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

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Alternative splicing significantly expands proteomic and transcriptomic diversity, crucial for cell identity and function.
• Single-cell RNA sequencing (scRNA-seq) offers cellular resolution for transcriptome profiling, but method performance for splicing analysis is not well-established.

Purpose of the Study:

• To systematically benchmark alternative splicing analysis methods using scRNA-seq data.
• To evaluate method performance on key biological tasks like cell clustering and differential splicing detection.
• To identify a robust method for uncovering cell type-specific splicing programs.

Main Methods:

• Benchmarking of six representative alternative splicing analysis tools.
• Utilized multiple full-length, short-read scRNA-seq datasets.
• Focused on tasks including cell clustering and differential splicing detection.

Main Results:

• scQuint demonstrated robust and consistent performance across various datasets and analytical tasks.
• Application of scQuint identified biologically significant, cell type-specific splicing programs.
• The study successfully applied scQuint to stem cell differentiation and tumor-associated immune cell datasets.

Conclusions:

• scQuint is a recommended method for alternative splicing analysis in scRNA-seq data.
• Single-cell splicing analysis provides valuable insights into regulatory mechanisms beyond gene expression.
• The findings guide method selection for scRNA-seq splicing studies and highlight its utility in biological discovery.