title: Time series expression analyses using RNA-seq: a statistical approach.
abstract: RNA-seq is becoming the de facto standard approach for transcriptome analysis with ever-reducing cost. It has considerable advantages over conventional technologies (microarrays) because it allows for direct identification and quantification of transcripts. Many time series RNA-seq datasets have been collected to study the dynamic regulations of transcripts. However, statistically rigorous and computationally efficient methods are needed to explore the time-dependent changes of gene expression in biological systems. These methods should explicitly account for the dependencies of expression patterns across time points. Here, we discuss several methods that can be applied to model timecourse RNA-seq data, including statistical evolutionary trajectory index (SETI), autoregressive time-lagged regression (AR(1)), and hidden Markov model (HMM) approaches. We use three real datasets and simulation studies to demonstrate the utility of these dynamic methods in temporal analysis.
authors.affiliation: Department of Pediatrics, Children's Hospital Medical Center, Cincinnati, OH 45229-3039, USA. sunghee.oh@cchmc.org
authors.forename: Sunghee
authors.initials: S
authors.lastname: Oh
authors.forename: Seongho
authors.initials: S
authors.lastname: Song
authors.forename: Gregory
authors.initials: G
authors.lastname: Grabowski
authors.forename: Hongyu
authors.initials: H
authors.lastname: Zhao
authors.forename: James P
authors.initials: JP
authors.lastname: Noonan
mesh_terms: D001483:Base Sequence; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D012313:RNA / Q000235:genetics*; D017423:Sequence Analysis, RNA / Q000379:methods / Q000706:statistics & numerical data*
chemical_list: D012313:RNA