StaRQR-K：受控于虚假发现率的区域分位数回归
StaRQR-K: False Discovery Rate Controlled Regional Quantile Regression
AI总结：
StaRQR-K通过控制虚假发现率实现区域分位数回归，用于检测基因组特征与结果分布特定分位数区域的关联。
摘要：
量化基因组特征如何影响结果分布的不同部分需要超越均值回归的统计工具，尤其是在超高维设置中。受对癌症中LINE-1活动研究的启发，我们提出StaRQR-K，一种带有模型-X knockoffs的稳定区域分位数回归框架，用于控制虚假发现率。StaRQR-K识别出甲基化水平与结果特定分位数区域相关的CpG位点，允许检测异质性和尾部敏感效应。该方法结合了高效的区域分位数sure independence筛选程序和基于winsorizing的模型-X knockoff过滤器，为区域分位数回归提供虚假发现率（FDR）控制。模拟研究显示，StaRQR-K在FDR控制和统计功效方面均优于现有方法。在对癌症基因组测图头颈癌队列的应用中，StaRQR-K揭示了CpG甲基化与LINE-1活动在分位数区域的特定关联，提高了样本外预测能力，并突显了具有已知功能相关性的基因组区域。
英文摘要：
Quantifying how genomic features influence different parts of an outcome distribution requires statistical tools that go beyond mean regression, especially in ultrahigh-dimensional settings. Motivated by the study of LINE-1 activity in cancer, we propose StaRQR-K, a stabilized regional quantile regression framework with model-X knockoffs for false discovery rate control. StaRQR-K identifies CpG sites whose methylation levels are associated with specific quantile regions of an outcome, allowing detection of heterogeneous and tail-sensitive effects. The method combines an efficient regional quantile sure independence screening procedure with a winsorizing-based model-X knockoff filter, providing false discovery rate (FDR) control for regional quantile regression. Simulation studies show that StaRQR-K achieves valid FDR control and substantially higher power than existing approaches. In an application to The Cancer Genome Atlas head and neck cancer cohort, StaRQR-K reveals quantile-region-specific associations between CpG methylation and LINE-1 activity that improve out-of-sample prediction and highlight genomic regions with known functional relevance.
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