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Temporal probabilistic modeling of bacterial compositions derived from 16S rRNA sequencing

Tarmo Äijö, Christian L. Müller, Richard Bonneau
doi: https://doi.org/10.1101/076836
Tarmo Äijö
1Center for Computational Biology, Simons Foundation, New York, NY 10010, USA
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  • For correspondence: taijo@simonsfoundation.org rb113@nyu.edu
Christian L. Müller
1Center for Computational Biology, Simons Foundation, New York, NY 10010, USA
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Richard Bonneau
1Center for Computational Biology, Simons Foundation, New York, NY 10010, USA
2Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY 10003, USA
3Courant Institute of Mathematical Sciences, New York University, New York, NY 10003, USA
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  • For correspondence: taijo@simonsfoundation.org rb113@nyu.edu
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Abstract

The number of microbial and metagenomic studies has increased drastically due to advance-ments in next-generation sequencing-based measurement techniques. Statistical analysis and the validity of conclusions drawn from (time series) 16S rRNA and other metagenomic sequencing data is hampered by the presence of significant amount of noise and missing data (sampling zeros). Accounting uncertainty in microbiome data is often challenging due to the difficulty of obtaining biological replicates. Additionally, the compositional nature of current amplicon and metagenomic data differs from many other biological data types adding another challenge to the data analysis.

To address these challenges in human microbiome research, we introduce a novel probabilistic approach to explicitly model overdispersion and sampling zeros by considering the temporal correlation between nearby time points using Gaussian Processes. The proposed Temporal Gaussian Process Model for Compositional Data Analysis (TGP-CODA) shows superior modeling performance compared to commonly used Dirichlet-multinomial, multinomial, and non-parametric regression models on real and synthetic data. We demonstrate that the nonreplicative nature of human gut microbiota studies can be partially overcome by our method with proper experimental design of dense temporal sampling. We also show that different modeling ap-proaches have a strong impact on ecological interpretation of the data, such as stationarity, persistence, and environmental noise models.

A Stan implementation of the proposed method is available under MIT license at https://github.com/tare/GPMicrobiome.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted September 22, 2016.
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Temporal probabilistic modeling of bacterial compositions derived from 16S rRNA sequencing
Tarmo Äijö, Christian L. Müller, Richard Bonneau
bioRxiv 076836; doi: https://doi.org/10.1101/076836
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Temporal probabilistic modeling of bacterial compositions derived from 16S rRNA sequencing
Tarmo Äijö, Christian L. Müller, Richard Bonneau
bioRxiv 076836; doi: https://doi.org/10.1101/076836

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