对比6种降维方法，为空间转录组分析选型提供科学依据。
Benchmarking Dimensionality Reduction Techniques for Spatial Transcriptomics
- 构建统一评估框架，系统测试六种降维方法在不同参数下的表现。
- 发现NMF在标记基因富集上最优，VAE在重建与可解释性间平衡最佳。
- 提出新指标MER指导细胞聚类重分配，平均提升12%的生物一致性。
我们提出一种统一框架，用于评估空间转录组中超越传统PCA的降维技术。在胆管癌Xenium数据集上，系统比较了六种方法：PCA、NMF、自编码器、变分自编码器（VAE）及两种混合嵌入，调节潜在维度（k=5-40）和聚类分辨率（ρ=0.1-1.2）。每组配置通过重建误差、解释方差、簇凝聚度，以及两项新的生物学导向指标——簇标记一致性（CMC）和标记排除率（MER）进行评估。结果表明：PCA为快速基线；NMF在标记基因富集上表现最佳；VAE在重建与可解释性之间取得良好平衡；自编码器居于中间。通过帕累托最优分析实现系统性超参数选择，并证明基于MER的重新分配可显著提升所有方法的生物保真度，平均使CMC提升12%。该框架支持针对具体空间转录组分析任务的降维方法合理选择。
原文摘要 · Abstract (English)
We introduce a unified framework for evaluating dimensionality reduction techniques in spatial transcriptomics beyond standard PCA approaches. We benchmark six methods PCA, NMF, autoencoder, VAE, and two hybrid embeddings on a cholangiocarcinoma Xenium dataset, systematically varying latent dimensions ($k$=5-40) and clustering resolutions ($ρ$=0.1-1.2). Each configuration is evaluated using complementary metrics including reconstruction error, explained variance, cluster cohesion, and two novel biologically-motivated measures: Cluster Marker Coherence (CMC) and Marker Exclusion Rate (MER). Our results demonstrate distinct performance profiles: PCA provides a fast baseline, NMF maximizes marker enrichment, VAE balances reconstruction and interpretability, while autoencoders occupy a middle ground. We provide systematic hyperparameter selection using Pareto optimal analysis and demonstrate how MER-guided reassignment improves biological fidelity across all methods, with CMC scores improving by up to 12\% on average. This framework enables principled selection of dimensionality reduction methods tailored to specific spatial transcriptomics analyses.
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