Detection of Differential Abundance Intervals in Longitudinal Metagenomic Data Using Negative Binomial Smoothing Spline ANOVA

Metagenomic longitudinal studies have become a widely-used study design to investigate the dynamics of the microbial ecological systems and their temporal effects. One of the important questions to be addressed in longitudinal studies is the identification of time intervals when microbial features show changes in their abundance. We propose a statistical method that is based on a semi-parametric Smoothing Spline ANOVA and negative binomial distribution to model the time-course of the features between two phenotypes. We demonstrate the superior performance of our proposed method compared to the two currently existing methods using simulated data. We present the analysis results of our proposed method in an analysis of a longitudinal dataset that investigates the association between the development of type 1 diabetes in infants and the gut microbiome. The identified significant species and their specific time intervals reveal new information that can be used in improving intervention or treatment plans.