Teschendorff Andrew E, Horvath Steve
CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Altos Labs, Cambridge, UK.
Nat Rev Genet. 2025 May;26(5):350-368. doi: 10.1038/s41576-024-00807-w. Epub 2025 Jan 13.
Over the past decade, epigenetic clocks have emerged as powerful machine learning tools, not only to estimate chronological and biological age but also to assess the efficacy of anti-ageing, cellular rejuvenation and disease-preventive interventions. However, many computational and statistical challenges remain that limit our understanding, interpretation and application of epigenetic clocks. Here, we review these computational challenges, focusing on interpretation, cell-type heterogeneity and emerging single-cell methods, aiming to provide guidelines for the rigorous construction of interpretable epigenetic clocks at cell-type and single-cell resolution.
在过去十年中,表观遗传时钟已成为强大的机器学习工具,不仅可用于估计实足年龄和生物学年龄,还可用于评估抗衰老、细胞年轻化和疾病预防干预措施的效果。然而,仍然存在许多计算和统计方面的挑战,这些挑战限制了我们对表观遗传时钟的理解、解释和应用。在这里,我们回顾这些计算挑战,重点关注解释、细胞类型异质性和新兴的单细胞方法,旨在为在细胞类型和单细胞分辨率下严格构建可解释的表观遗传时钟提供指导。