TEMPTED: time-informed dimensionality reduction for longitudinal microbiome studies
Pixu Shi, Cameron Martino, Rungang Han, Stefan Janssen, Gregory A. Buck, Myrna G. Serrano, Kouros Owzar, Rob Knight, Liat Shenhav, Anru R. Zhang
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.