无需标签的基因通路分析新方法，能发现更丰富的生物通路关联。
LaCoGSEA: Unsupervised deep learning for pathway analysis via latent correlation
- 用自编码器捕捉非线性基因表达结构，通过潜在变量相关性排序基因。
- 在癌症亚型区分上优于现有无监督方法，高排名通路更丰富且生物意义强。
- 适用于不同实验条件和数据规模，结果稳定可靠，适合生物通路探索研究。
路径富集分析广泛用于解读基因表达数据。传统方法如GSEA依赖预定义表型标签和成对比较，限制了其在无监督场景的应用。现有无监督扩展，包括单样本方法，仅提供通路层面摘要，主要捕捉线性关系，未显式建模基因-通路关联。近年来深度学习被用于捕捉非线性转录组结构，但其解释通常依赖通用可解释AI（XAI）技术，仅支持特征级归因，不适用于无监督通路级解释。为此，我们提出LaCoGSEA（潜在相关性GSEA），一种将深度表征学习与稳健通路统计相结合的无监督框架。该方法使用自编码器捕捉非线性流形，并提出全局基因-潜在变量相关性作为差异表达代理，生成无需先验标签的密集基因排序。结果显示：(i) LaCoGSEA在区分癌症亚型方面优于现有无监督基线；(ii) 比线性降维和基于梯度的XAI方法在更高排名恢复更广泛的生物学有意义通路；(iii) 在不同实验协议和数据规模下保持高鲁棒性和一致性。总体而言，LaCoGSEA在无监督通路富集分析中达到当前最优性能。代码与实现：https://github.com/willyzzz/LaCoGSEA
原文摘要 · Abstract (English)
Motivation: Pathway enrichment analysis is widely used to interpret gene expression data. Standard approaches, such as GSEA, rely on predefined phenotypic labels and pairwise comparisons, which limits their applicability in unsupervised settings. Existing unsupervised extensions, including single-sample methods, provide pathway-level summaries but primarily capture linear relationships and do not explicitly model gene-pathway associations. More recently, deep learning models have been explored to capture non-linear transcriptomic structure. However, their interpretation has typically relied on generic explainable AI (XAI) techniques designed for feature-level attribution. As these methods are not designed for pathway-level interpretation in unsupervised transcriptomic analyses, their effectiveness in this setting remains limited. Results: To bridge this gap, we introduce LaCoGSEA (Latent Correlation GSEA), an unsupervised framework that integrates deep representation learning with robust pathway statistics. LaCoGSEA employs an autoencoder to capture non-linear manifolds and proposes a global gene-latent correlation metric as a proxy for differential expression, generating dense gene rankings without prior labels. We demonstrate that LaCoGSEA offers three key advantages: (i) it achieves improved clustering performance in distinguishing cancer subtypes compared to existing unsupervised baselines; (ii) it recovers a broader range of biologically meaningful pathways at higher ranks compared with linear dimensionality reduction and gradient-based XAI methods; and (iii) it maintains high robustness and consistency across varying experimental protocols and dataset sizes. Overall, LaCoGSEA provides state-of-the-art performance in unsupervised pathway enrichment analysis. Availability and implementation: https://github.com/willyzzz/LaCoGSEA
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