Gene grouping strategy for network modeling from a small time-series dataset: An illustrative analysis of human organogenesis

摘要

Several algorithms have been proposed for modeling a gene regulatory network from a time-series expression dataset, but these have been used in relatively few studies because experimental cost often restricts the number of sampling time points to less than that of genes by more than one order of magnitude. In order to reduce the number of parameters for network modeling, we propose a method for grouping genes by both temporal expression pattern and biological function, modeling interactions between the gene groups by a dynamic Bayesian network approach. Results from applying the method to a gene expression dataset on human organogenesis demonstrate that more biologically plausible results can be obtained by modeling an interaction network for groups of genes than by modeling that for single genes.