Comprehensive assessment of alternative splicing analysis methods for single-cell RNA-seq

Alternative splicing expands transcriptomic and proteomic diversity and contributes to cell identity and function. Advances in single-cell RNA sequencing enable transcriptome profiling at cellular resolution, yet their performance across biologically relevant tasks has not been systematically evaluated. Here, we benchmark six representative alternative splicing analysis methods across multiple full-length, short-read scRNA-seq datasets, focusing on biologically relevant tasks including cell clustering and differential splicing detection. Among these methods, scQuint achieves robust performance across datasets and tasks. Application of scQuint to stem cell differentiation and tumor-associated immune cell datasets identifies cell type-specific splicing programs with biological significance. These results provide guidance for method selection and highlight the value of single-cell splicing analysis for uncovering regulatory mechanisms beyond gene expression.