SCnorm: robust normalization of single-cell RNA-seq data

被引：212

作者：

Bacher, Rhonda ^{[1
]}

Chu, Li-Fang ^{[2
]}

Leng, Ning ^{[2
]}

Gasch, Audrey P. ^{[3
]}

Thomson, James A. ^{[2
]}

Stewart, Ron M. ^{[2
]}

Newton, Michael ^{[1
,4
]}

Kendziorski, Christina ^{[4
]}

机构：

[1] Univ Wisconsin, Dept Stat, Madison, WI 53706 USA

[2] Morgridge Inst Res, Madison, WI USA

[3] Univ Wisconsin, Lab Genet, Madison, WI USA

[4] Univ Wisconsin, Dept Biostat & Med Informat, Madison, WI 53706 USA

来源：

NATURE METHODS | 2017年 / 14卷 / 06期

关键词：

DIFFERENTIAL EXPRESSION ANALYSIS; COMPUTATIONAL ANALYSIS; SEQUENCING DATA; HETEROGENEITY; CYCLE; REVEALS;

D O I：

10.1038/nmeth.4263

中图分类号：

Q5 [生物化学];

学科分类号：

070307 [化学生物学];

摘要：

The normalization of RNA-seq data is essential for accurate downstream inference, but the assumptions upon which most normalization methods are based are not applicable in the single-cell setting. Consequently, applying existing normalization methods to single-cell RNA-seq data introduces artifacts that bias downstream analyses. To address this, we introduce SCnorm for accurate and efficient normalization of single-cell RNA-seq data.

引用

收藏

页码：584 / +

页数：6

相关论文

共 27 条

[1]

Differential expression analysis for sequence count data [J].

Anders, Simon ;

Huber, Wolfgang .

GENOME BIOLOGY, 2010, 11 (10)

[2]

HTSeq-a Python']Python framework to work with high-throughput sequencing data [J].

Anders, Simon ;

Pyl, Paul Theodor ;

Huber, Wolfgang .

BIOINFORMATICS, 2015, 31 (02) :166-169

[3]

Design and computational analysis of single-cell RNA-sequencing experiments [J].

Bacher, Rhonda ;

Kendziorski, Christina .

GENOME BIOLOGY, 2016, 17

[4]

CONTROLLING THE FALSE DISCOVERY RATE - A PRACTICAL AND POWERFUL APPROACH TO MULTIPLE TESTING [J].

BENJAMINI, Y ;

HOCHBERG, Y .

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 1995, 57 (01) :289-300

[5]

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells [J].

Buettner, Florian ;

Natarajan, Kedar N. ;

Casale, F. Paolo ;

Proserpio, Valentina ;

Scialdone, Antonio ;

Theis, Fabian J. ;

Teichmann, Sarah A. ;

Marioni, John C. ;

Stegie, Oliver .

NATURE BIOTECHNOLOGY, 2015, 33 (02) :155-160

[6]

Chen GK, 2011, NAT METHODS, V8, P424, DOI [10.1038/nmeth.1593, 10.1038/NMETH.1593]

[7]

Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm [J].

Chu, Li-Fang ;

Leng, Ning ;

Zhang, Jue ;

Hou, Zhonggang ;

Mamott, Daniel ;

Vereide, David T. ;

Choi, Jeea ;

Kendziorski, Christina ;

Stewart, Ron ;

Thomson, James A. .

GENOME BIOLOGY, 2016, 17

[8]

A survey of best practices for RNA-seq data analysis [J].

Conesa, Ana ;

Madrigal, Pedro ;

Tarazona, Sonia ;

Gomez-Cabrero, David ;

Cervera, Alejandra ;

McPherson, Andrew ;

Szczesniak, Michal Wojciech ;

Gaffney, Daniel J. ;

Elo, Laura L. ;

Zhang, Xuegong ;

Mortazavi, Ali .

GENOME BIOLOGY, 2016, 17

[9]

MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data [J].

Finak, Greg ;

McDavid, Andrew ;

Yajima, Masanao ;

Deng, Jingyuan ;

Gersuk, Vivian ;

Shalek, Alex K. ;

Slichter, Chloe K. ;

Miller, Hannah W. ;

McElrath, M. Juliana ;

Prlic, Martin ;

Linsley, Peter S. ;

Gottardo, Raphael .

GENOME BIOLOGY, 2015, 16

[10]

Fluidigm Corporation, 2017, US C1 SINGL CELL AUT