Shen Xiaoyu, He Chuan, Guan Leying
bioRxiv. 2025 Aug 27:2025.08.23.671952. doi: 10.1101/2025.08.23.671952.
Semi-supervised methods for single-cell RNA-seq integration promise to improve batch correction and biological signal preservation by leveraging cell-type labels. However, their reported benefits often rely on overly idealized settings. Here, we present the first systematic benchmark of five leading semi-supervised methods (scANVI, scGEN, ssSTACAS, scDREAMER, ItClust) against five widely used unsupervised baselines across six diverse datasets. We evaluate performance under five realistic annotation scenarios, including missing, erroneous, boundary-missing and mixed, batch-specific, and auto-generated labels, using nine established integration metrics. While semi-supervised methods show gains with perfect annotations, their robustness declines sharply under practical imperfections. Only scANVI and ssSTACAS maintain stable but modest improvements relative to their unsupervised counterparts, while none consistently outperform the strongest unsupervised method, scCRAFT. Our results highlight that current semi-supervised strategies offer limited practical advantage and that careful choice of integration method remains critical when label quality is uncertain.
用于单细胞RNA测序整合的半监督方法有望通过利用细胞类型标签来改善批次校正和生物信号保留。然而，它们所报告的优势往往依赖于过于理想化的设置。在这里，我们针对六个不同数据集上的五个广泛使用的无监督基线，对五种领先的半监督方法（scANVI、scGEN、ssSTACAS、scDREAMER、ItClust）进行了首次系统基准测试。我们使用九个既定的整合指标，在五种现实的注释场景下评估性能，包括缺失、错误、边界缺失和混合、批次特定以及自动生成的标签。虽然半监督方法在完美注释下显示出优势，但在实际存在缺陷的情况下，它们的稳健性会急剧下降。只有scANVI和ssSTACAS相对于无监督对应方法保持稳定但适度的改进，而没有一种方法始终优于最强的无监督方法scCRAFT。我们的结果突出表明，当前的半监督策略提供的实际优势有限，并且当标签质量不确定时，谨慎选择整合方法仍然至关重要。
