Application of zero-inflated negative binomial mixed model to human microbiota sequence data

15 Identification of the majority of organisms present in human-associated microbial 16 communities is feasible with the advent of high throughput sequencing technology. 17 However, these data consist of non-negative, highly skewed sequence counts with a large 18 proportion of zeros. Zero-inflated models are useful for analyzing such data. Moreover, 19 the non-zero observations may be over-dispersed in relation to the Poisson distribution, 20 biasing parameter estimates and underestimating standard errors. In such a circumstance, 21 a zero-inflated negative binomial (ZINB) model better accounts for these characteristics 22 compared to a zero-inflated Poisson (ZIP). In addition, complex study designs are 23 possible with repeated measurements or multiple samples collected from the same 24 subject, thus random effects are introduced to account for the within subject variation. A 25 zero-inflated negative binomial mixed model contains components to model the 26 probability of excess zero values and the negative binomial parameters, allowing for 27 repeated measures using independent random effects between these two components. The 28 objective of this study is to examine the application of a zero-inflated negative binomial 29 mixed model to human microbiota sequence data. 30