Measuring Correlation between Microarray Time-series Data using Dominant Spectral Component

Abstract

Microarray time-series data provides us a possible means for identification of transcriptional regulation relationships among genes. Currently, the most widely used method in determining whether or not two genes have a potential regulatory relationship is to measure their expressional similarity using Pearson's correlation coefficient. Although this traditional correlation method has been successfully applied to find functionally correlated genes, it does have many limitations. In this paper, we propose a new metric for more reliable measurement of correlation between gene expression data. In our method, time-series expression profiles are decomposed into spectral components and correlations between them are computed in a component-wise sense. This technique has been applied to known gene regulations of yeast and is able to identify many of those missed by the traditional correlation method.