TEMPTED: time-informed dimensionality reduction for longitudinal microbiome studies

Longitudinal studies are crucial for understanding complex microbiome dynamics and their link to health. We introduce TEMPoral TEnsor Decomposition (TEMPTED), a time-informed dimensionality reduction method for high-dimensional longitudinal data that treats time as a continuous variable, effectively characterizing temporal information and handling varying temporal sampling. TEMPTED captures key microbial dynamics, facilitates beta-diversity analysis, and enhances reproducibility by transferring learned representations to new data. In simulations, it achieves 90% accuracy in phenotype classification, significantly outperforming existing methods. In real data, TEMPTED identifies vaginal microbial markers linked to term and preterm births, demonstrating robust performance across datasets and sequencing platforms.

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Publication Details

Journal
Genome biology
Published
2024-12-18
DOI
https://doi.org/10.1186/s13059-024-03453-x
Citations
12
Primary Topic
Gut microbiota and health
Type
article
Field-Weighted Citation Impact
2.81

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article

TEMPTED: time-informed dimensionality reduction for longitudinal microbiome studies

Anru R. Zhang, Kouros Owzar, Rob Knight, Rungang Han et al.
12 citations
Genome biology
Gut microbiota and health
2.81
article

TEMPTED: time-informed dimensionality reduction for longitudinal microbiome studies

Anru R. Zhang, Kouros Owzar, Rob Knight, Rungang Han, Myrna G. Serrano, Liat Shenhav, Stefan Janssen, Pixu Shi, Cameron Martino, Gregory A. Buck
article en
12 citations

Abstract

Longitudinal studies are crucial for understanding complex microbiome dynamics and their link to health. We introduce TEMPoral TEnsor Decomposition (TEMPTED), a time-informed dimensionality reduction method for high-dimensional longitudinal data that treats time as a continuous variable, effectively characterizing temporal information and handling varying temporal sampling. TEMPTED captures key microbial dynamics, facilitates beta-diversity analysis, and enhances reproducibility by transferring learned representations to new data. In simulations, it achieves 90% accuracy in phenotype classification, significantly outperforming existing methods. In real data, TEMPTED identifies vaginal microbial markers linked to term and preterm births, demonstrating robust performance across datasets and sequencing platforms.

Genome biologyVol. 25(1)
Duke University (US), Virginia Commonwealth University (US), Justus-Liebig-Universität Gießen (DE), University of California San Diego (US), Duke Medical Center (US), La Jolla Bioengineering Institute (US), New York University (US)
National Science Foundation, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, Division of Cancer Prevention, National Cancer Institute, Center for Strategic Scientific Initiatives, National Cancer Institute, Emerald Foundation, National Institutes of Health, Centers for Disease Control and Prevention, National Heart, Lung, and Blood Institute, National Cancer Institute, National Institute of Child Health and Human Development, Division of Mathematical Sciences, National Center for Complementary and Integrative Health, NHLBI Division of Intramural Research
Openalex Percentile: Top 9%
Gut microbiota and health
2.81
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