Time Course RNA-seq: A Potential Avenue with Somewhat Different Approach in Tandem of Differential Analysis

RNA-seq is exponentially becoming the de facto standard approach to compel considerable advantages over conventional technologies such as micro array by directly sequencing transcripts in gene expression profile. As the cost to sequencing is dropping rapidly, studies to dynamic change of gene expression in a given biological system over time have shown steady growth over the past few years as micro array, however, statistical approaches to characterize dynamic temporal complexities are currently elusive. In differential gene expression analysis, as somehow limited but intuitive solutions, static differential expression methods without respect to time can be applied, which do not take into account the inherent dependencies in time series explicitly that the expression patterns at later stages are dependent on patterns at earlier stages. We present a statistical framework to define dynamic gene expression patterns over time using trajectory index and Hidden Markov Model (HMM) approach in time series RNA-seq data, and our methods are validated through Markov Chain Monte Carlo (MCMC) simulation study in time series dependent data. The utility of the dynamic specific methods for temporal RNA-seq is demonstrated by application to the analyses of gene expression patterns in RNA-seq seven real data sets and MCMC simulation study in details.