NDLI: A New Approach to Identify Functional Modules Using Random Matrix Theory

Content Provider	IEEE Xplore Digital Library
Author	Mengxia Zhu Qishi Wu Yunfeng Yang Jizhong Zhou
Copyright Year	2006
Description	Author affiliation: Dept. of Comput. Sci., Southern Illinois Univ., Carbondale, IL (Mengxia Zhu)
Abstract	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
Starting Page	1
Ending Page	7
File Size	7696737
Page Count	7
File Format	PDF
ISBN	1424406234
DOI	10.1109/CIBCB.2006.330980
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-09-28
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genomics Bioinformatics Gene expression Humans Information analysis Large-scale systems Testing Statistical analysis Eigenvalues and eigenfunctions Fungi Varimax orthogonal rotation Random Matrix Microarray Pearson correlation Eigen-value Eigenvector
Content Type	Text
Resource Type	Article

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