Statistical Design and Analysis of RNA Sequencing Data

被引：259

作者：

Auer, Paul L. ^{[1
]}

Doerge, R. W. ^{[1
]}

机构：

[1] Purdue Univ, Dept Stat, W Lafayette, IN 47907 USA

来源：

GENETICS | 2010年 / 185卷 / 02期

基金：

美国国家科学基金会;

关键词：

GENE-EXPRESSION; SERIAL ANALYSIS; DIFFERENTIAL EXPRESSION; SAGE LIBRARIES; BIOCONDUCTOR; REGRESSION; TESTS; SEQ;

D O I：

10.1534/genetics.110.114983

中图分类号：

Q3 [遗传学];

学科分类号：

071007 ; 090102 ;

摘要：

Next-generation sequencing technologies are quickly becoming the preferred approach for characterizing and quantifying entire genomes. Even though data produced from these technologies are proving to be the most informative of any thus far, very little attention has been paid to fundamental design aspects of data collection and analysis, namely sampling, randomization, replication, and blocking. We discuss these concepts in an RNA sequencing framework. Using simulations we demonstrate the benefits of collecting replicated RNA sequencing data according to well known statistical designs that partition the sources of biological and technical variation. Examples of these designs and their corresponding models are presented with the goal of testing differential expression.

引用

页码：405 / U32

页数：16

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