The SpliZ generalizes 'percent spliced in' to reveal regulated splicing at single-cell resolution

Julia Eve Olivieri; Roozbeh Dehghannasiri; Julia Salzman

doi:10.1038/s41592-022-01400-x

The SpliZ generalizes 'percent spliced in' to reveal regulated splicing at single-cell resolution

Nat Methods. 2022 Mar;19(3):307-310. doi: 10.1038/s41592-022-01400-x. Epub 2022 Mar 3.

Authors

Julia Eve Olivieri^#^{1

2}, Roozbeh Dehghannasiri^#^{1

3}, Julia Salzman^{4

5}

Affiliations

¹ Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
² Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA.
³ Department of Biochemistry, Stanford University, Stanford, CA, USA.
⁴ Department of Biomedical Data Science, Stanford University, Stanford, CA, USA. julia.salzman@stanford.edu.
⁵ Department of Biochemistry, Stanford University, Stanford, CA, USA. julia.salzman@stanford.edu.

^# Contributed equally.

Abstract

Detecting single-cell-regulated splicing from droplet-based technologies is challenging. Here, we introduce the splicing Z score (SpliZ), an annotation-free statistical method to detect regulated splicing in single-cell RNA sequencing. We applied the SpliZ to human lung cells, discovering hundreds of genes with cell-type-specific splicing patterns including ones with potential implications for basic and translational biology.

The SpliZ generalizes 'percent spliced in' to reveal regulated splicing at single-cell resolution

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