Shi Pixu, Martino Cameron, Han Rungang, Janssen Stefan, Buck Gregory, Serrano Myrna, Owzar Kouros, Knight Rob, Shenhav Liat, Zhang Anru R
Department of Biostatistics & Bioinformatics, Duke University, Durham, NC, USA.
Duke Microbiome Center, Duke University, Durham, NC, USA.
Genome Biol. 2024 Dec 19;25(1):317. doi: 10.1186/s13059-024-03453-x.
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.
纵向研究对于理解复杂的微生物组动态及其与健康的联系至关重要。我们引入了时间张量分解（TEMPoral TEnsor Decomposition，TEMPTED），这是一种用于高维纵向数据的考虑时间因素的降维方法，它将时间视为连续变量，有效地刻画时间信息并处理不同的时间采样。TEMPTED能够捕捉关键的微生物动态，促进β多样性分析，并通过将学习到的表示转移到新数据来提高可重复性。在模拟中，它在表型分类中达到了90%的准确率，显著优于现有方法。在实际数据中，TEMPTED识别出了与足月和早产相关的阴道微生物标志物，在不同数据集和测序平台上都表现出强大的性能。
