用于泛组学泛癌研究中快速可靠模块发现的组正则化矩阵分解
Group-regularized matrix factorization for fast and reliable module discovery in pan-omics pan-cancer studies
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中文总结 AI 辅助
针对泛组学泛癌研究的模块发现问题，提出GL-BIDIFAC+组正则化矩阵分解框架，实现快速可靠的模块识别，在模拟与TCGA数据应用中表现良好。
中文摘要 AI 辅助
在泛组学泛癌研究中，识别特定子集间共有的潜在变异源至关重要。该任务常需将双向关联的数据矩阵分解为块稀疏低秩模块之和。现有方法常依赖预先指定的模块数量、秩或事后阈值处理，当潜在共享结构复杂时，对模型设定较为敏感。为解决这些问题，我们提出GL-BIDIFAC+，一种用于发现部分共享模块的组正则化矩阵分解框架。它仅需潜在维度的上界，通过具有理论依据的调参选择和局部支持恢复分析的组正则化来促进模块选择，为模块发现提供可扩展性和原则性指导。它还支持概率解释，可实现基于模型的缺失数据插补。模拟研究表明，与现有方法相比，其模块恢复准确且计算性能良好。我们进一步将GL-BIDIFAC+应用于分析癌症基因组图谱（TCGA）数据，其中成熟的分子结构提供了可解释的生物学参考。我们的分析区分了广泛的泛癌变异、癌症特异性亚型结构，以及具有相关组织起源或组织学特征的癌症间共享的变异。
英文摘要
In pan-omics pan-cancer studies, it is critical to identify latent sources of variation that are shared across particular subsets. This task often requires bidimensionally linked data matrices to be decomposed into a sum of block-sparse, low-rank modules. Existing approaches often rely on pre-specified module numbers, ranks, or post-hoc thresholding and can be sensitive to model specification when the underlying sharing structure is complex. To address these issues, we propose GL-BIDIFAC+, a group-regularized matrix factorization framework for discovering partially shared modules. It requires only an upper bound on the latent dimension and encourages module selection through group regularization with theoretically-motivated tuning parameter selection and local support recovery analysis, providing both scalability and principled guidance for module discovery. It also admits a probabilistic interpretation that enables model-based imputation of missing data. Simulation studies demonstrate accurate module recovery and favorable computational performance relative to existing approaches. We further apply GL-BIDIFAC+ to analyze the Cancer Genome Atlas data, where well-established molecular structure provides interpretable biological references. Our analysis distinguishes broad pan-cancer variation, cancer-specific subtype structure, and variation shared across cancers with related tissue origins or histologic features.
