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.

中文翻译：

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TEMPTED：用于纵向微生物组研究的时间知情降维


纵向研究对于了解复杂的微生物组动力学及其与健康的联系至关重要。我们介绍了 TEMPoral TEnsor Decomposition （TEMPTED），这是一种用于高维纵向数据的时间知情降维方法，它将时间视为连续变量，有效地表征时间信息并处理不同的时间采样。TEMPTED 捕获关键的微生物动力学，促进 β 多样性分析，并通过将学习的表征转移到新数据来提高可重复性。在模拟中，它在表型分类中实现了 90% 的准确率，明显优于现有方法。在真实数据中，TEMPTED 识别了与足月和早产相关的阴道微生物标志物，在数据集和测序平台上表现出稳健的性能。