Statistical methods for identifying differentially expressed genes in RNA-Seq experiments.
Statistical methods for identifying differentially expressed genes in RNA-Seq experiments.
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DOI：
10.1186/2045-3701-2-26
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发表时间：
2012-07-31
影响因子：
7.5
通讯作者：
Wang Z
中科院分区：
文献类型：
--
作者：
Fang Z;Martin J;Wang Z
RNA测序（RNA-Seq）正在迅速取代用于基因表达谱分析的微阵列，其准确性和灵敏度大大提高。在一个典型的基因分析实验中最常见的问题之一是如何识别一组在不同实验条件下差异表达的转录本。一些为微阵列数据分析开发的统计方法可以在修改或不修改的情况下应用于RNA-Seq数据。最近，专门针对RNA-Seq数据集开发了几种其他方法。本文试图对这些统计方法进行深入的回顾，目的是为RNA-Seq统计分析选择合适的指标提供全面的指导。
RNA sequencing (RNA-Seq) is rapidly replacing microarrays for profiling gene expression with much improved accuracy and sensitivity. One of the most common questions in a typical gene profiling experiment is how to identify a set of transcripts that are differentially expressed between different experimental conditions. Some of the statistical methods developed for microarray data analysis can be applied to RNA-Seq data with or without modifications. Recently several additional methods have been developed specifically for RNA-Seq data sets. This review attempts to give an in-depth review of these statistical methods, with the goal of providing a comprehensive guide when choosing appropriate metrics for RNA-Seq statistical analyses.
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