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Sequence count data are poorly fit by the negative binomial distribution

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Listed:
  • Stijn Hawinkel
  • J C W Rayner
  • Luc Bijnens
  • Olivier Thas

Abstract

Sequence count data are commonly modelled using the negative binomial (NB) distribution. Several empirical studies, however, have demonstrated that methods based on the NB-assumption do not always succeed in controlling the false discovery rate (FDR) at its nominal level. In this paper, we propose a dedicated statistical goodness of fit test for the NB distribution in regression models and demonstrate that the NB-assumption is violated in many publicly available RNA-Seq and 16S rRNA microbiome datasets. The zero-inflated NB distribution was not found to give a substantially better fit. We also show that the NB-based tests perform worse on the features for which the NB-assumption was violated than on the features for which no significant deviation was detected. This gives an explanation for the poor behaviour of NB-based tests in many published evaluation studies. We conclude that nonparametric tests should be preferred over parametric methods.

Suggested Citation

  • Stijn Hawinkel & J C W Rayner & Luc Bijnens & Olivier Thas, 2020. "Sequence count data are poorly fit by the negative binomial distribution," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-16, April.
    • Handle: RePEc:plo:pone00:0224909
    DOI: 10.1371/journal.pone.0224909
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    References listed on IDEAS

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    1. Paul J McMurdie & Susan Holmes, 2014. "Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible," PLOS Computational Biology, Public Library of Science, vol. 10(4), pages 1-12, April.
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