That's an interesting paper. Here is a link to the full text: https://springerlink3.metapress.com/content/18284k158v887k57/fulltext.html
Hi everyone
I understand the RPKM fomula is as follows:
C = Number of reads mapped to a gene
N = Total mapped reads in the experiment
L = exon length in base-pairs for a gene
Equation = RPKM = (10^9 * C)/(N * L)
I have the counts ( from HTSeq) and transcript length (retrieved from Ensembl API) for each gene.
My question is, for the total mapped reads(N), should I be counting the reads only in the exons for all genes? If thats right, can I just add all the gene counts from HTSeq output to get the total mapped reads.
OR
the total mapped reads will be ALL the mapped reads in the BAM file?
2 answers
Nice blog post on this Damian: RPKM measure is inconsistent among samples. Of course this begs the question, just how many of the papers using RPKM are not quite correct?
First of all, it does not matter if you are going to perform differential gene expression analysis or something like that.
Note that N is common in all genes, so either of the options will only scale your numbers.
If you are focusing only on the expression level of the genes, perhaps a good way is to set N as the number of reads mapped to the exons, to have the sum of C for all genes equal to N.
If you are interested in miRNAs, ncRNAs and so on, you may keep N as all of the mapped reads.
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Can somebody confirm that this is the same equation as http://www.rna-seqblog.com/rpkm-fpkm-and-tpm-clearly-explained/? Why is 10^9 different that 1e6? CORRECTION: nevermind, I didn't see the 1e3 incorporated in one of the other eqns