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

被引：15

作者：

Olivieri, Julia Eve ^{[1
,2
]}

Dehghannasiri, Roozbeh ^{[1
,3
]}

Salzman, Julia ^{[1
,3
]}

机构：

[1] Stanford Univ, Dept Biomed Data Sci, Stanford, CA 94305 USA

[2] Stanford Univ, Inst Computat & Math Engn, Stanford, CA 94305 USA

[3] Stanford Univ, Dept Biochem, Stanford, CA 94305 USA

来源：

NATURE METHODS | 2022年 / 19卷 / 03期

基金：

美国国家科学基金会;

关键词：

RNA; ROBUST;

D O I：

10.1038/s41592-022-01400-x

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

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 Splicing Z Score (SpliZ) provides single-cell-level quantification of alternative splicing with improved statistical power.

引用

页码：307 / +

页数：19

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