Clustering for time-series gene expression data using mixture of constrained PCAS

In a cluster analysis of gene expression time-series data, it is often required that genes with similar expression patterns should be classified into the same cluster regardless of their magnitude (scale). We propose a clustering method for gene expression time-series data based on mixture of constrained PCAs (MCPCA). The proposed method is scale-insensitive, while keeping the robustness to noise possibly involved in expression patterns with a small magnitude. We also propose a method that combines clustering results in order to improve the stability of the cluster analysis. The proposed method was applied to a time-series gene expression data set. In the experiment, an appropriate number of clusters was determined based on a statistical criterion. Furthermore, by combining clustering results, robustness of the cluster analysis was achieved. As a result, our method was able to catch biologically-meaningful expression patterns.