Published in Zhu, M., Wu, Q., Yang, Y. & Zhou, J. (2006). A new approach to identify functional modules using random matrix theory. IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, 2006. CIBCB '06, 1-7. doi: 10.1109/CIBCB.2006.330980 ©2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.


The advance in high-throughput genomic technologies including microarrays has generated a tremendous amount of gene expression data for the entire genome. Deciphering transcriptional networks that convey information on members of gene clusters and cluster interactions is a crucial analysis task in the post-sequence era. Most of the existing analysis methods for large-scale genome-wide gene expression profiles involve several steps that often require human intervention. We propose a random matrix theory-based approach to analyze the cross correlations of gene expression data in an entirely automatic and objective manner to eliminate the ambiguities and subjectivity inherent to human decisions. The correlations calculated from experimental measurements typically contain both "genuine" and "random" components. In the proposed approach, we remove the "random" component by testing the statistics of the eigenvalues of the correlation matrix against a "null hypothesis" -- a truly random correlation matrix obtained from mutually uncorrelated expression data series. Our investigation on the components of deviating eigenvectors using varimax orthogonal rotation reveals distinct functional modules. We apply the proposed approach to the publicly available yeast cycle expression data and produce a transcriptional network that consists of interacting functional modules. The experimental results nicely conform to those obtained in previously published literatures.