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Polish Information Processing Society
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Annals of Computer Science and Information Systems, Volume 8

Proceedings of the 2016 Federated Conference on Computer Science and Information Systems

QtBiVis: a software toolbox for visual analysis of biclustering experiment


DOI: http://dx.doi.org/10.15439/2016F129

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 13751378 ()

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Abstract. In this article we introduce QtBiVis - a novel software intended for the comparative analysis of biclustering results. This modular tool has been efficiently implemented in C++ with Qt framework GUI. It may be successfully used for coverage analysis of the results of biclustering as well filtering or sorting biclusters by Gene Ontology (GO) identifiers or bicluster enrichment values. It may also be useful for parameter studies of biclustering algorithms. In future releases we plan to add different modules for visualizing and comparing different GO terms and biclusters.


  1. M. Schena, D. Shalon, R. W. Davis, and P. O. Brown, “Quantitative monitoring of gene expression patterns with a complementary dna microarray,” Science, vol. 270, no. 5235, pp. 467–470, 1995.
  2. S. C. Madeira and A. L. Oliveira, “Biclustering algorithms for biological data analysis: a survey,” Computational Biology and Bioinformatics, IEEE/ACM Transactions on, vol. 1, no. 1, pp. 24–45, 2004.
  3. A. Oghabian, S. Kilpinen, S. Hautaniemi, and E. Czeizler, “Biclustering methods: biological relevance and application in gene expression analysis,” PloS one, vol. 9, no. 3, p. e90801, 2014.
  4. B. Pontes, R. Giráldez, and J. S. Aguilar-Ruiz, “Biclustering on expression data: A review,” Journal of biomedical informatics, vol. 57, pp. 163–180, 2015.
  5. P. Orzechowski, “Proximity measures and results validation in biclustering—A survey,” in Artificial Intelligence and Soft Computing (L. Rutkowski, M. Korytkowski, R. Scherer, R. Tadeusiewicz, L. A. Zadeh, and J. M. Zurada, eds.), vol. 7895 of Lecture Notes in Computer Science, pp. 206–217, Springer Berlin Heidelberg, 2013.
  6. B. Pontes, R. Girldez, and J. S. Aguilar-Ruiz, “Quality measures for gene expression biclusters,” PloS one, vol. 10, no. 3, p. e0115497, 2015.
  7. K. Eren, M. Deveci, O. Küçüktunç, and Ü. Çatalyürek, “A comparative analysis of biclustering algorithms for gene expression data,” Briefings in Bioinformatics, 2012.
  8. G. A. Grothaus, A. Mufti, and T. Murali, “Automatic layout and visualization of biclusters,” Algorithms for Molecular Biology, vol. 1, no. 1, p. 15, 2006.
  9. K.-O. Cheng, N.-F. Law, W.-C. Siu, and T. Lau, “Bivisu: software tool for bicluster detection and visualization,” Bioinformatics, vol. 23, no. 17, pp. 2342–2344, 2007.
  10. S. Barkow, S. Bleuler, A. Prelić, P. Zimmermann, and E. Zitzler, “Bicat: a biclustering analysis toolbox,” Bioinformatics, vol. 22, no. 10, pp. 1282–1283, 2006.
  11. F. M. Al-Akwaa, M. H. Ali, and V. M. Kadah, “Bicat_plus: An automatic comparative tool for bi/clustering of gene expression data obtained using microarrays,” in Radio Science Conference, 2009. NRSC 2009. National, pp. 1–8, IEEE, 2009.
  12. R. Santamaría, R. Therón, and L. Quintales, “Bicoverlapper: a tool for bicluster visualization,” Bioinformatics, vol. 24, no. 9, pp. 1212–1213, 2008.
  13. R. Santamaría, R. Therón, and L. Quintales, “Bicoverlapper 2.0: visual analysis for gene expression,” Bioinformatics, p. btu120, 2014.
  14. M. Streit, S. Gratzl, M. Gillhofer, A. Mayr, A. Mitterecker, and S. Hochreiter, “Furby: fuzzy force-directed bicluster visualization,” BMC bioinformatics, vol. 15, no. Suppl 6, p. S4, 2014.
  15. J. Heinrich, R. Seifert, M. Burch, and D. Weiskopf, “Bicluster viewer: a visualization tool for analyzing gene expression data,” in Advances in Visual Computing, pp. 641–652, Springer, 2011.
  16. J. P. Gonçalves, S. C. Madeira, and A. L. Oliveira, “Biggests: integrated environment for biclustering analysis of time series gene expression data,” BMC research notes, vol. 2, no. 1, p. 124, 2009.
  17. Y. Benjamini and Y. Hochberg, “Controlling the false discovery rate: a practical and powerful approach to multiple testing,” Journal of the Royal Statistical Society. Series B (Methodological), pp. 289–300, 1995.