Orchestrating single-cell analysis with Bioconductor

被引：516

作者：

Amezquita, Robert A. ^{[1
]}

Lun, Aaron T. L. ^{[2
,16
]}

Becht, Etienne ^{[1
]}

Carey, Vince J. ^{[3
]}

Carpp, Lindsay N. ^{[1
]}

Geistlinger, Ludwig ^{[4
,5
]}

Marini, Federico ^{[6
,7
]}

Rue-Albrecht, Kevin ^{[8
]}

Risso, Davide ^{[9
,10
]}

Soneson, Charlotte ^{[11
,12
]}

Waldron, Levi ^{[4
,5
]}

Pages, Herve ^{[1
]}

Smith, Mike L. ^{[13
]}

Huber, Wolfgang ^{[13
]}

Morgan, Martin ^{[14
]}

Gottardo, Raphael ^{[1
]}

Hicks, Stephanie C. ^{[15
]}

机构：

[1] Fred Hutchinson Canc Res Ctr, 1124 Columbia St, Seattle, WA 98104 USA

[2] Univ Cambridge, Canc Res UK Cambridge Inst, Cambridge, England

[3] Brigham & Womens Hosp, Channing Div Network Med, 75 Francis St, Boston, MA 02115 USA

[4] CUNY, Grad Sch Publ Hlth & Hlth Policy, New York, NY 10021 USA

[5] CUNY, Inst Implementat Sci Populat Hlth, New York, NY 10021 USA

[6] Ctr Thrombosis & Hemostasis, Mainz, Germany

[7] Inst Med Biostat Epidemiol & Informat, Mainz, Germany

[8] Univ Oxford, Kennedy Inst Rheumatol, Oxford, England

[9] Univ Padua, Dept Stat Sci, Padua, Italy

[10] Weill Cornell Med, Dept Healthcare Policy & Res, Div Biostat & Epidemiol, New York, NY USA

[11] Friedrich Miescher Inst Biomed Res, Basel, Switzerland

[12] SIB Swiss Inst Bioinformat, Basel, Switzerland

[13] European Mol Biol Lab, Genome Biol Unit, Heidelberg, Germany

[14] Roswell Pk Comprehens Canc Ctr, Biostat & Bioinformat, Buffalo, NY USA

[15] Johns Hopkins Bloomberg Sch Publ Hlth, Dept Biostat, Baltimore, MD 21205 USA

[16] Genentech Inc, Bioinformat & Computat Biol, 460 Point San Bruno Blvd, San Francisco, CA 94080 USA

来源：

NATURE METHODS | 2020年 / 17卷 / 02期

基金：

欧盟地平线“2020”; 英国惠康基金; 美国国家卫生研究院;

关键词：

RNA-SEQ DATA; DIFFERENTIAL EXPRESSION; GENE-EXPRESSION; SEQUENCING DATA; QUALITY-CONTROL; INFERENCE; VISUALIZATION; PACKAGE; GENOME;

D O I：

10.1038/s41592-019-0654-x

中图分类号：

Q5 [生物化学];

学科分类号：

070307 [化学生物学];

摘要：

This Perspective highlights open-source software for single-cell analysis released as part of the Bioconductor project, providing an overview for users and developers. Recent technological advancements have enabled the profiling of a large number of genome-wide features in individual cells. However, single-cell data present unique challenges that require the development of specialized methods and software infrastructure to successfully derive biological insights. The Bioconductor project has rapidly grown to meet these demands, hosting community-developed open-source software distributed as R packages. Featuring state-of-the-art computational methods, standardized data infrastructure and interactive data visualization tools, we present an overview and online book () of single-cell methods for prospective users.

引用

页码：137 / 145

页数：9

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