Bayesian model-based clustering of temporal gene expression using autoregressive panel data approach

Abstract

Motivation: in a microarray time series analysis, due to the large number of genes evaluated, the first step toward understanding the complex time network is the clustering of genes that share similar expression patterns over time. Up until now, the proposed methods do not point simultaneously to the temporal autocorrelation of the gene expression and the model-based clustering. We present a Bayesian method that considers jointly the fit of autoregressive panel data models and hierarchical gene clustering. Results: the proposed methodology was able to cluster genes that share similar expression over time, which was determined jointly by the estimates of autoregression parameters, by the average level of expression) and by the quality of the fitted model. Availability and implementation: the R codes for implementation of the proposed clustering method and for simulation study, as well as the real and simulated datasets, are freely accessible on the Web.