Time Series Expression Analyses Using RNA-seq: A Statistical Approach

Figure 3

Venn diagram of five DE sets for static and dynamic methods in a sheep model data. In static methods, for two simple pairwise methods, Audic-Claverie statistics and Fisher’s exact test were performed and both methods take a union set of all possible pairwise comparisons to identify temporally differentially expressed (TDE) genes across time points and two healing systems. As another static approach, pooling methods of samples, log linear model in [39], and generalized linear model fit in edge R in [36] were performed and detected TDE genes by FDR 0.05. In dynamic HMM method, we identify top candidate TDE genes defined at least showing DE pattern from one time point based on posterior probabilities for latent variables (DE/EE between given biological conditions). On the basis of comparison of the number of DE genes identified by each method, patterns of identification of TDE genes are method specific suggesting validation procedures of methods in biological aspects.