Biweight Midcorrelation-Based Gene Differential Coexpression Analysis and Its Application to Type II Diabetes

  • Lin Yuan
  • Wen Sha
  • Zhan-Li Sun
  • Chun-Hou Zheng
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 375)


Differential coexpression analysis usually requires the definition of ‘distance’ or ‘similarity’ between measured datasets, the most common choices being Pearson correlation. However, Pearson correlation is sensitive to outliers. Biweight midcorrelation is considered to be a good alternative to Pearson correlation since it is more robust to outliers. In this paper, we introduce to use Biweight Midcorrelation to measure ‘similarity’ between gene expression profiles, and provide a new approach for gene differential coexpression analysis. The results show that the new approach performed better than three previously published differential coexpression analysis (DCEA) methods. We applied the new approach to a public available type 2 diabetes (T2D) expression dataset, and many additional discoveries can be found through our method.


gene differential coexpression analysis biweight midcorrelation half-thresholding 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Allison, D.B., Cui, X.Q., Page, G.P., Sabripour, M.: Microarray Data Analysis: from Disarray to Consolidation and Consensus. Nature Reviews Genetics 7, 55–65 (2006)CrossRefGoogle Scholar
  2. 2.
    Baldi, P., Long, A.D.: A Bayesian Framework for The Analysis of Microarray Expression Data: Regularized t-test and Statistical Inferences of Gene Changes. Bioinformatics 17(6), 509–519 (2001)CrossRefGoogle Scholar
  3. 3.
    Brown, M.P.S., Grundy, W.N., Lin, D., Cristianini, N., Sugnet, C.W., Furey, T.S., Ares Jr., M., Haussler, D.: Knowledge-based Analysis of Microarray Gene Expression Data by Using Support Vector Machines. Proc. Natl. Acad Sci. USA 97(1), 262–267 (2000)CrossRefGoogle Scholar
  4. 4.
    Sturn, A., Quackenbush, J., Trajanoski, Z.: Genesis: Cluster Analysis of Microarray Data. Bioinformatics 18(1), 207–208 (2002)CrossRefGoogle Scholar
  5. 5.
    Choi, J.K., Yu, U., Yoo, O.J., Kim, S.: Differential Coexpression Analysis Using Microarray Data and Its Application to Human Cancer. Bioinformatics 21(24), 4348–4355 (2005)CrossRefGoogle Scholar
  6. 6.
    Rachlin, J., Cohen, D.D., Cantor, C., Kasif, S.: Biological Context Networks: A mosaic View of The Interactome. Mol. Syst. Biol. 2, 66 (2006)CrossRefGoogle Scholar
  7. 7.
    Reverter, A., Ingham, A., Lehnert, S.A., Tan, S.H., Wang, Y., Ratnakumar, A., Dalrymple, B.P.: Simultaneous Identification of Differential Gene Expression and Connectivity in Inflammation, Adipogenesis and Cancer. Bioinformatics 22(19), 239–2404 (2006)CrossRefGoogle Scholar
  8. 8.
    Carter, S.L., Brechbuhler, C.M., Griffin, M., Bond, A.T.: Gene Co-expression Network Topology Provides A Framework for Molecular Characterization of Eellular State. Bioinformatics 20(14), 2242–2250 (2004)CrossRefGoogle Scholar
  9. 9.
    Mason, M.J., Fan, G., Plath, K., Zhou, Q., Horvath, S.: Signed Weighted Gene Co-expression Network Analysis of Transcriptional Regulation in Uurine Embryonic Stem Cells. BMC Genomics 10, 327 (2009)CrossRefGoogle Scholar
  10. 10.
    Fuller, T.F., Ghazalpour, A., Aten, J.E., Drake, T.A., Lusis, A.J., Horvath, S.: Weighted Gene Coexpression Network Analysis Strategies Applied to Mouse Weight. Mammalian Genome 18(6-7), 463–472 (2007)CrossRefGoogle Scholar
  11. 11.
    Freudenberg, J.M., Sivaganesan, S., Wagner, M., Medvedovic, M.: A Semi-parametric Bayesian Model for Unsupervised Differential Coexpression Analysis. BMC Bioinformatics 11, 234 (2010)CrossRefGoogle Scholar
  12. 12.
    Graeber, T.G., Eisenberg, D.: Bioinformatic Identification for Potential Autocrine Signaling Loops in Cancers from Gene Expression Profiles. Nat. Genet. 29, 295–300 (2001)CrossRefGoogle Scholar
  13. 13.
    Yu, H., Liu, B.H., Li, Y.Y.: Link-based Quantitative Methods to Identify Differentially Coexpressed Genes and Gene Pairs. BMC Bioinformatics 12, 315 (2011)CrossRefGoogle Scholar
  14. 14.
    Wilcox, R.: Introduction to Robust Estimation and Hypothesis Testing. Academic Press, San Diego (1997)zbMATHGoogle Scholar
  15. 15.
    Zhang, B., Li, H., Riggins, R.B., Zhan, M., Xuan, J., Zhang, Z., Hoffman, E.P., Clarke, R., Wang, Y.: Differential Dependency Network Analysis to Identify Condition Specific Topological Changes in Biological Networks. Bioinformatics 25(4), 526–532 (2009)CrossRefGoogle Scholar
  16. 16.
    Bulcke, V.T., Leemput, V.K., Naudts, B., Remortel, P., Ma, H., Verschoren, A., Moor, D.B., Marchal, K.: SynTReN: A Generator of Synthetic Gene Expression Data for Design and Analysis of Structure Learning Algorithms. BMC Bioinformatics 7, 43 (2006)CrossRefGoogle Scholar
  17. 17.
    Benjamini, Y., Hochberg, Y.: Controlling The False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B 57, 289–300 (1995)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Scott, et al.: A Genome-wide Association Study of Type 2 Diabetes in Finns Detects Multiple Susceptibility Variants. Science 316(5829), 1341–1345 (2007)CrossRefGoogle Scholar
  19. 19.
    Zeggini, et al.: Meta-analysis of Genome-wide Association Data and Large-scale Replication Identifies Additional Susceptibility Loci for Type 2 Diabetes. Nature Genetics 40, 638–645 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Lin Yuan
    • 1
    • 2
  • Wen Sha
    • 2
  • Zhan-Li Sun
    • 2
  • Chun-Hou Zheng
    • 1
  1. 1.College of Information and Communication TechnologyQufu Normal UniversityRizhaoChina
  2. 2.College of Electrical Engineering and AutomationAnhui UniversityHefeiChina

Personalised recommendations