A benchmark of semi-supervised scRNA-seq integration methods in real-world scenarios
Fig 7
Impact of inconsistent label granularity on integration performance.
(a-c) Benchmarking results on the human immune dataset where 30% (a), 50% (b), and 70% (c) of batches were assigned coarse-grained labels, while the remainder retained fine-grained labels. Bar plots show the overall integration score (y-axis) across three strategies: coarse label (harmonizing all batches to coarse levels), mixing coarse (using inconsistent labels as-is), and unannotated (masking coarse labels as “Unknown”). Semi-supervised methods’ performance using oracle label and unsupervised methods’ performance are also plotted as reference. Colors represent different integration methods. Note that generative methods (e.g., scDREAMER, scGEN) generally outperform the unannotated baseline when using mixed labels, whereas reference-based methods (e.g., scANVI) show sensitivity to label inconsistency. Dotted lines represent the baseline performance of unsupervised integration.).