JACIII Vol.16 No.2 pp. 273-281
doi: 10.20965/jaciii.2012.p0273


Interactive Data Mining Tool for Microarray Data Analysis Using Formal Concept Analysis

Takanari Tanabata*, Fumiaki Hirose*,
Hidenobu Hashikami**, and Hajime Nobuhara**

*National Institute of Agrobiological Sciences, 2-1-2 Kannondai, Tsukuba, Ibaraki 305-8602, Japan

**Department of Intelligent Interaction Technologies, University of Tsukuba, 1-1-1 Tenodai, Tsukuba Science City, Ibaraki 305-8573, Japan

September 7, 2011
November 5, 2011
March 20, 2012
formal concept analysis, microarray, visualization, bioinformatics
The DNA microarray analysis can explain gene functions by measuring tens of thousands of gene expressions at once and analyzing gene expression profiles that are obtained from the measurement. However, gene expression profiles have such a vast amount of information and therefore most analyses work are done on the data narrowed down by statistical methods, there remains a possibility ofmissing out on genes that consist the factors of phenomena from their evaluations. This study propose a method based on a formal concept analysis to visualize all gene expression profiles and characteristic information that can be obtained from annotation information of each gene so that the user can overview them. In the formal concept analysis, a lattice structure that allows genes to be hierarchically classified and made viewable is built based on the inclusion relations of attributes from a context table in which gene is the object and the attributes are expression profiles and binarized characteristic information. With the proposed method, the user can change the overview state by adjusting the expression ratio and the binary state of characteristic information, understand the relational structure of gene expressions, and carry out analyses of gene functions. We develop software to practice the proposed method, and then ask a biologist to evaluate effectiveness of proposed method applied to a function analysis of genes related to blue light signaling of rice seedlings.
Cite this article as:
T. Tanabata, F. Hirose, H. Hashikami, and H. Nobuhara, “Interactive Data Mining Tool for Microarray Data Analysis Using Formal Concept Analysis,” J. Adv. Comput. Intell. Intell. Inform., Vol.16 No.2, pp. 273-281, 2012.
Data files:
  1. [1] T. Sasaki et al., “The genome sequence and structure of rice chromosome 1,” Nature, Vol.420, pp. 312-316, 2002.
  2. [2] M. Takano, N. Inagaki, X. Xie, S. Kiyota, A. Baba-Kasai, T. Tanabata, and T. Shinomura, “Phytochromes are the sole photoreceptors for perceiving red/far-red light in rice,” Proc. Natl. Acad. Sci. USA, Vol.106, pp. 14705-14710, 2009.
  3. [3] S. Isobe, A. Nakaya, and S. Tabata, “Genotype Matrix Mapping: Searching for Quantitative Trait Loci Interactions in Genetic Variation in Complex Traits,” DNA Research, Vol.14, pp. 217-225, 2009.
  4. [4] Dov Stekel, “Microarray Bioinformatics,” Cambridge University Press, 2003.
  5. [5] D.B. Allison, X. Cui, G. P. Page, and M. Sabripour, “Microarray data analysis: from disarray to consolidation and consensus,” Nature Reviews Genetics Vol.7, pp. 55-65, 2006.
  6. [6] K. M. Folta, M. A. Pontin, G. Karlin-Neumann, R. Bottini, and E. P. Spalding, “Genomic and physiological studies of early cryptochrome 1 action demonstrate roles for auxin and gibberellin in the control of hypocotyl growth by blue light,” The Plant J., Vol.36, pp. 203-214, 2003.
  7. [7] M. B. Eisen, P. T. Spellman, P. O. Brown, and D. Botstein, “Cluster analysis and display of genome-wide expression patterns,” Proc. Natl. Acad. Sci. USA, Vol.95, pp. 14863-14868, 1998.
  8. [8] J. P. Mehta and S. Rani, “Software and tools for microarray data analysis,” Methods in Molecular Biology, Vol.784, pp. 41-53, 2011.
  9. [9] C. Carpineto and G. Romano, “Concept Data Analysis: Theory and Applications,” Wiley, 2004.
  10. [10] B. A. Davey and H. A. Priestley, “Introduction to Lattices and Order, second edition,” Cambridge University Press, 2002.
  11. [11] B. Ganter and R. Wille, “Formal Concept Analysis: Mathematical Foundations,” Springer, 1998.
  12. [12] K. Sawase and H. Nobuhara, “Lattice Visualization System based on Formal Concept Analysis for Large Scale Image Database,” J. of Japan Society for Fuzzy Theory and Intelligent Informatics, Vol.21, pp. 32-40, 2009.
  13. [13] S. Andrews, “In-Close, a fast algorithm for computing formal concepts,” In: Int. Conf. on Conceptual Structures (ICCS), Moscow, 2009.
  14. [14] Rice Annotation Project, “The Rice Annotation Project Database (RAP-DB): 2008 update,” Nucleic Acids Res., Vol.36, pp. D1028-D1033, 2008.
  15. [15] T. Itoh, T. Tanaka et al., “Curated genome annotation of Oryza sativa ssp. japonica and comparative genome analysis with Arabidopsis thaliana,” Genome Res., Vol.17, pp. 175-183, 2007.

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