title: Differential Expression Analysis for RNA-Seq: An Overview of Statistical Methods and Computational Software.
abstract: Deep sequencing has recently emerged as a powerful alternative to microarrays for the high-throughput profiling of gene expression. In order to account for the discrete nature of RNA sequencing data, new statistical methods and computational tools have been developed for the analysis of differential expression to identify genes that are relevant to a disease such as cancer. In this paper, it is thus timely to provide an overview of these analysis methods and tools. For readers with statistical background, we also review the parameter estimation algorithms and hypothesis testing strategies used in these methods.
authors.affiliation: Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
authors.forename: Huei-Chung
authors.initials: HC
authors.lastname: Huang
authors.affiliation: Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
authors.forename: Yi
authors.initials: Y
authors.lastname: Niu
authors.affiliation: Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
authors.forename: Li-Xuan
authors.initials: LX
authors.lastname: Qin
keywords: RNA sequencing; differential expression analysis; overview; software; statistical methods