Highly accurate reference and method selection for universal cross-dataset cell type annotation with CAMUS

Abstract

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, how to select the optimal references and methods is often overlooked. To this end, we present a cross-dataset 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 datasets. The annotation results with references selected by CAMUS achieved substantial accuracy gains (25.0-124.7%) over random selection strategies across five reference-based methods. CAMUS achieved high accuracy in choosing the best reference-method pair among 3360 pairs (49.1%). Moreover, CAMUS showed high accuracy in selecting the best methods on the 80 scST datasets (82.5%) and five scATAC-seq datasets (100.0%), illustrating its universal applicability. In addition, we utilized the CAMUS score with other metrics to predict the annotation accuracy, providing direct guidance on whether to accept current annotation results.