Computational identification of splicing phenotypes from single cell transcriptomic experiments

RNA splicing is an important driver of heterogeneity in single cells, and a major determinant of the dynamical state of developing cells. However, the intrinsic coverage limitations of scRNA-seq technologies make it challenging to associate specific splicing events to cell-level phenotypes. Here, we present BRIE2, a scalable computational method that resolves these issues by regressing single-cell transcriptomic data against cell-level features. We show on different biological systems that BRIE2 effectively identifies differential splicing events that are associated with disease or developmental lineages, and detects differential momentum genes for improving RNA velocity analyses. BRIE2 therefore extends the scope of single-cell transcriptomic experiments towards the identification of splicing phenotypes associated with biological changes at the single-cell level.