A Subspace Module Extraction Technique for Gene Expression Data

  • Priyakshi Mahanta
  • Dhruba Kr. Bhattacharyya
  • Ashish Ghosh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8251)


Construction of co-expression network and extraction of network modules have been an appealing area of bioinformatics research. In literature, most existing algorithms of gene co-expression network extract network modules where all samples are considered. In this paper, we propose a method to construct a co-expression network based on mutual information and to extract network modules defined over a subset of samples. The method was applied over several real life gene expression datasets and the results are validated in terms of p value, Q value and topological properties.


Co-expression network network modules mutual information topological property 


  1. 1.
    Qiu, P., Gentles, A.J., Plevritis, S.K.: Fast calculation of pairwise mutual information for gene regulatory network reconstruction. Comput. Methods Prog. Biomed. 94(2), 177–180 (2009)CrossRefGoogle Scholar
  2. 2.
    Moon, Y.I., Rajagopalan, B., Lall, U.: Estimation of mutual information using kernel density estimators. Phys. Rev. E 52(3) (September 1995)Google Scholar
  3. 3.
    Mahanta, P., Ahmed, H.A., Kalita, J.K., Bhattacharyya, D.K.: Discretization in gene expression data analysis: a selected survey. In: Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology, CCSEIT 2012, pp. 69–75. ACM, New York (2012)Google Scholar
  4. 4.
    Prim, R.C.: Shortest connection networks and some generalizations. Bell System Technology Journal 36, 1389–1401 (1957)CrossRefGoogle Scholar
  5. 5.
    Sharma, S., Bhattacharyya, D.K.: An effective technique for clustering incremental gene expression data. International Journal of Computer Science Issues 7 (2010)Google Scholar
  6. 6.
    Tavazoie, S., Hughes, J.D., Campbell, M.J., Cho, R.J., Church, G.M.: Systematic determination of genetic network architecture. Nature Genetics (1999)Google Scholar
  7. 7.
    Berriz, G.F., King, O.D., Bryant, B., Sander, C., Roth, F.P.: Characterizing gene sets with FuncAssociate. Bioinformatics (Oxford, England) 19, 2502–2504 (2003)CrossRefGoogle Scholar
  8. 8.
    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 (Methodological) 57, 289–300 (1995)MathSciNetzbMATHGoogle Scholar
  9. 9.
    Warde-Farley, D., Donaldson, S.L., Comes, O., Zuberi, K., Badrawi, R., Chao, P., Franz, M., Grouios, C., Kazi, F., Lopes, C.T., Maitland, A., Mostafavi, S., Montojo, J., Shao, Q., Wright, G., Bader, G.D., Morris, Q.: The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Research 38, W214–W220 (2010)Google Scholar
  10. 10.
    Glaab, E., Baudot, A., Krasnogor, N., Valencia, A.: TopoGSA: network topological gene set analysis. Bioinformatics 26(9), 1271–1272 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Priyakshi Mahanta
    • 1
  • Dhruba Kr. Bhattacharyya
    • 1
  • Ashish Ghosh
    • 2
  1. 1.Department of Comp. Sc. and Engg.Tezpur UniversityNapaamIndia
  2. 2.Machine Intelligent UnitIndian Statistical InstituteKolkataIndia

Personalised recommendations