时程数据的主趋势分析及其在基因组医学中的应用
Principal trend analysis for time-course data with applications in genomic medicine
摘要：
基因组学和转化医学中出现了时间进程高通量基因表达数据。从患者队列中提取有趣的时间过程模式可以为进一步的临床研究和患者治疗提供生物学见解。我们提出了主趋势分析（PTA）提取的主要趋势的时间过程中的基因表达数据从一组患者，并确定基因，使主要趋势的主要贡献。通过仿真，我们证明了PTA的降维，时间过程中的信号恢复和高维数据的特征选择的效用。此外，PTA在真实的生物学和临床研究中获得了新的见解。我们证明了PTA的实用性，通过将其应用于昼夜节律调节系统和烧伤患者的纵向基因表达数据。这些应用表明，PTA可以提取具有生物学意义的有趣时程趋势，这有助于理解昼夜调节系统的生物学机制以及烧伤患者的康复。总体而言，所提出的PTA方法将有利于基因组医学研究。我们的方法被实现到一个R-包：PTA（主要趋势分析）。
英文摘要：
Time-course high-throughput gene expression data are emerging in genomic and translational medicine. Extracting interesting time-course patterns from a patient cohort can provide biological insights for further clinical research and patient treatment. We propose principal trend analysis (PTA) to extract principal trends of time-course gene expression data from a group of patients, and identify genes that make dominant contributions to the principal trends. Through simulations, we demonstrate the utility of PTA for dimension reduction, time-course signal recovery and feature selection with high-dimensional data. Moreover, PTA derives new insights in real biological and clinical research. We demonstrate the usefulness of PTA by applying it to longitudinal gene expression data of a circadian regulation system and burn patients. These applications show that PTA can extract interesting time-course trends with biological significance, which helps the understanding of biological mechanisms of circadian regulation systems as well as the recovery of burn patients. Overall, the proposed PTA approach will benefit the genomic medicine research. Our method is implemented into an R-package: PTA (Principal Trend Analysis).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
