Skip to main content
SpringerLink
Log in

Suitable Overlapping Set Visualization Techniques and Their Application to Visualize Biclustering Results on Gene Expression Data

  • Conference paper
  • First Online:
Database and Expert Systems Applications (DEXA 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 903))

Included in the following conference series:

Abstract

Biclustering algorithms applied in classification of genomic data have two main theoretical differences compared to traditional clustering ones. First, it provides bi-dimensionality, grouping both genes and conditions together, since a group of genes can be co-regulated for a given condition but not for others. Second, it considers group overlaps, allowing genes to contribute to more than one activity. Visualizing biclustering results is a non-trivial process due to these two characteristics. Heatmaps-based techniques are considered as a standard for visualizing clustering results. They consist on reordering rows and/or columns in order to show clusters as contiguous blocks. However, for biclustering results, this same process cannot be applied without duplicating rows and/or columns. Moreover, a variety of techniques for visualizing sets and their relations has been published in the past recent years. Some of them can be considered as an ideal solution to visualize large sets with high number of possible relations between them. In this paper, we firstly review several set-visualizing techniques that we consider most suitable to satisfy the two mentioned features of biclustering and then, we discuss how these new techniques can visualize biclustering results.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Alsallakh, B., Aigner, W., Miksch, S., Hauser, H.: Radial Sets: interactive visual analysis of large overlapping sets. IEEE Trans. Vis. Comput. Graph. 19(12), 2496–2505 (2013)

    Article  Google Scholar 

  2. Alsallakh, B., Micallef, L., Aigner, W., Hauser, H., Miksch, S., Rodgers, P.: Visualizing sets and set-typed data: state-of-the-art and future challenges. In: Eurographics Conference on Visualization (EuroVis) State of the Art Reports, pp. 1–21 (2014)

    Google Scholar 

  3. Barkow, S., Bleuler, S., Prelic, A., Zimmermann, P., Zitzler, E.: BicAT: a biclustering analysis toolbox. Bioinformatics 22(10), 1282–1283 (2006)

    Article  Google Scholar 

  4. Baron, M.E.: A note on the historical development of logic diagrams: Leibniz, Euler and Venn. Math. Gaz. 53(384), 113 (1969)

    Article  Google Scholar 

  5. Cheng, K.O., Law, N.F., Siu, W.C., Lau, T.H.: BiVisu: software tool for bicluster detection and visualization. Bioinformatics 23(17), 2342–2344 (2007)

    Article  Google Scholar 

  6. Cheng, Y., Church, G.M.: Biclustering of expression data. In: Proceedings of International Conference on Intelligent Systems for Molecular Biology, pp. 93–103 (2000)

    Google Scholar 

  7. Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. 95(25), 1–6 (1998)

    Article  Google Scholar 

  8. Gonçalves, J.P., Madeira, S.C., Oliveira, A.L.: BiGGEsTS: integrated environment for biclustering analysis of time series gene expression data. BMC Res. Notes 2, 1–11 (2009)

    Article  Google Scholar 

  9. Grothaus, G.A., Mufti, A., Murali, T.M.: Automatic layout and visualization of biclusters. Algorithms Mol. Biol. 1(1) (2006)

    Google Scholar 

  10. Heinrich, J., Seifert, R., Burch, M., Weiskopf, D.: BiCluster viewer: a visualization tool for analyzing gene expression data. In: Bebis, G., et al. (eds.) ISVC 2011. LNCS, vol. 6938, pp. 641–652. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24028-7_59

    Chapter  Google Scholar 

  11. Henry, N., Fekete, J.D., McGuffin, M.J.: NodeTrix: a hybrid visualization of social networks. IEEE Trans. Vis. Comput. Graph. 13(6), 1302–1309 (2007)

    Article  Google Scholar 

  12. Hochreiter, S., et al.: FABIA: factor analysis for bicluster acquisition. Bioinformatics 26(12), 1520–1527 (2010)

    Article  Google Scholar 

  13. Inselberg, A.: The plane with parallel coordinates. Vis. Comput. 1(2), 69–91 (1985)

    Article  MathSciNet  Google Scholar 

  14. Jin, R., Xiang, Y., Fuhry, D., Dragan, F.F.: Overlapping matrix pattern visualization: a hypergraph approach. In: Proceedings of IEEE International Conference on Data Mining, ICDM, pp. 313–322 (2008)

    Google Scholar 

  15. Kaiser, S., et al.: BiClust: BiCluster Algorithms. R package version 1.0.2 (2013)

    Google Scholar 

  16. Euler, L.: Lettres a une princesse d’Allemagne sur divers sujets de physique et de philosophie. Leonhard Euler: Free Download & Streaming: Internet Archive, vol. 1, 1 edn (1772)

