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Abstract:
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The human microbiome, which plays a key role in health and disease, consists of a dynamic community of microorganisms. There is a keen interest in understanding interactions among these microbes, and how these relations change over time. However, current methods for microbiome network inference exist only for a single time point. We propose a novel method to jointly estimate time-varying network associations for microbiome data, which encourages edge similarity across a neighborhood of time points. The new method accounts for the compositional constraint and zero-inflation that typify microbiome data sets, and includes the ability to analyze multi-site or multi-domain data. We demonstrate the performance through simulation studies and real data applications.
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