Qi Xinhe, Zhang Menghan, Wei Tongqing, Lin Jinran, Zhao Xingming, Yao Yin, Hu Yueqing, Zheng Yan
State Key Laboratory of Genetics and Development of Complex Phenotypes, Ministry of Education Key Laboratory of Contemporary Anthropology, Human Phenome Institute, Center for Evolutionary Biology, Fudan University, Shanghai, China.
School of Life Sciences, Fudan University, Shanghai, China.
Microbiol Spectr. 2025 Aug 5;13(8):e0154225. doi: 10.1128/spectrum.01542-25. Epub 2025 Jul 11.
Longitudinal human microbial data offer insights into microbiome dynamics over time. Traditional methods usually overlook temporal relationships among samples from the same subject. Here, we presented the Longitudinal Microbial Data Distance (LorDist) method, which uses functional data fitting to construct a distance matrix integrating information from the same subject at different time points. Simulation data showed that LorDist handled well up to 60% sparseness and worked robustly with various sequencing depths and time points. Empirical data analysis demonstrated that LorDist excels in capturing differences across subjects with longitudinal microbiome data. LorDist presented the potential of longitudinal microbial data in addressing temporal autocorrelation and distinguishing phenotypes.IMPORTANCELongitudinal analysis of the human microbiome is critical for understanding its dynamic role in health and disease. However, current analytical approaches struggle to address key challenges, such as data sparsity and irregular sampling, inherent to time-series microbiome studies. Here, we developed longitudinal microbial data distance (LorDist), an innovative method leveraging functional data analysis to model temporal microbial dynamics with enhanced precision. Compared to existing methods, LorDist consistently outperforms in discerning biologically meaningful group differences, even in highly sparse data sets or under fluctuating sequencing depths. Our findings demonstrate LorDist's robust performance on real-world data sets involving inflammatory bowel disease and infant gut development. By explicitly preserving the temporal structure inherent in microbiome data, LorDist enables robust detection of subtle yet critical biological shifts, paving the way for improved diagnostics and personalized therapeutic strategies in microbiome science.
纵向人类微生物数据提供了随时间推移的微生物组动态变化的见解。传统方法通常会忽略来自同一受试者的样本之间的时间关系。在此，我们提出了纵向微生物数据距离（LorDist）方法，该方法使用功能数据拟合来构建一个整合来自同一受试者在不同时间点信息的距离矩阵。模拟数据表明，LorDist能够很好地处理高达60%的稀疏性，并且在各种测序深度和时间点下都能稳健运行。实证数据分析表明，LorDist在利用纵向微生物组数据捕捉不同受试者之间的差异方面表现出色。LorDist展现了纵向微生物数据在解决时间自相关和区分表型方面的潜力。
重要性
对人类微生物组进行纵向分析对于理解其在健康和疾病中的动态作用至关重要。然而，当前的分析方法难以应对时间序列微生物组研究中固有的关键挑战，如数据稀疏性和不规则采样。在此，我们开发了纵向微生物数据距离（LorDist），这是一种创新方法，利用功能数据分析以更高的精度对时间微生物动态进行建模。与现有方法相比，即使在高度稀疏的数据集或测序深度波动的情况下，LorDist在辨别具有生物学意义的组间差异方面始终表现更优。我们的研究结果证明了LorDist在涉及炎症性肠病和婴儿肠道发育的真实世界数据集上的稳健性能。通过明确保留微生物组数据中固有的时间结构，LorDist能够稳健地检测到细微但关键的生物学变化，为微生物组科学中改进诊断和个性化治疗策略铺平了道路。
