具有相关结构的超高维变量选择的鲁棒性方法：基于群体测试
Robust Ultra-High-Dimensional Variable Selection With Correlated Structure Using Group Testing
AI总结：
本文提出了一种基于群体测试的鲁棒超高维变量选择方法，通过数据驱动的组内聚类和稳健统计方法，在数据污染和非正态条件下均表现出色，适用于基因组生物标志物发现。
摘要：
背景：高维基因组数据表现出强烈的组内相关结构，这对传统特征选择方法构成了挑战，这些方法通常假设特征独立或依赖于预定义的通路，并且对异常值和模型设定错误敏感。 方法：我们提出了Dorfman筛查框架，这是一种多阶段程序，通过层次聚类形成数据驱动的变量组，进行组内和组内假设检验，并利用弹性网络或自适应弹性网络进行优化。鲁棒变量采用基于OGK的协方差估计、基于排名的相关性和Huber加权回归来处理受污染和非正态数据。 结果：在模拟中，Dorfman-Sparse-Adaptive-EN在正常条件下表现最佳，而Robust-OGK-Dorfman-Adaptive-EN在数据污染情况下表现出明显优势，优于传统Dorfman和竞争方法。应用于NSCLC基因表达数据以预测trametinib反应时，鲁棒Dorfman方法实现了最低的预测误差和临床相关基因的富集恢复。 结论：Dorfman框架提供了一种高效且稳健的基因组特征选择方法。Robust-OGK-Dorfman-Adaptive-EN在理想和受污染条件下均表现优异，能够扩展到超高维设置，使其成为现代基因组生物标志物发现的理想选择。
英文摘要：
Background: High-dimensional genomic data exhibit strong group correlation structures that challenge conventional feature selection methods, which often assume feature independence or rely on pre-defined pathways and are sensitive to outliers and model misspecification. Methods: We propose the Dorfman screening framework, a multi-stage procedure that forms data-driven variable groups via hierarchical clustering, performs group and within-group hypothesis testing, and refines selection using elastic net or adaptive elastic net. Robust variants incorporate OGK-based covariance estimation, rank-based correlation, and Huber-weighted regression to handle contaminated and non-normal data. Results: In simulations, Dorfman-Sparse-Adaptive-EN performed best under normal conditions, while Robust-OGK-Dorfman-Adaptive-EN showed clear advantages under data contamination, outperforming classical Dorfman and competing methods. Applied to NSCLC gene expression data for trametinib response, robust Dorfman methods achieved the lowest prediction errors and enriched recovery of clinically relevant genes. Conclusions: The Dorfman framework provides an efficient and robust approach to genomic feature selection. Robust-OGK-Dorfman-Adaptive-EN offers strong performance under both ideal and contaminated conditions and scales to ultra-high-dimensional settings, making it well suited for modern genomic biomarker discovery.
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