Searching the principal genes for neural differentiation of mouse ES cells by factorizing eigengenes of clusters

A time-series microarray experiment is useful to study the changes in the expression of a large number of genes over time. Many methods for clustering genes using gene expression profiles have been suggested, but it is not easy to interpret the biological significance of the results or utilize these methods for understanding the dynamics of gene regulatory systems. In this study, we introduce an algorithm for readjusting the boundaries of clusters by adopting the advantages of both k-means and singular value decomposition (SVD). In addition, we suggest a methodology for searching the principal genes that can be the most crucial genes in regulation of clusters. We found 34 principal genes from 171 clusters having strong concentratedness in their expression patterns and distinct ranges of oscillatory phases, by using a time-series microarray dataset of mouse embryonic stem (ES) cells after induction of dopaminergic neural differentiation. The biological significance of the principal genes examined in the literature supports the feasibility of our algorithms in that the hierarchy of clusters may lead the manifestation of the phenotypes, e.g., the development of the nervous system.     

Bayesian hierarchical modeling for time course microarray experiments

Time course microarray experiments designed to characterize the dynamic regulation of gene expression in biological systems are becoming increasingly important. One critical issue that arises when examining time course microarray data is the identification of genes that show different temporal expression patterns among biological conditions. Here we propose a Bayesian hierarchical model to incorporate important experimental factors and to account for correlated gene expression measurements over time and over different genes. A new gene selection algorithm is also presented with the model to simultaneously identify genes that show changes in expression among biological conditions, in response to time and other experimental factors of interest. The algorithm performs well in terms of the false positive and false negative rates in simulation studies. The methodology is applied to a mouse model time course experiment to correlate temporal changes in azoxymethane-induced gene expression profiles with colorectal cancer susceptibility.