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I'm trying to understand why RPKM is not an appropriate way to normalize for RNA seq (I understand the general idea, but I'd like to gain a deeper understanding). So I'm reading the original paper by Wagner et al 2012 that suggests TPM as an alternative. However, I'm struggling to understand the following paragraph, and was wondering if anyone can shed some light on what the authors are trying to say:

The reason for the inconsistency of RPKM across samples arises from the normalization by the total number of reads. While rmc as well as qPCR results are ratios of transcript concentrations, the RPKM normalizes a proxy for transcript number by $r_g \times 10^3 / fl_g$ the number of sequencing reads in millions, $R / 10^6$. The latter, however, is not a measure of total transcript number. The relationship between $R$ and the total number of transcripts sampled depends on the size distribution of RNA transcripts, which can differ between samples. In a sample with, on average, longer transcripts the same number of reads represents fewer transcripts.

One part that I'm particularly confused by is: "The RPKM normalizes a proxy for transcript number by $r_g \times 10^3/fl_g$ the number of sequencing reads in millions, $R/10^6$." What is the "proxy" here?

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That is why: https://www.biostars.org/p/9465851/#9465854.

All these naive per-million scaling methods may fail to correct for library composition differences.

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  • $\begingroup$ Thank you for the reply. So do you mind elaborating on this idea of "library composition differences"? I keep encountering this idea when I try to understand normalization but I just can't seem to understand it $\endgroup$ Jul 22 at 17:30
  • $\begingroup$ Nicely illustrated here: youtube.com/watch?v=UFB993xufUU $\endgroup$
    – ATpoint
    Jul 22 at 18:16
  • $\begingroup$ Will check it out thank you. Another question: Do you know what the authors mean by "The RPKM normalizes a proxy for transcript number by $r_g \times 10^3/fl_g$ the number of sequencing reads in millions, $R/10^6$." I understand that TPM is also not recommended to use, but I believe before I understand more complicated techniques I should build up my understanding using the simpler methods $\endgroup$ Jul 22 at 18:23

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