ABSTRACT
We provide a model to integrate the visualization of biclusters extracted from gene expresion data and the underlying PPI networks. Such an integration conveys the biologically relevant interconnection between these two structures inferred from biological experiments. We model the reliabilities of the structures using directed graphs with vertex and edge weights. The resulting graphs are drawn using appropriate weighted modifications of the algorithms necessary for the layered drawings of directed graphs. We provide applications of the proposed visualization model on the S. cerevisiae dataset.
- Mips. http://mips.gsf.de/genre/proj/mpact/yeast.Google Scholar
- S. Barkow, S. Bleuler, A. Prelic, P. Zimmermann, and E. Zitzler. Bicat: a biclustering analysis toolbox. Bioinformatics, 22(10):1282--1283, 2006. Google ScholarDigital Library
- O. Bastert and C. Matuszewski. Layered drawings of digraphs. pages 87--120, 2001. Google ScholarDigital Library
- U. Brandes and B. Köpf. Fast and simple horizontal coordinate assignment. In Proc. of 9th International Symposium on Graph Drawing, pages 31--44, London, UK, 2002. Springer-Verlag. Google ScholarDigital Library
- B.-J. Breitkreutz, C. Stark, and M. Tyers. Osprey: A network visualization system. Genome Biology, 3(12):1--6, 2002.Google Scholar
- O. A. Çakiroglu, C. Erten, Ö. Karatas, and M. Sözdinler. Crossing minimization in weighted bipartite graphs. Journal of Discrete Algorithms, doi:10.1016/j.jda.2008.08.003, 2008. Google ScholarDigital Library
- Y. Cheng and G. Church. Biclustering of expression data. In Proc. of the 8th Int. Conf. on Intelligent Systems for Molecular Biology, pages 93--103, 2000. Google ScholarDigital Library
- E. Coffman and R. L. Graham. Optimal scheduling for two-processor systems. Acta Informatica, 1:200--213, 1972.Google ScholarDigital Library
- Consortium. Gene ontology: tool for the unification of biology. the gene ontology consortium. Nature Genetics, 25(1):25--29, 2000.Google ScholarCross Ref
- C. Demetrescu and I. Finocchi. Combinatorial algorithms for feedback problems in directed graphs. Inf. Process. Lett., 86(3):129--136, 2003. Google ScholarDigital Library
- T. M. Ebbels, B. F. Buxton, and D. T. Jones. Springscape: visualisation of microarray and contextual bioinformatic data using spring embedding and an Śinformation landscapeŠ. Bioinformatics, 22(14):e99--e107, 2006. Google ScholarDigital Library
- C. Erten and M. Sözdinler. Biclustering expression data based on expanding localized substructures. In Proceedings of BICoB 2009, pages 224--235, 2009. Google ScholarDigital Library
- E. Gansner, E. Koutsofios, S. North, and K. Vo. A technique for drawing directed graphs. IEEE Trans. Softw. Eng., 19(3):214--230, 1993. Google ScholarDigital Library
- 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, 2(124), 2009.Google Scholar
- J. Hartigan. Direct clustering of a data matrix. Journal of the American Statistical Association, 67(337):123--129, 1972.Google ScholarCross Ref
- F. Iragne, M. Nikolski, B. Mathieu, D. Auber, and D. Sherman. Proviz: protein interaction visualization and exploration. Bioinformatics, 21(2):272--274, 2005. Google ScholarDigital Library
- R. Jansen, D. Greenbaum, and M. Gerstein. Relating whole-genome expression data with protein-protein interactions. Genome Research, 12(1):37--46, 2002.Google ScholarCross Ref
- R. M. Karp. Reducibility among combinatorial problems. pages 85--103, 1972.Google Scholar
- X. Lin, M. Liu, and W. Chen. Assessing reliability of protein-protein interactions by integrative analysis of data in model organisms. BMC Bioinformatics, 10:S5, 2009.Google ScholarCross Ref
- S. Madeira and A. Oliveira. Biclustering algorithms for biological data analysis: A survey. IEEE/ACM Trans. on Comp. Biol. and Bioinformatics, 1(1):24--45, 2004. Google ScholarDigital Library
- N. Nikolov, A. Tarassov, and J. Branke. In search for efficient heuristics for minimum-width graph layering with consideration of dummy nodes. Journal Experimental Algorithmics, 10:2.7, 2005. Google ScholarDigital Library
- A. Prelic, S. Bleuler, P. Zimmermann, A. Wille, P. Buhlmann, W. Gruissem, L. Hennig, L. Thiele, and E. Zitzler. A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics, 22:1122--1129, 2006. Google ScholarDigital Library
- R. Santamaría, R. Therón, and L. Quintales. Bicoverlapper. Bioinformatics, 24(9):1212--1213, 2008. Google ScholarDigital Library
- P. Shannon, A. Markiel, O. Ozier, N. S. Baliga, J. T. Wang, D. Ramage, N. Amin, B. Schwikowski, and T. Ideker. Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Research, 13(11):2498--2504, 2003.Google ScholarCross Ref
- R. Sharan, A. Maron-katz, and R. Shamir. Click and expander: A system for clustering and visualizing gene expression data. Bioinformatics, 19:1787--1799, 2003.Google ScholarCross Ref
- K. Sugiyama, S. Tagawa, and M. Toda. Methods for visual understanding of hierarchical system structures. Systems, Man and Cybernetics, IEEE Transactions on, 11(2):109--125, Feb. 1981.Google Scholar
- S. Suthram, T. Shlomi, E. Ruppin, R. Sharan, and T. Ideker. A direct comparison of protein interaction confidence assignment schemes. BMC Bioinformatics, 7(1):360, 2006.Google ScholarCross Ref
- J. Vlasblom, S. Wu, S. Pu, M. Superina, G. Liu, C. Orsi, and S. J. Wodak. Genepro: a cytoscape plug-in for advanced visualization and analysis of interaction networks. Bioinformatics, 22(17):2178--2179, 2006. Google ScholarDigital Library
Index Terms
- An integrated model for visualizing biclusters from gene expression data and PPI networks
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