Clustering is indispensable to obtain a general view of series data from a number of data such as gene expression profiles. We propose a novel metric for clustering. The proposed metric automatically normalizes data to minimize a logarithmic scale distance between the data series.

Activity patterns of metabolic subnetworks, each of which can be regarded as a biological function module, were focused on in order to clarify biological meanings of observed deviation patterns of gene expressions induced by various chemical stimuli. We tried to infer association structures of genes by applying the multivariate statistical method called graphical Gaussian modeling to the gene expression data in a subnetwork-wise manner. It can be expected that the obtained graphical models will provide reasonable relationships between gene expressions and macroscopic biological functions. In this study, the gene expression patterns in nematodes under various conditions (stresses by chemicals such as heavy metals and endocrine disrupters) were observed using DNA microarrays. The graphical models for metabolic subnetworks were obtained from these expression data. The obtained models (independence graph) represent gene association structures of cooperativities of genes. We compared each independence graph with a corresponding metabolic subnetwork. Then we obtained a pattern that is a set of characteristic values for these graphs, and found that the pattern of heavy metals differs considerably from that of endocrine disrupters. This implies that a set of characteristic values of the graphs can representative a macroscopic biological meaning.