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Generalized estimating equation modeling on correlated microbiome sequencing data with longitudinal measures

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  • Bo Chen
  • Wei Xu

Abstract

Existing models for assessing microbiome sequencing such as operational taxonomic units (OTUs) can only test predictors’ effects on OTUs. There is limited work on how to estimate the correlations between multiple OTUs and incorporate such relationship into models to evaluate longitudinal OTU measures. We propose a novel approach to estimate OTU correlations based on their taxonomic structure, and apply such correlation structure in Generalized Estimating Equations (GEE) models to estimate both predictors’ effects and OTU correlations. We develop a two-part Microbiome Taxonomic Longitudinal Correlation (MTLC) model for multivariate zero-inflated OTU outcomes based on the GEE framework. In addition, longitudinal and other types of repeated OTU measures are integrated in the MTLC model. Extensive simulations have been conducted to evaluate the performance of the MTLC method. Compared with the existing methods, the MTLC method shows robust and consistent estimation, and improved statistical power for testing predictors’ effects. Lastly we demonstrate our proposed method by implementing it into a real human microbiome study to evaluate the obesity on twins.Author summary: Human microbiome sequencing data analysis has been a fast growing area of genomic research in recent years. Although there have been several works for detecting predictors on a single operational taxonomic unit (OTU) or multiple OTUs simultaneously, there is limited work on how to estimate the correlations between multiple OTUs and incorporate such relationship into models to evaluate longitudinal OTU measures. Here we propose a novel approach to estimate OTU correlations based on their taxonomic structure after integrating longitudinal and other types of repeated OTU measures, and apply such correlation structure in Generalized Estimating Equations (GEE) models to estimate both predictors’ effects and OTU correlations. The method is theoretically sound and practically easy to implement, and we provide corroborating evidence from simulation and a real human microbiome study.

Suggested Citation

  • Bo Chen & Wei Xu, 2020. "Generalized estimating equation modeling on correlated microbiome sequencing data with longitudinal measures," PLOS Computational Biology, Public Library of Science, vol. 16(9), pages 1-22, September.
    • Handle: RePEc:plo:pcbi00:1008108
    DOI: 10.1371/journal.pcbi.1008108
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    References listed on IDEAS

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    1. Lizhen Xu & Andrew D Paterson & Williams Turpin & Wei Xu, 2015. "Assessment and Selection of Competing Models for Zero-Inflated Microbiome Data," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-30, July.
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