BRIE2: computational identification of splicing phenotypes from single-cell transcriptomic experiments

RNA splicing is an important driver of heterogeneity in single cells through the expression of alternative transcripts and as a determinant of transcriptional kinetics. However, the intrinsic coverage limitations of scRNA-seq technologies make it challenging to associate specific splicing events to cell-level phenotypes. BRIE2 is a scalable computational method that resolves these issues by regressing single-cell transcriptomic data against cell-level features. We show that BRIE2 effectively identifies differential disease-associated alternative splicing events and allows a principled selection of genes that capture heterogeneity in transcriptional kinetics and improve RNA velocity analyses, enabling the identification of splicing phenotypes associated with biological changes.

Automated summary

RNA splicing plays a crucial role in driving heterogeneity in single cells through alternative transcript expression and transcriptional kinetics. BRIE2, a scalable computational method, effectively identifies differential disease-associated alternative splicing events and improves RNA velocity analysis, enabling exploration of the association between splicing phenotypes and biological changes.