title: Statistical methods for identifying differentially expressed genes in RNA-Seq experiments.
abstract: 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.
authors: Biostatistics Program, School of Public Health, LSU Health Sciences Center, 2020 Gravier Street, 3rd floor, New Orleans, LA, 70112, USA. zfang@lsuhsc.edu.
authors: Zhide
authors: Z
authors: Fang
authors: Jeffrey
authors: J
authors: Martin
authors: Zhong
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authors: Wang