Skip to main content
SpringerLink
Log in

Enhancing gene expression clustering analysis using tangent transformation

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Even though extensive work has been done on clustering gene expression data, none existing algorithms evaluates gene expression coherence simultaneously by both regulation direction and relative proportion. As an example, density-based algorithms group genes with similar expression levels together and may separate genes whose expression levels have a large difference in value but vary in a fixed proportion relative to one another. In order to simultaneously measure profile coherence in regulation proportion as well as regulation direction, we propose a novel tangent transformation method. Experimental results indicate that our tangent transformation method has enhanced the gene expression clustering results significantly. Our tangent transformation method can be flexibly applied for either global clustering or biclustering, in either unsupervised or supervised scenario.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Scott A, Armstrong et al (2002) MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia. Nat Genet 30:41–47

    Article  Google Scholar 

  2. Bar-Joseph Z, Farkash S, Gifford DK, Simon I, Rosenfeld R (2004) Deconvolving cell cycle expression data with complementary information. Bioinformatics 20:i23–i30

    Article  Google Scholar 

  3. Ben-Dor A, Chor B, Karp R, Yakhini Z (2003) Discovering local structure in gene expression data: the ordering-preserving submatrix problem. J Comput Biol 10(3–4):373–338

    Google Scholar 

  4. Cheng Y, Church GM (2000) Biclustering of expression data. Proceedings of the eighth international conference on intelligent systems for molecular biology

  5. Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. PNAS 95:14863–14868

    Article  Google Scholar 

  6. Liu H, Tarima S, Borders AS, Getchell TV, Getchell ML, Stromberg AJ (2005) Quadratic regression analysis for gene discovery and pattern recognition for non-cyclic short time-course microarray experiments. BMC Bioinformatics 6:106

    Article  Google Scholar 

  7. Liu J, Wang W, Yang J (2004) Gene ontology friendly biclustering of expression profiles. IEEE Computational Systems Bioinformatics Conference (CSB’04). pp 436–447

  8. Murray JI, Whitfield ML, Trinklein ND, Myers RM, Brown PO, Botstein D (2004) Diverse and specific gene expression responses to stresses in cultured human cells. Mol Biol Cell 15:2361–2374

    Article  Google Scholar 

  9. Peddada SD, Lobenhofer EK, Li L, Afshari CA, Weinberg CR, Umbach DM (2003) Gene selection and clustering for time-course and doseCresponse microarray experiments using order-restricted inference. Bioinformatics 19:834–841

    Article  Google Scholar 

  10. Prelic A, Bleuler S, Zimmermann P, Wille A, Buhlmann P, Gruissem W, Hennig L, Thiele L, Zitzler E (2006) A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics 22:1122–1129

    Article  Google Scholar 

  11. Sharan R, Shamir R (2000) CLICK: a clustering algorithm with applications to gene expression analysis, ISMB’00, 307–316

  12. Shamir R, Maron-Katz A, Tanay A, Linhart C, Steinfeld I, Sharan R, Shiloh Y, Elkon R (2005) EXPANDER: an integrative program suite for microarray data analysis. BMC Bioinform 6:232

    Google Scholar 

  13. Spellman PT, Sherlock G et al (1998) Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell 9(12):3273–3297

    Google Scholar 

  14. Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E, Lander ES, Golub TR (1999) Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. PNAS 96:2907–2912

    Article  Google Scholar 

  15. Tanay A, Sharan R, Shamir R (2002) Discovering statistically significant biclusters in gene expression data. Bioinformatics 18:S136–S144

    Article  Google Scholar 

  16. Tavazoie S, Hughes JD, Campbell MJ, Cho RJ, Church GM (1999) Systematic determination of genetic network architecture. Nat Genet 22:281–285

    Article  Google Scholar 

  17. Wu CJ, Kasif S (2005) GEMS: a web server for biclustering analysis of expression data. Nucleic Acids Res 33:W596–W599

    Google Scholar 

  18. Liang J, Song W (2011) Clustering based on Steiner points. Int J Mach Learn Cyber. doi:10.1007/s13042-011-0047-7

  19. Graaff AJ, Engelbrecht AP (2011) Clustering data in stationary environments with a local network neighborhood artificial immune system. Int J Mach Learn Cyber. doi:10.1007/s13042-011-0041-0

  20. Guo G, Chen S, Chen L (2011) Soft subspace clustering with an improved feature weight self-adjustment mechanism. Int J Mach Learn Cyber. doi:10.1007/s13042-011-0039-7

  21. Wang X, Wang YD, Wang LJ (2004) Improving fuzzy c-means clustering based on feature-weight learning. Pattern Recogn Lett 25(10):1123–1132 (16 July 2004)

    Google Scholar 

  22. Yeung DS, Wang X (2002) Improving performance of similarity-based clustering by feature weight learning. IEEE Transact Pattern Anal Mach Intell 24(4):556–561

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Science and Technology on Information System Engineering Laboratory, Nanjing, 210007, China

    Xin Xu

Authors
  1. Xin Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xin Xu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Xu, X. Enhancing gene expression clustering analysis using tangent transformation. Int. J. Mach. Learn. & Cyber. 4, 31–40 (2013). https://doi.org/10.1007/s13042-012-0069-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-012-0069-9

Keywords

Search

Navigation