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bioRxiv · 10.1101/2020.11.04.368019

Computational identification of splicing phenotypes from single cell transcriptomic experiments

Huang, Y.·Sanguinetti, G.
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Abstract

RNA splicing is an important driver of heterogeneity in single cells, both through the expression of alternative transcripts and as a major 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. Here, we present BRIE2, 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 alternative splicing events that are associated with a disease. Additionally, BRIE2 allows a principled selection of genes (differential momentum genes) that capture heterogeneity in transcriptional kinetics and improve quantitatively 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.

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Huang, Y., Sanguinetti, G.. 2020-11-05. Computational identification of splicing phenotypes from single cell transcriptomic experiments. https://doi.org/10.1101/2020.11.04.368019

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