Genomics & Informatics

  • 제5권3호
  • /
  • Pages.124-128
  • /
  • 2007
  • /
  • 1598-866X(pISSN)
  • /
  • 2234-0742(eISSN)
한국유전체학회 (Korea Genome Organization)
권호 표지

PathTalk: Interpretation of Microarray Gene-Expression Clusters in Association with Biological Pathways

  • Chung, Tae-Su (Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine) ;
  • Chung, Hee-Joon (Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine) ;
  • Kim, Ju-Han (Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine)
  • 발행 : 2007.09.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Microarray technology enables us to measure the expression of tens of thousands of genes simultaneously under various experimental conditions. Clustering analysis is one of the most successful methods for analyzing microarray data using the assumption that co-expressed genes may be co-regulated. It is important to extract meaningful clusters from a long unordered list of clusters and to evaluate the functional homogeneity and heterogeneity of clusters. Many quality measures for clustering results have been suggested in different conditions. In the present study, we consider biological pathways as a collection of biological knowledge and used them as a reference for measuring the quality of clustering results and functional homogeneities. PathTalk visualizes and evaluates functional relationships between gene clusters and biological pathways.

키워드

참고문헌

  1. Ben-Dor, A., Shamir, R, Yakhini, Z. (1999). Clustering gene expression patterns. J. Comput. Biol. 6, 281-297 https://doi.org/10.1089/106652799318274
  2. Chung, H.J., Kim, M., Park, C.H., and Kim, J.H. (2004). ArrayXPath: mapping and visualizing microarray gene expression data with integrated pathway resources using Scalable Vector Graphics. Nucleic Acids Research 1;32:W460-W464
  3. Chung, H.J., Park, C.H., Han, M.R, Lee, S., Ohn, J.H., Kim, J., Kim, J., and Kim, J.H. (2005). ArrayXPath II: mapping and visualizing microarray gene expression data with biomedical ontologies and integrated pathway resources using Scalable Vector Graphics. Nucleic Acids Research 1;33:W621-W626
  4. Dahlquist, K.D., Salomonis, N., Vranizan, K., Lawlor, S.C., and Conklin, B.R (2002). GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways. Nature Genet. 31, 19-20 https://doi.org/10.1038/ng0502-19
  5. Eisen, M.B., Spellman, P.T., Brown, P.O., and Botstein, D. (1998). Clustering analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95, 14863-14868
  6. Gat-Viks, I., Sharan, R, Shamir, R (2003). Scoring clustering solution by their biological relevance, Bioinformatics 19, 2381-2389 https://doi.org/10.1093/bioinformatics/btg330
  7. Gibbons, F.D. and Roth F.P., (2002). Judging the Quality of Gene Expression-Based Clustering Methods Using Gene Annotation. Genome Research 12,1574-1581 https://doi.org/10.1101/gr.397002
  8. Hansen, P. and Jaumard, B. (1997). Cluster analysis and mathematical programming. Math. Program. 79,191-215
  9. Sharan, R, Maron-Katz, A., and Shamir, R (2003). Click and expander: a system for clustering and visualizing gene expression data. Bioinformatics 19, 1787-1799 https://doi.org/10.1093/bioinformatics/btg232
  10. Sharan, R. and Shamir, R. (2000). CLICK: a clustering algorithm with applications to gene expression analysis. In Proceedings ofthe Eighth International Conference on Intelligent Systems for Molecular Biology (ISMB) 307-316
  11. Sakal, R.R. (1977). Clustering and classification: background and current directions. In Van Ryzin,J. (ed.), Classification and Clustering. Academic Press, London, pp. 1-15
  12. Spellman, P.T, Sherlock, G., Zhang, H.Q., Iyer, V.R, Andres, K., Eisen, M.B., Brown, P.O., Botstein, D., and Futcher, B. (1998). Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Bioi. Cell 9,3273-3297 https://doi.org/10.1091/mbc.9.12.3273
  13. Stephanopoulos, G., Hwang, D., Schmitt, W., Misra, J., and Stephanopoulos, G. (2002). Mapping physiological states from microarray expression measurments. Bioinformatics 18, 1054-1063 https://doi.org/10.1093/bioinformatics/18.8.1054
  14. Tamayo, P., Sionim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E.S., Golub, T. (1999). Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc. Natl Acad. Sci. USA 96,2907-2912
  15. Yeung, K., Haynor, D., and Ruzzo, W. (2001). Validating clustering for gene expression data. Bioinformatics 17, 309-318 https://doi.org/10.1093/bioinformatics/17.4.309