X-Module: A novel fusion measure to associate co-expressed gene modules from condition-specific expression profiles

Abstract

A gene co-expression network (CEN) is of biological interest, since co-expressed genes share common functions and biological processes or pathways. Finding relationships among modules can reveal inter-modular preservation, and similarity in transcriptome, functional, and biological behaviors among modules of the same or two different datasets. There is no method which explores the one-to-one relationships and one-to-many relationships among modules extracted from control and disease samples based on both topological and semantic similarity using both microarray and RNA seq data. In this work, we propose a novel fusion measure to detect mapping between modules from two sets of co-expressed modules extracted from control and disease stages of Alzheimer’s disease (AD) and Parkinson’s disease (PD) datasets. Our measure considers both topological and biological information of a module and is an estimation of four parameters, namely, semantic similarity, eigengene correlation, degree difference, and the number of common genes. We analyze the consensus modules shared between both control and disease stages in terms of their association with diseases. We also validate the close associations between human and chimpanzee modules and compare with the state-of-the-art method. Additionally, we propose two novel observations on the relationships between modules for further analysis.

This is a preview of subscription content, access via your institution.

Figure 1
Figure 2
Figure 3

References

  1. Alter O, Brown PO and Botstein D 2003 Generalized singular value decomposition for comparative analysis of genome-scale expression data sets of two different organisms. Proc. Nat. Acad. Sci. 100 3351–3356

    CAS  Article  Google Scholar 

  2. De Ferrari GV and Inestrosa NC 2000 Wnt signaling function in Alzheimer’s disease. Brain Res. Rev. 33 1–12

    Article  Google Scholar 

  3. Eisen MB, Spellman PT, Brown PO and Botstein D 1998 Cluster analysis and display of genome-wide expression patterns. Proc. Nat. Acad. Sci. 95 14863–14868

    CAS  Article  Google Scholar 

  4. Guo X, Liu R, Shriver CD, Hu H and Liebman MN 2006 Assessing semantic similarity measures for the characterization of human regulatory pathways. Bioinformatics 22 967–973

    CAS  Article  Google Scholar 

  5. Ha MJ, Baladandayuthapani V and Do K-A. 2015 Dingo: differential network analysis in genomics. Bioinformatics 31 3413–3420

    CAS  Article  Google Scholar 

  6. Heyer LJ, Kruglyak S and Yooseph S 1999 Exploring expression data: identification and analysis of coexpressed genes. Genome Res. 9 1106–1115

    CAS  Article  Google Scholar 

  7. Jiang JJ and Conrath DW 1997 Semantic similarity based on corpus statistics and lexical taxonomy. arXiv preprint cmp-lg/9709008

  8. Kakati T, Kashyap H and Bhattacharyya DK 2016 Thd-module extractor: an application for cen module extraction and interesting gene identification for Alzheimer’s disease. Sci. Rep. 6 38046

    CAS  Article  Google Scholar 

  9. Khaitovich P, Muetzel B, She X, Lachmann M, Hellmann I, Dietzsch J, Steigele S, Do H-H, Weiss G, Enard W, et al. 2004 Regional patterns of gene expression in human and chimpanzee brains. Genome Res. 14 1462–1473

    CAS  Article  Google Scholar 

  10. Langfelder P and Horvath S 2007 Eigengene networks for studying the relationships between co-expression modules. BMC Syst. Biol. 1 54

    Article  Google Scholar 

  11. Langfelder P and Horvath S 2008 WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9 559

    Article  Google Scholar 

  12. Langfelder P, Luo R, Oldham MC and Horvath S 2011 Is my network module preserved and reproducible? PLoS Comput. Biol. 7 e1001057

    CAS  Article  Google Scholar 

  13. Leal LG, López C and López-Kleine L 2014 Construction and comparison of gene co-expression networks shows complex plant immune responses. PeerJ 2 e610

    Article  Google Scholar 

  14. Lin D et al. 1998 An information-theoretic definition of similarity. ICML 98 296–304

    Google Scholar 

  15. Lobo I 2008 Pleiotropy: one gene can affect multiple traits. Nat. Edu. 1 10

    Google Scholar 

  16. Mahanta P, Ahmed HA, Bhattacharyya DK and Ghosh A 2014 Fumet: a fuzzy network module extraction technique for gene expression data. J. Biosci. 39 351–364

    CAS  Article  Google Scholar 

  17. Rahmatallah Y, Emmert-Streib F and Glazko G 2013 Gene sets net correlations analysis (GSNCA): a multi-variate differential coexpression test for gene sets. Bioinformatics 30 360–368

    Article  Google Scholar 

  18. Ray S and Bandyopadhyay S 2016 Discovering condition specific topological pattern changes in coexpression network: an application to HIV-1 progression. IEEE/ACM Transact. Comput. Biol. Bioinform. 13 1086–1099

    Article  Google Scholar 

  19. Resnik P et al. 1999 Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language. J. Artif. Intell. Res. 11 95–130

    Article  Google Scholar 

  20. Ruan J, Dean AK and Zhang W 2010 A general co-expression network-based approach to gene expression analysis: comparison and applications. BMC Syst. Biol. 4 8

    Article  Google Scholar 

  21. Tan N, Chung MK, Smith JD, Hsu J, Serre D, Newton DW, Castel L, Soltesz E, Pettersson G, Gillinov AM, et al. 2013 A weighted gene co-expression network analysis of human left atrial tissue identifies gene modules associated with atrial fibrillation. Circ. Genomic Precision Med. 6 113

    Google Scholar 

  22. Tesson BM, Breitling R and Jansen RC 2010 Diffcoex: a simple and sensitive method to find differentially coexpressed gene modules. BMC Bioinform. 11 497

    Article  Google Scholar 

  23. Thinakaran G 1999 The role of presenilins in Alzheimer’s disease. J. Clin. Invest. 104 1321–1327

    CAS  Article  Google Scholar 

  24. Wang Q and Chen G 2017 Fuzzy soft subspace clustering method for gene co-expression network analysis. Int. J. Machine Learn. Cybernetics 8 1157–1165

    Article  Google Scholar 

  25. Watson M 2006 Coxpress: differential co-expression in gene expression data. BMC Bioinformatics 7 509

    Article  Google Scholar 

  26. Xu X, Lu Y, Tung A and Wang W 2006 Mining shifting-and-scaling co-regulation patterns on gene expression profiles. Proceedings of the 22nd International Conference on Data Engineering ICDE’06 pp 89–89 (IEEE)

  27. Zhang B, Li H, Riggins RB, Zhan M, Xuan, J., Zhang, Z., Hoffman EP, Clarke R and Wang Y 2008 Differential dependency network analysis to identify condition-specific topological changes in biological networks. Bioinformatics 25 526–532

    Article  Google Scholar 

  28. Zhao L and Zaki MJ 2005 Tricluster: an effective algorithm for mining coherent clusters in 3d microarray data. Proceedings of the 2005 ACM SIGMOD international conference on Management of Data pp 694–705 (ACM)

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Dhruba K Bhattacharyya.

Additional information

Corresponding editor: Stuart A Newman

Communicated by Stuart A. Newman.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 915 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kakati, T., Bhattacharyya, D.K. & Kalita, J.K. X-Module: A novel fusion measure to associate co-expressed gene modules from condition-specific expression profiles. J Biosci 45, 33 (2020). https://doi.org/10.1007/s12038-020-0007-z

Download citation

Keywords

  • Module association
  • biomarkers
  • Parkinson’s disease
  • Alzheimer’s disease
  • co-expression network