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.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
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)
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)
Barkow, S., Bleuler, S., Prelic, A., Zimmermann, P., Zitzler, E.: BicAT: a biclustering analysis toolbox. Bioinformatics 22(10), 1282–1283 (2006)
Baron, M.E.: A note on the historical development of logic diagrams: Leibniz, Euler and Venn. Math. Gaz. 53(384), 113 (1969)
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)
Cheng, Y., Church, G.M.: Biclustering of expression data. In: Proceedings of International Conference on Intelligent Systems for Molecular Biology, pp. 93–103 (2000)
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)
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)
Grothaus, G.A., Mufti, A., Murali, T.M.: Automatic layout and visualization of biclusters. Algorithms Mol. Biol. 1(1) (2006)
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
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)
Hochreiter, S., et al.: FABIA: factor analysis for bicluster acquisition. Bioinformatics 26(12), 1520–1527 (2010)
Inselberg, A.: The plane with parallel coordinates. Vis. Comput. 1(2), 69–91 (1985)
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)
Kaiser, S., et al.: BiClust: BiCluster Algorithms. R package version 1.0.2 (2013)
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)
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)
Madeira, S.C., Oliveira, A.L.: Biclustering algorithms for biological data analysis: a survey. IEEE Trans. Comput. Biol. Bioinforma. 1(1), 24–45 (2004)
Padilha, V.A., Campello, R.J.G.B.: A systematic comparative evaluation of biclustering techniques. BMC Bioinform. 18, 55 (2017)
Pontes, B., Giráldez, R., Aguilar-Ruiz, J.S.: Biclustering on expression data: a review. J. Biomed. Inform. 57, 163–180 (2015)
Prelić, A., et al.: A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics 22(9), 1122–1129 (2006)
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)
Riche, N.H., Dwyer, T.: Untangling Euler diagrams. IEEE Trans. Vis. Comput. Graph. 16(6), 1090–1099 (2010)
Rodgers, P.: A survey of Euler diagrams. J. Vis. Lang. Comput. 25(3), 134–155 (2014)
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)
Santamaría, R., Therón, R., Quintales, L.: BicOverlapper 2.0: visual analysis for gene expression. Bioinformatics 30(12), 1785–1786 (2014)
Shamir, R., et al.: EXPANDER - an integrative program suite for microarray data analysis. BMC Bioinform. 6, 1–12 (2005)
Streit, M., Gratzl, S., Gillhofer, M., Mayr, A., Mitterecker, A., Hochreiter, S.: Furby: fuzzy force-directed bicluster visualization. BMC Bioinform. 15(6), S4 (2014)
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)
Wilkinson, L., Friendly, M.: The history of the cluster heat map. Am. Stat. 63(2), 179–184 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
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)