title: A comparison of statistical methods for detecting differentially expressed genes from RNA-seq data.
abstract: RNA-Seq technologies are quickly revolutionizing genomic studies, and statistical methods for RNA-seq data are under continuous development. Timely review and comparison of the most recently proposed statistical methods will provide a useful guide for choosing among them for data analysis. Particular interest surrounds the ability to detect differential expression (DE) in genes. Here we compare four recently proposed statistical methods, edgeR, DESeq, baySeq, and a method with a two-stage Poisson model (TSPM), through a variety of simulations that were based on different distribution models or real data. We compared the ability of these methods to detect DE genes in terms of the significance ranking of genes and false discovery rate control. All methods compared are implemented in freely available software. We also discuss the availability and functions of the currently available versions of these software.
authors.affiliation: Department of Statistics, Iowa State University, Snedecor Hall, Ames, Iowa 50011-1210, USA.
authors.forename: Vanessa M
authors.initials: VM
authors.lastname: Kvam
authors.forename: Peng
authors.initials: P
authors.lastname: Liu
authors.forename: Yaqing
authors.initials: Y
authors.lastname: Si
mesh_terms: D001483:Base Sequence; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D030561:Databases, Nucleic Acid; D018506:Gene Expression Regulation, Plant; D017343:Genes, Plant; D008957:Models, Genetic; D016012:Poisson Distribution; D018749:RNA, Plant / Q000235:genetics*; D012372:ROC Curve; D015203:Reproducibility of Results; D017423:Sequence Analysis, RNA / Q000379:methods; D012984:Software
chemical_list: D018749:RNA, Plant