Human viral challenge studies, in which participants are deliberately inoculated with influenza strains such as H1N1 or H3N2 and monitored through longitudinal transcriptomic profiling before and after inoculation, are critical for characterizing dynamic biological immune responses to viral infection. A key analytical goal in such settings is to detect critical transition times, or change points, at which an underlying trajectory shifts direction or rate, indicating events such as the onset of an immune response or recovery. However, change-point detection in these longitudinal data is fundamentally challenging because observations are often sparse and irregularly spaced, sample sizes are small, outliers are common, and the number of change points is unknown in advance. To address these challenges, we propose LoopPerm-CPD, a robust change-point detection approach with a built-in loop permutation procedure for automatic multiple change-point detection. The method evaluates candidate slope change points and assesses their significance using within-subject circular permutation combined with binary segmentation, jointly estimating both the number and locations of change points. The accompanying R package, LoopPerm-CPD, implements this framework and flexibly accommodates generalized least squares, quantile regression, and quantile rank-score statistics for different types of longitudinal outcomes. The proposed approach is evaluated through simulations, demonstrating Type I error control and improved power compared with competing methods. Applied to real data, the framework identifies interpretable transition points in multiple human respiratory viral inoculation studies. Together, these results establish LoopPerm-CPD and its companion software as a robust and user-friendly tool for change-point detection in complex human longitudinal cohort data.

翻译：摘要: 人体病毒挑战试验中，参与者被故意接种H1N1或H3N2等流感病毒株，并通过纵向转录组分析在接种前后进行监测，对于描述病毒感染的动态生物学免疫反应至关重要。在此类研究中的关键分析目标是检测关键转变时间点（即变点），在这些点上潜在轨迹的方向或速度发生变化，标志着免疫反应启动或恢复等事件。然而，由于这些纵向数据中的观测值通常稀疏且间隔不规则、样本量较小、异常值普遍存在且变点数量预先未知，因此变点检测面临根本性挑战。为解决这些问题，我们提出LoopPerm-CPD，这是一种鲁棒的变点检测方法，内置循环排列程序用于自动多重变点检测。该方法评估候选斜率变点，并通过结合二元分割的受试者内部循环排列评估其显著性，同时估计变点数量及其位置。配套的R语言软件包LoopPerm-CPD实现了该框架，并灵活地采用广义最小二乘法、分位数回归和分位数秩次统计量处理不同类型的纵向结果。通过模拟评估所提方法，结果表明相较于竞争方法，其控制了第一类错误并提高了统计功效。应用于真实数据时，该框架在多项人类呼吸道病毒接种研究中识别出可解释的转变点。综合上述结果，LoopPerm-CPD及其配套软件被确立为一种鲁棒且用户友好的工具，适用于复杂人类纵向队列数据的变点检测。