UCell: Robust and scalable single-cell gene signature scoring

被引：409

作者：

Andreatta, Massimo ^{[1
,2
,3
,4
]}

Carmona, Santiago J. ^{[1
,2
,3
,4
]}

机构：

[1] CHUV, Lausanne Branch, Ludwig Inst Canc Res, CH-1066 Epalinges, Switzerland

[2] CHUV, Dept Oncol, CH-1066 Epalinges, Switzerland

[3] Univ Lausanne, CH-1066 Epalinges, Switzerland

[4] Swiss Inst Bioinformat, Lausanne, Switzerland

来源：

COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL | 2021年 / 19卷

基金：

瑞士国家科学基金会;

关键词：

Single-cell; Gene signature; Module scoring; Cell type; Gene set enrichment;

D O I：

10.1016/j.csbj.2021.06.043

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

UCell is an R package for evaluating gene signatures in single-cell datasets. UCell signature scores, based on the Mann-Whitney U statistic, are robust to dataset size and heterogeneity, and their calculation demands less computing time and memory than other available methods, enabling the processing of large datasets in a few minutes even on machines with limited computing power. UCell can be applied to any single-cell data matrix, and includes functions to directly interact with Seurat objects. The UCell package and documentation are available on GitHub at https://github.com/carmonalab/UCell. (C) 2021 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.

引用

页码：3796 / 3798

页数：3

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