Identifying individual mediators is a central goal of high-dimensional mediation analysis, yet pervasive dependence among mediators can invalidate standard debiased inference and lead to substantial false discovery rate (FDR) inflation. We propose a Factor-Adjusted Debiased Mediation Testing (FADMT) framework that enables large-scale inference for individual mediation effects with FDR control under complex dependence structures. Our approach posits an approximate factor structure on the unobserved errors of the mediator model, extracts common latent factors, and constructs decorrelated pseudo-mediators for the subsequent inferential procedure. We establish the asymptotic normality of the debiased estimator and develop a multiple testing procedure with theoretical FDR control under mild high-dimensional conditions. By adjusting for latent factor induced dependence, FADMT also improves robustness to spurious associations driven by shared latent variation in observational studies. Extensive simulations demonstrate the superior finite-sample performance across a wide range of correlation structures. Applications to TCGA-BRCA multi-omics data and to China's stock connect study further illustrate the practical utility of the proposed method.

翻译：识别个体中介变量是高维中介分析的核心目标，然而中介变量间普遍存在的依赖性可能使标准去偏推断失效，并导致错误发现率显著膨胀。我们提出了一个因子调整去偏中介检验框架，该框架能够在复杂依赖结构下实现对个体中介效应的大规模推断并控制错误发现率。我们的方法假设中介变量模型的未观测误差具有近似因子结构，提取公共潜在因子，并为后续推断过程构建去相关的伪中介变量。我们建立了去偏估计量的渐近正态性，并在温和的高维条件下开发了具有理论错误发现率控制的多重检验程序。通过调整潜在因子诱导的依赖性，该方法还提高了对观察性研究中由共享潜在变异驱动的伪关联的稳健性。大量模拟实验表明，该方法在多种相关结构下均具有优异的有限样本性能。在TCGA-BRCA多组学数据和中国股市互联互通研究中的应用进一步说明了所提方法的实用价值。