Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory

Luo, Feng; Yang, Yunfeng; Zhong, Jianxin; Gao, Haichun; Khan, Latifur; Thompson, Dorothea K.; Zhou, Jizhong

doi:10.1186/1471-2105-8-299

Title: Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory

Journal Article · Tue Aug 14 00:00:00 EDT 2007 · BMC Bioinformatics

DOI:https://doi.org/10.1186/1471-2105-8-299· OSTI ID:1626342

Luo, Feng ^[1]; Yang, Yunfeng ^[2]; Zhong, Jianxin ^[3]; Gao, Haichun ^[4]; Khan, Latifur ^[5]; Thompson, Dorothea K. ^[6]; Zhou, Jizhong ^[4]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Environmental Sciences Division; Clemson Univ., SC (United States). School of Computing
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Environmental Sciences Division
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computer Science and Mathematics Division; Xiangtan Univ., Hunan (China). Dept. of Physics
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Environmental Sciences Division; Univ. of Oklahoma, Norman, OK (United States). Dept. of Botany and Microbiology. Inst. for Environmental Genomics
Univ. of Texas at Dallas, Richardson, TX (United States). Dept. of Computer Science
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Environmental Sciences Division; Purdue Univ., West Lafayette, IN (United States). Dept. of Biological Sciences

Background: Large-scale sequencing of entire genomes has ushered in a new age in biology. One of the next grand challenges is to dissect the cellular networks consisting of many individual functional modules. Defining co-expression networks without ambiguity based on genome-wide microarray data is difficult and current methods are not robust and consistent with different data sets. This is particularly problematic for little understood organisms since not much existing biological knowledge can be exploited for determining the threshold to differentiate true correlation from random noise. Random matrix theory (RMT), which has been widely and successfully used in physics, is a powerful approach to distinguish system-specific, non-random properties embedded in complex systems from random noise. Here, we have hypothesized that the universal predictions of RMT are also applicable to biological systems and the correlation threshold can be determined by characterizing the correlation matrix of microarray profiles using random matrix theory. Results: Application of random matrix theory to microarray data of S. oneidensis, E. coli, yeast, A. thaliana, Drosophila, mouse and human indicates that there is a of nearest neighbour spacing distribution (NNSD) of correlation matrix after gradually removing certain elements insider the matrix. Testing on an in silico modular model has demonstrated that this transition can be used to determine the correlation threshold for revealing modular co-expression networks. The coexpression network derived from yeast cell cycling microarray data is supported by gene annotation. The topological properties of the resulting co-expression network agree well with the general properties of biological networks. Computational evaluations have showed that RMT approach is sensitive and robust. Furthermore, evaluation on sampled expression data of an in silico modular gene system has showed that under-sampled expressions do not affect the recovery of gene co-expression network. Moreover, the cellular roles of 215 functionally unknown genes from yeast, E. coli and S. oneidensis are predicted by the gene co-expression networks using guilt-by-association principle, many of which are supported by existing information or our experimental verification, further demonstrating the reliability of this approach for gene function prediction. Conclusion: Our rigorous analysis of gene expression microarray profiles using RMT has showed that the transition of NNSD of correlation matrix of microarray profile provides a profound theoretical criterion to determine the correlation threshold for identifying gene co-expression networks.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1626342

Journal Information:: BMC Bioinformatics, Vol. 8, Issue 1; ISSN 1471-2105

Publisher:: BioMed CentralCopyright Statement

Country of Publication:: United States

Language:: English

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Discovery and validation of a glioblastoma co-expressed gene module Dunwoodie, Leland J.; Poehlman, William L.; Ficklin, Stephen P. Oncotarget, Vol. 9, Issue 13 https://doi.org/10.18632/oncotarget.24228	journal	January 2018
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Long-Term Oil Contamination Alters the Molecular Ecological Networks of Soil Microbial Functional Genes Liang, Yuting; Zhao, Huihui; Deng, Ye Frontiers in Microbiology, Vol. 7 https://doi.org/10.3389/fmicb.2016.00060	journal	February 2016
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Investigating the Combinatory Effects of Biological Networks on Gene Co-expression Zhang, Cheng; Lee, Sunjae; Mardinoglu, Adil Frontiers in Physiology, Vol. 7 https://doi.org/10.3389/fphys.2016.00160	journal	May 2016
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