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

Abstract

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 demonstratedmore » 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.« less

Authors:
 [1];  [2];  [3];  [4];  [5];  [6];  [4]
+ Show Author Affiliations
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Environmental Sciences Division; Clemson Univ., SC (United States). School of Computing
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Environmental Sciences Division
  3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computer Science and Mathematics Division; Xiangtan Univ., Hunan (China). Dept. of Physics
  4. 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
  5. Univ. of Texas at Dallas, Richardson, TX (United States). Dept. of Computer Science
  6. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Environmental Sciences Division; Purdue Univ., West Lafayette, IN (United States). Dept. of Biological Sciences
Publication Date:
Research Org.:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division
OSTI Identifier:
1626342
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
BMC Bioinformatics
Additional Journal Information:
Journal Volume: 8; Journal Issue: 1; Journal ID: ISSN 1471-2105
Publisher:
BioMed Central
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING; Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Mathematical & Computational Biology

Citation Formats

Luo, Feng, Yang, Yunfeng, Zhong, Jianxin, Gao, Haichun, Khan, Latifur, Thompson, Dorothea K., and Zhou, Jizhong. Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory. United States: N. p., 2007. Web. doi:10.1186/1471-2105-8-299.
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Luo, Feng, Yang, Yunfeng, Zhong, Jianxin, Gao, Haichun, Khan, Latifur, Thompson, Dorothea K., & Zhou, Jizhong. Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory. United States. https://doi.org/10.1186/1471-2105-8-299
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Luo, Feng, Yang, Yunfeng, Zhong, Jianxin, Gao, Haichun, Khan, Latifur, Thompson, Dorothea K., and Zhou, Jizhong. Tue . "Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory". United States. https://doi.org/10.1186/1471-2105-8-299. https://www.osti.gov/servlets/purl/1626342.
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@article{osti_1626342,
title = {Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory},
author = {Luo, Feng and Yang, Yunfeng and Zhong, Jianxin and Gao, Haichun and Khan, Latifur and Thompson, Dorothea K. and Zhou, Jizhong},
abstractNote = {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.},
doi = {10.1186/1471-2105-8-299},
journal = {BMC Bioinformatics},
number = 1,
volume = 8,
place = {United States},
year = {Tue Aug 14 00:00:00 EDT 2007},
month = {Tue Aug 14 00:00:00 EDT 2007}
}
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    • Feltus, F.; Ficklin, Stephen P.; Gibson, Scott M.
    • BMC Systems Biology, Vol. 7, Issue 1
    • DOI: 10.1186/1752-0509-7-44

    Assessment of weighted topological overlap (wTO) to improve fidelity of gene co-expression networks
    journal, January 2019

    The succession pattern of soil microbial communities and its relationship with tobacco bacterial wilt
    journal, October 2016

    Long noncoding RNAs expressed in human hepatic stellate cells form networks with extracellular matrix proteins
    journal, March 2016

    From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data
    journal, March 2017

    • Vella, Danila; Zoppis, Italo; Mauri, Giancarlo
    • EURASIP Journal on Bioinformatics and Systems Biology, Vol. 2017, Issue 1
    • DOI: 10.1186/s13637-017-0059-z

    The Thermoanaerobacter Glycobiome Reveals Mechanisms of Pentose and Hexose Co-Utilization in Bacteria
    journal, October 2011

    Conserved Non-Coding Regulatory Signatures in Arabidopsis Co-Expressed Gene Modules
    journal, September 2012

    Massive-Scale Gene Co-Expression Network Construction and Robustness Testing Using Random Matrix Theory
    journal, February 2013

    Tracing Evolutionary Footprints to Identify Novel Gene Functional Linkages
    journal, June 2013

    A Null Model for Pearson Coexpression Networks
    journal, June 2015

    An Approach to Function Annotation for Proteins of Unknown Function (PUFs) in the Transcriptome of Indian Mulberry
    journal, March 2016

    Comparative analysis of weighted gene co-expression networks in human and mouse
    journal, November 2017

    Discovery and validation of a glioblastoma co-expressed gene module
    journal, January 2018

    • Dunwoodie, Leland J.; Poehlman, William L.; Ficklin, Stephen P.
    • Oncotarget, Vol. 9, Issue 13
    • DOI: 10.18632/oncotarget.24228

    LncRNA ontology: inferring lncRNA functions based on chromatin states and expression patterns
    journal, September 2015

    Modelling business and management systems using Fuzzy cognitive maps: A critical overview
    conference, November 2015

    Network Medicine in the Age of Biomedical Big Data
    journal, April 2019

    Long-Term Oil Contamination Alters the Molecular Ecological Networks of Soil Microbial Functional Genes
    journal, February 2016

    Conservation of Species- and Trait-Based Modeling Network Interactions in Extremely Acidic Microbial Community Assembly
    journal, August 2017

    Chemotherapy Alters the Phylogenetic Molecular Ecological Networks of Intestinal Microbial Communities
    journal, May 2019

    Investigating the Combinatory Effects of Biological Networks on Gene Co-expression
    journal, May 2016

    Flooding Irrigation Weakens the Molecular Ecological Network Complexity of Soil Microbes during the Process of Dryland-to-Paddy Conversion
    journal, January 2020

    • Li, Xiaoxiao; Zhang, Qi; Ma, Jing
    • International Journal of Environmental Research and Public Health, Vol. 17, Issue 2
    • DOI: 10.3390/ijerph17020561

    An improved hypergeometric probability method for identification of functionally linked proteins using phylogenetic profiles
    journal, April 2013

    Construction and comparison of gene co-expression networks shows complex plant immune responses
    journal, January 2014

    • Leal, Luis Guillermo; López, Camilo; López-Kleine, Liliana
    • PeerJ, Vol. 2
    • DOI: 10.7717/peerj.610
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