RT Journal
A1 Shen, Qunlun
A1 Zhang, Shuqin
A1 Zhang, Shihua
T1 Highly accurate reference and method selection for universal cross–data set cell type annotation with CAMUS
JF Genome Research 
JO Genome Research 
YR 2025 
FD November 01 
VO 35 
IS 11 
SP 2527 
OP 2538 
DO 10.1101/gr.280821.125 
UL http://genome.cshlp.org/content/35/11/2527.abstract 
AB Cell type annotation is a critical and essential task in single-cell data analysis. Various reference-based methods have provided rapid annotation for diverse single-cell data. However, selection of the optimal references and methods is often overlooked. To this end, we present a cross–data set cell type annotation methodology with a universal reference data and method selection strategy (CAMUS) to achieve highly accurate and efficient annotations. We demonstrate the advantages of CAMUS by conducting comprehensive analyses on 672 pairs of cross-species scRNA-seq data sets. The annotation results with references selected by CAMUS achieves substantial accuracy gains (25.0%–124.7%) over random selection strategies across five reference-based methods. CAMUS achieves high accuracy in choosing the best reference–method pair among 3360 pairs (49.1%). Moreover, CAMUS shows high accuracy in selecting the best methods on the 80 scST data sets (82.5%) and five scATAC-seq data sets (100.0%), illustrating its universal applicability. In addition, we utilize the CAMUS score with other metrics to predict the annotation accuracy, providing direct guidance on whether to accept current annotation results.