A Benchmark of Semi-Supervised scRNA-seq Integration Methods in Real-World Scenarios.

ABSTRACT: 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 scA

SUBMITTER: Shen X 

PROVIDER: S-EPMC12407688 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature