Longitudinal gene expression profiles of subjects are collected in some clinical studies to monitor disease progression and understand disease etiology. The identification of gene sets that have coordinated changes with relevant clinical outcomes over time from these data could provide significant insights into the molecular basis of disease progression and lead to better treatments. In this article, we propose a Distance-Correlation based Gene Set Analysis (dcGSA) method for longitudinal gene expression data. dcGSA is a non-parametric approach, statistically robust, and can capture both linear and nonlinear relationships between gene sets and clinical outcomes. In addition, dcGSA is able to identify related gene sets in cases where the effects of gene sets on clinical outcomes differ across subjects due to the subject heterogeneity, remove the confounding effects of some unobserved time-invariant covariates, and allow the assessment of associations between gene sets and multiple related outcomes simultaneously. Through extensive simulation studies, we demonstrate that dcGSA is more powerful of detecting relevant genes than other commonly used gene set analysis methods. When dcGSA is applied to a real dataset on systemic lupus erythematosus, we are able to identify more disease related gene sets than other methods.

中文翻译：

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纵向研究中基于距离相关的基因集分析

在一些临床研究中收集受试者的纵向基因表达谱以监测疾病进展并了解疾病病因。从这些数据中识别出随着时间的推移与相关临床结果协调变化的基因组可以为疾病进展的分子基础提供重要的见解，并导致更好的治疗。在本文中，我们提出了一种用于纵向基因表达数据的基于距离相关的基因集分析 (dcGSA) 方法。dcGSA 是一种非参数方法，具有统计稳健性，并且可以捕获基因集和临床结果之间的线性和非线性关系。此外，由于受试者异质性，基因组对临床结果的影响因受试者而异，dcGSA 能够识别相关基因组，消除一些未观察到的时不变协变量的混杂效应，并允许同时评估基因集和多个相关结果之间的关联。通过广泛的模拟研究，我们证明 dcGSA 在检测相关基因方面比其他常用的基因集分析方法更强大。当 dcGSA 应用于系统性红斑狼疮的真实数据集时，我们能够识别出比其他方法更多的疾病相关基因集。