    Google Scholar 

  17. Lex, A., Gehlenborg, N., Strobelt, H., Vuillemot, R., Pfister, H.: UpSet: visualization of intersecting sets. IEEE Trans. Vis. Comput. Graph. 20(12), 1983–1992 (2014)

    Article  Google Scholar 

  18. Madeira, S.C., Oliveira, A.L.: Biclustering algorithms for biological data analysis: a survey. IEEE Trans. Comput. Biol. Bioinforma. 1(1), 24–45 (2004)

    Article  Google Scholar 

  19. Padilha, V.A., Campello, R.J.G.B.: A systematic comparative evaluation of biclustering techniques. BMC Bioinform. 18, 55 (2017)

    Article  Google Scholar 

  20. Pontes, B., Giráldez, R., Aguilar-Ruiz, J.S.: Biclustering on expression data: a review. J. Biomed. Inform. 57, 163–180 (2015)

    Article  Google Scholar 

  21. Prelić, A., et al.: A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics 22(9), 1122–1129 (2006)

    Article  Google Scholar 

  22. Reid, R.J.D., et al.: Selective ploidy ablation, a high-throughput plasmid transfer protocol, identifies new genes affecting topoisomerase I-induced DNA damage. Genome. Res. 21(3), 477–486 (2011)

    Article  Google Scholar 

  23. Riche, N.H., Dwyer, T.: Untangling Euler diagrams. IEEE Trans. Vis. Comput. Graph. 16(6), 1090–1099 (2010)

    Article  Google Scholar 

  24. Rodgers, P.: A survey of Euler diagrams. J. Vis. Lang. Comput. 25(3), 134–155 (2014)

    Article  Google Scholar 

  25. Sadana, R., Major, T., Dove, A., Stasko, J.: OnSet: A visualization technique for large-scale binary set data. IEEE Trans. Vis. Comput. Graph. 20(12), 1993–2002 (2014)

    Article  Google Scholar 

  26. Santamaría, R., Therón, R., Quintales, L.: BicOverlapper 2.0: visual analysis for gene expression. Bioinformatics 30(12), 1785–1786 (2014)

    Article  Google Scholar 

  27. Shamir, R., et al.: EXPANDER - an integrative program suite for microarray data analysis. BMC Bioinform. 6, 1–12 (2005)

    Article  Google Scholar 

  28. Streit, M., Gratzl, S., Gillhofer, M., Mayr, A., Mitterecker, A., Hochreiter, S.: Furby: fuzzy force-directed bicluster visualization. BMC Bioinform. 15(6), S4 (2014)

    Article  Google Scholar 

  29. Tu, B.P., Kudlicki, A., Rowicka, M., McKnight, S.L.: Logic of the yeast metabolic cycle: temporal compartmentalization of cellular processes. Science 310(5751), 1152–1158 (2005)

    Article  Google Scholar 

  30. Wilkinson, L., Friendly, M.: The history of the cluster heat map. Am. Stat. 63(2), 179–184 (2009)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory of Technologies of Information and Communication and Electrical Engineering (LaTICE), National High School of Engineers of Tunis (ENSIT), University of Tunis, Tunis, Tunisia

    Haithem Aouabed & Mourad Elloumi

  2. Departamento de Informática y Automática, Universidad de Salamanca, Salamanca, Spain

    Rodrigo Santamaría

Authors
  1. Haithem Aouabed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rodrigo Santamaría
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mourad Elloumi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mourad Elloumi .

Editor information

Editors and Affiliations

  1. University of Tunis, Tunis, Tunisia

    Mourad Elloumi

  2. MiCS, Media Computer Science, University of Passau, Passau, Bayern, Germany

    Michael Granitzer

  3. IRIT, Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  4. University of Twente, Enschede, Overijssel, The Netherlands

    Christin Seifert

  5. Fak. Medien, Bauhaus Universität Weimar, Weimar, Thüringen, Germany

    Benno Stein

  6. Inst. für Softwaretechnik, Vienna University of Technology, Vienna, Austria

    A Min Tjoa

  7. FAW, Johannes Kepler University of Linz, Linz, Austria

    Roland Wagner

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Aouabed, H., Santamaría, R., Elloumi, M. (2018). Suitable Overlapping Set Visualization Techniques and Their Application to Visualize Biclustering Results on Gene Expression Data. In: Elloumi, M., et al. Database and Expert Systems Applications. DEXA 2018. Communications in Computer and Information Science, vol 903. Springer, Cham. https://doi.org/10.1007/978-3-319-99133-7_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-99133-7_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99132-0

  • Online ISBN: 978-3-319-99133-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation