发表机构
Seoul National University（首尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多源数据整合中联合与个体成分分离精度差、计算量大的问题，提出基于得分子空间对齐的MSSAT检验方法，实现联合秩的准确估计，在模拟和TCGA数据上表现优于现有方法。
AI 中文摘要
在多源数据整合中，将共享（联合）结构与特定来源（个体）变异分离是一项基本任务。现有的联合-个体模型往往依赖于计算密集的优化或松散的谱界，导致分离精度次优且可扩展性差。本文提出了多源序贯对齐检验（MSSAT）。MSSAT利用了几何观测，即真正的联合成分在不同数据源间表现为紧密对齐的得分子空间。通过推导对齐统计量的渐近零分布，我们开发了一个严谨、无需重抽样的序贯检验程序，以准确估计联合秩。大量模拟和实际数据应用（包括TCGA多组学数据集）表明，与竞争方法相比，MSSAT实现了更优的分离精度和显著更快的计算速度。
英文摘要
Disentangling shared (joint) structures from source-specific (individual) variations is a fundamental task in multi-source data integration. Existing joint-individual models often rely on computationally intensive optimization or loose spectral bounds, leading to suboptimal separation accuracy and poor scalability. In this paper, we propose the Multi-Source Sequential Alignment Test (MSSAT). MSSAT leverages the geometric observation that true joint components manifest as closely aligned score subspaces across different data sources. By deriving the asymptotic null distribution of our alignment statistic, we develop a rigorous, resampling-free sequential testing procedure to accurately estimate the joint rank. Extensive simulations and real data applications, including a TCGA multi-omics dataset, demonstrate that MSSAT achieves superior separation accuracy and substantially faster computation compared to competing methods.
Comments31 pages, 6 figures; supplementary material: 19 pages, 1 figure
