TY  - JOUR
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
Y1  - 2025/11/01 
JF  - Genome Research 
JO  - Genome Research 
SP  - 2527 
EP  - 2538 
DO  - 10.1101/gr.280821.125 
VL  - 35 
IS  - 11 
UR  - http://genome.cshlp.org/content/35/11/2527.abstract 
N2  - 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. 
ER  -