Sequence count data are poorly fit by the negative binomial distribution

Stijn Hawinkel, J C W Rayner, Luc Bijnens and Olivier Thas

PLOS ONE, 2020, vol. 15, issue 4, 1-16

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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0224909

DOI: 10.1371/journal.pone.0224909

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