Analysis of Gene Coexpression by B-Spline Based CoD Estimation

1687-4153-2007-49478 1687-4153 Research Article Analysis of Gene Coexpression by B-Spline Based CoD Estimation LiHuaihuaili@mail.nih.gov SunYusunyu@mail.nih.gov ZhanMingzhanmi@mail.nih.gov

Bioinformatics Unit, Branch of Research Resources, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA

EURASIP Journal on Bioinformatics and Systems Biology 1687-4153 2007 2007 1 49478 http://bsb.eurasipjournals.com/content/2007/1/49478 10.1155/2007/49478 31720063120076120071332007 2007Huai Li et al.This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The gene coexpression study has emerged as a novel holistic approach for microarray data analysis. Different indices have been used in exploring coexpression relationship, but each is associated with certain pitfalls. The Pearson's correlation coefficient, for example, is not capable of uncovering nonlinear pattern and directionality of coexpression. Mutual information can detect nonlinearity but fails to show directionality. The coefficient of determination (CoD) is unique in exploring different patterns of gene coexpression, but so far only applied to discrete data and the conversion of continuous microarray data to the discrete format could lead to information loss. Here, we proposed an effective algorithm, CoexPro, for gene coexpression analysis. The new algorithm is based on B-spline approximation of coexpression between a pair of genes, followed by CoD estimation. The algorithm was justified by simulation studies and by functional semantic similarity analysis. The proposed algorithm is capable of uncovering both linear and a specific class of nonlinear relationships from continuous microarray data. It can also provide suggestions for possible directionality of coexpression to the researchers. The new algorithm presents a novel model for gene coexpression and will be a valuable tool for a variety of gene expression and network studies. The application of the algorithm was demonstrated by an analysis on ligand-receptor coexpression in cancerous and noncancerous cells. The software implementing the algorithm is available upon request to the authors.

Genetic Regulatory NetworksAhmed Tewfik, Edward R Dougherty, Paul Dan Cristea and Tatsuya Akutsu

[1234567891011121314151617181920212223242526272829]

A gene-coexpression network for global discovery of conserved genetic modulesStuartJMSegalEKollerDKimSKScience2003302564324925510.1126/science.108744712934013Coexpresion analysis of human genes across many microarray data setsLeeHKHsuAKSajdakJQinJPavlidisPGenome Research20041461085109410.1101/gr.191090441978715173114The yeast coexpression network has a small-world, scale-free architecture and can be explained by a simple modelvan NoortVSnelBHuynenMAEMBO Reports20045328028410.1038/sj.embor.7400090129900214968131Gene co-expression network topology provides a framework for molecular characterization of cellular stateCarterSLBrechbühlerCMGriffinMBondATBioinformatics200420142242225010.1093/bioinformatics/bth23415130938Bioinformatic identification of potential autocrine signaling loops in cancers from gene expression profilesGraeberTGEisenbergDNature Genetics200129329530010.1038/ng75511685206Reconciling gene expression data with known genome-scale regulatory network structuresHerrgårdMJCovertMWPalssonBØGenome Research200313112423243410.1101/gr.133000340376114559784Estimation of genetic networks and functional structures between genes by using Bayesian networks and nonparametric regressionImotoSGotoTMiyanoSPacific Symposium on Biocomputing2002175186Mutual information relevance networks: functional genomic clustering using pairwise entropy measurementsButteAJKohaneISPacific Symposium on Biocomputing2000418429Construction of genomic networks using mutual-information clustering and reversible-jump Markov-chain-Monte-Carlo predictor designZhouXWangXDoughertyERSignal Processing200383474576110.1016/S0165-1684(02)00469-3Can Markov chain models mimic biological regulation?KimSLiHDoughertyERJournal of Biological Systems200210433735710.1142/S0218339002000676Growing genetic regulatory networks from seed genesHashimotoRFKimSShmulevichIZhangWBittnerMLDoughertyERBioinformatics20042081241124710.1093/bioinformatics/bth07414871865Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networksShmulevichIDoughertyERKimSZhangWBioinformatics200218226127410.1093/bioinformatics/18.2.26111847074Coefficient of determination in nonlinear signal processingDoughertyERKimSChenYSignal Processing200080102219223510.1016/S0165-1684(00)00079-7Systematic intervention of transcription for identifying network response to disease and cellular phenotypesLiHZhanMBioinformatics20062219610210.1093/bioinformatics/bti75216278241Dynamical analysis of gene networks requires both mRNA and protein expression informationHatzimanikatisVLeeKHMetabolic Engineering19991427528110.1006/mben.1999.011510937820PrautzschHBoehmWPalusznyMBézier and B-Spline TechniquesSpringer, Berlin, Germany200221863587A data-driven clustering method for time course gene expression dataMaPCastillo-DavisCIZhongWLiuJSNucleic Acids Research20063441261126910.1093/nar/gkl013138809716510852Significance analysis of time course microarray experimentsStoreyJDXiaoWLeekJTTompkinsRGDavisRWProceedings of the National Academy of Sciences of the United States of America200510236128371284210.1073/pnas.0504609102120169716141318Continuous representations of time-series gene expression dataBar-JosephZGerberGKGiffordDKJaakkolaTSSimonIJournal of Computational Biology2003103-434135610.1089/1066527036068805712935332SPLINDID: a semi-parametric, model-based method for obtaining transcription rates and gene regulation parameters from genomic and proteomic expression profilesBhasiKForrestARamanathanMBioinformatics200521203873387910.1093/bioinformatics/bti624260748216096347A spline function approach for detecting differentially expressed genes in microarray data analysisHeWBioinformatics200420172954296310.1093/bioinformatics/bth33915180936Clustering of time-course gene expression data using a mixed-effects model with B-splinesLuanYLiHBioinformatics200319447448210.1093/bioinformatics/btg01412611802Estimating mutual information using B-spline functions—an improved similarity measure for analysing gene expression dataDaubCOSteuerRSelbigJKloskaSBMC Bioinformatics20045111810.1186/1471-2105-5-11851680015339346Summaries of Affymetrix GeneChip probe level dataIrizarryRABolstadBMCollinFCopeLMHobbsBSpeedTPNucleic Acids Research2003314e1510.1093/nar/gng01515024712582260Investigating semantic similarity measures across the gene ontology: the relationship between sequence and annotationLordPWStevensRDBrassAGobleCABioinformatics200319101275128310.1093/bioinformatics/btg15312835272Bone morphogenetic protein signaling in prostate cancer cell linesBrubakerKDCoreyEBrownLGVessellaRLJournal of Cellular Biochemistry200491115116010.1002/jcb.1067914689587Diverse biological effect and Smad signaling of bone morphogenetic protein 7 in prostate tumor cellsYangSZhongCFrenkelBReddiAHRoy-BurmanPCancer Research200565135769577710.1158/0008-5472.CAN-05-028915994952Involvement of chemokine receptors in breast cancer metastasisMüllerAHomeyBSotoHNature20014106824505610.1038/3506501611242036Chemokines and their role in tumor growth and metastasisWangJMDengXGongWSuSJournal of Immunological Methods19982201-211710.1016/S0022-1759(98)00128-89839921