values are reported for known/predicted genes. It is certainly possible that the number of genes are different in a certain sample compared to the other and perhaps therefore the sums are different. If you are not interested in finding new transcripts then you can run cufflinks with a reference GTF/GFF file provided using --GFF along with using --compatible-hits-norm.

You can also try this:

Assemble with cufflinks, merge the GTFs with cuffmerge and run cuffquant with the merged GTF.

You can subsequently use cuffnorm to normalize the expression values. It has been observed by me and others that the FPKM value reported by cuffdiff and cufflinks are different which is most likely because of differences in default parameters (see this). The inner operations of cufflinks are not very clearly documented; so I cannot conclusively answer your question.

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I fell like the --total-hits-norm must be it. but I cannot confirm either. thanks! – kamula Jun 19 '14 at 21:53

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