A scaling normalization method for differential expression analysis of RNA-seq data

被引：5232

作者：

Robinson, Mark D. ^{[1
,2
]}

Oshlack, Alicia ^{[1
]}

机构：

[1] Walter & Eliza Hall Inst Med Res, Bioinformat Div, Parkville, Vic 3052, Australia

[2] St Vincents Hosp, Garvan Inst Med Res, Canc Program, Epigenet Lab, Darlinghurst, NSW 2010, Australia

来源：

GENOME BIOLOGY | 2010年 / 11卷 / 03期

基金：

英国医学研究理事会;

关键词：

HOUSEKEEPING GENES; MICROARRAY DATA; TRANSCRIPTOME; BIOCONDUCTOR; MODEL; BIOLOGY; SAGE; BIAS;

D O I：

10.1186/gb-2010-11-3-r25

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

The fine detail provided by sequencing-based transcriptome surveys suggests that RNA-seq is likely to become the platform of choice for interrogating steady state RNA. In order to discover biologically important changes in expression, we show that normalization continues to be an essential step in the analysis. We outline a simple and effective method for performing normalization and show dramatically improved results for inferring differential expression in simulated and publicly available data sets.

引用

页数：9

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