Time series expression analyses using RNA-seq: a statistical approach

Biomed Res Int. 2013:2013:203681. doi: 10.1155/2013/203681. Epub 2013 Mar 24.

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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Base Sequence
  • Gene Expression Profiling / methods*
  • Gene Expression*
  • Humans
  • Markov Chains
  • Models, Statistical
  • RNA / genetics*
  • Sequence Analysis, RNA / methods
  • Sequence Analysis, RNA / statistics & numerical data*

Substances

  • RNA