Predictive Modeling of Microbiome Data with Interaction Effects

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Contributed Abstract 

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Mara Stadler (1), Christian L. Müller (1), Jacob Bien (2)

(1) Helmholtz Munich, N/A, (2) University of Southern California, N/A

Christian L. Müller  
Helmholtz Munich

Jacob Bien  
University of Southern California

Mara Stadler  
Helmholtz Munich

Mara Stadler  
Helmholtz Center Munich

In microbiome research, predicting an outcome of interest from microbial abundances via sparse regression models is a common task. However, models linear in the features might be too simple to capture dynamics in communities, as microbial species tend to interact with one another. To address this, we propose a framework that includes strategies for modeling interaction effects in presence-absence data of microbial species, absolute abundance data, and compositional microbial 16S rRNA sequencing data, where only relative abundance information is available. Our framework incorporates an extension of the constrained lasso for compositional data to interaction effects as well as the statistical concept of hierarchy to enhance the interpretability of interaction effects. Based on synthetic data, we demonstrate the conditions under which true effects can be statically detected, considering varying sparsity of features and varying noise levels. For a selection of real-world microbiome datasets, we show that robust interaction effects between microbial species can be detected and the predictive accuracy can be improved when modeling interaction effects compared to merely additive effects.

interaction modeling|microbial interactions|compositional data|sparsity|lasso|hierarchical interactions

Biometrics Section

Genomics, Metabolomics, Microbiome and NextGen Sequencing