用于批量和单细胞RNA测序应用的差异转录本使用情况的可扩展分析。

: Scalable analysis of differential transcript usage for bulk and single-cell RNA-sequencing applications.

作者信息

Gilis Jeroen, Vitting-Seerup Kristoffer, Van den Berge Koen, Clement Lieven

机构信息

Applied Mathematics, Computer science and Statistics, Ghent University, Ghent, 9000, Belgium.

Data Mining and Modeling for Biomedicine, VIB Flemish Institute for Biotechnology, Ghent, 9000, Belgium.

出版信息

F1000Res. 2021 May 11;10:374. doi: 10.12688/f1000research.51749.2. eCollection 2021.

Abstract

Alternative splicing produces multiple functional transcripts from a single gene. Dysregulation of splicing is known to be associated with disease and as a hallmark of cancer. Existing tools for differential transcript usage (DTU) analysis either lack in performance, cannot account for complex experimental designs or do not scale to massive single-cell transcriptome sequencing (scRNA-seq) datasets. We introduce , a fast and flexible quasi-binomial generalized linear modelling framework that is on par with the best performing DTU methods from the bulk RNA-seq realm, while providing good false discovery rate control, addressing complex experimental designs, and scaling to scRNA-seq applications.

摘要

可变剪接可从单个基因产生多个功能性转录本。已知剪接失调与疾病相关,且是癌症的一个标志。现有的差异转录本使用情况(DTU)分析工具要么性能不足,无法处理复杂的实验设计,要么无法扩展到大规模单细胞转录组测序(scRNA-seq)数据集。我们引入了一种快速且灵活的准二项式广义线性建模框架,该框架与来自批量RNA-seq领域性能最佳的DTU方法相当,同时能很好地控制错误发现率,处理复杂的实验设计,并可扩展到scRNA-seq应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/03e1/9892656/8e68dd5e3e3c/f1000research-10-136695-g0000.jpg

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