Integrating functional knowledge during sample clustering for microarray data using unsupervised decision trees

Biometrical Journal

Volumn 49, Issue 2, 2007, Pages 214-229

  Redestig, Henning ^a   Repsilber, Dirk ^b   Sohler, Florian ^c   Selbig, Joachim ^a,b  

^a MAX PLANCK INSTITUTE OF MOLECULAR PLANT PHYSIOLOGY   (Germany)

^b UNIVERSITY OF POTSDAM   (Germany)

^c UNIVERSITY OF MUNICH   (Germany)

Author keywords

Clustering; Functional ontologies; Gene expression; Integrative data analysis; Microarray; Samplewise clustering; UDT; Unsupervised decision trees

Indexed keywords

BIOINFORMATICS; CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; DECISION TREES; INFORMATION FILTERING;

CLUSTERINGS; FUNCTIONAL ONTOLOGIES; GENES EXPRESSION; INTEGRATIVE DATA ANALYSE; MICROARRAY GENE EXPRESSION DATA; MICROARRAYS DATA; SAMPLEWISE CLUSTERING; UDT; UNSUPERVISED DECISION TREE;

GENE EXPRESSION;

EID: 34247388808     PISSN: 03233847     EISSN: 15214036     Source Type: Journal    
DOI: 10.1002/bimj.200610278     Document Type: Article

References (43)

1. Al-Shahrour, F., Diaz-Uriarte, R., and Dopazo, J. (2004). Fatigo a web tool for nding signicant associations of gene ontology terms with groups of genes. Bioinformatics 20, 578-580.
2. Alter, O., Brown, P. O., and Botstein, D. (2000). Singular value decomposition for genome-wide expression data processing and modeling. Proceeedings of the National Academy of Sciences of the United States of America 97, 10101-10106.
3. Basak, J. and Krishnapuram, R. (2005). Interpretable hierarchical clustering by constructing and unsupervised decision tree. IEEE transactions on knowledge and data engineering 17, 121-132.
4. Bellot, P. and El-Bèze, M. (2000). Clustering by means of unsupervised decision trees or hierarchical and k-means-like algorithm. In RIAO' 2000 Conference Proceedings, 344-363.
5. Benjamini, Y. and Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics 29, 1165-1188.
6. Breitling, R., Amtmann, A., and Herzyk, P. (2004). Iterative group analysis (iGA): a simple tool to enhance sensitivity and facilitate interpretation of microarray experiments. BMC Bioinformatics 5, 5-34.
7. Carey, V. J., Gentry, J., Whalen, E., and Gentleman, R. (2005). Network structures and algorithms in bioconductor. Bioinformatics 21, 135-136.
8. Chan, W. C. (2005). Gene expression proling in lymphoma diagnosis and research. Croatian Medical Journal 46, 349-359.
9. Chiaretti, S., Li, X., Gentleman, R., Vitale, A., Vignetti, M., Mandelli, F., Ritz, J., and Foa, R. (2004). Gene expression prole of adult T-cell acute lymphocytic leukemia identies distinct subsets of patients with different response to therapy and survival. Blood 103, 2771-2778.
10. Eisen, M., Spellman, P., Brown, P., and Botstein, D. (1998). Cluster analysis and display of genomewide expression patterns. Proceeedings of the National Academy of Sciences of the United States of America 95, 14863-14868.
11. Eriksson, L., Johansson, E., Kettaneh-Wold, N., and Wold, S. (1999). Introduction to Multi- and Megavariate Data Analysis using Projection Methods (PCA & PLS), chapter 8. Umetrics, Umea, Sweden.
12. Fraley, C. and Raftery, A. (2002). MCLUST Software for model-based clustering, density estimation and discriminat analysis, and density estimation. Journal of the American Statistical Association 97, 611-631.
13. Gansner, E., Koutsoos, E., and North, S. (2002). Drawing graphs with dot. AT&T.
14. Gentleman, R., Carey, V., Bates, D., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J., Hornik, K., Hothorn, T., Huber, W., Iacus, S., Irizarry, R., Li, F. L. C., Maechler, M., Rossini, A., Sawitzki, G., Smith, C., Smyth, G., Tierney, L., Yang, J., and Zhang, J. (2004). Bioconductor: Open software development for computational biology and bioinformatics. Genome Biology 5, R80.
15. Getz, G., Levine, E., and Domany, E. (2000). Coupled two-way clustering analysis of gene microarray data. Proceeedings of the National Academy of Sciences of the United States of America 97, 12079-12084.
16. Goeman, J., van de Geer, S., de Kort, F., and van Houwelingen, H. (2004), A global test for groups of genes: testing association with a clinical outcome. Bioinformatics 20, 93-99.
17. Golub, T., Slonim, D., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J., Coller, H., Loh, M., Downing, J., Caligiuri, M., Bloomeld, C., and Lander, E. (1999). Molecular classication of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531-537.
18. Hannah, M. A., Heyer, A. G., and Hincha, D. K. (2005). A global survey of gene regulation during cold acclimation in Arabidopsis thaliana. Public Library of Science Genetics 1, e26.
19. Hubert, L. and Arabie, P. (1985). Comparing partitions. Journal of Classication 2, 193-218.
20. Jiang, H., Shen, Y. M., Quinn, A. M., and Penning, T. M. (2005). Competing roles of cytochrome p450 1a1/1b1 and aldo-keto reductase 1a1 in the metabolic activation of (±)-7,8-dihydroxy-7,8-dihydrobenzo[a]pyrene in human bronchoalveolar cell extracts. Chemical research in toxicology 18, 365-374.
21. Karakos, D., Khudanpur, S., Eisner, J., and Priebe, C. E. (2005). Unsupervised classication via decision trees: an information-theoretic perspective. In Proceedings of the 2005 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE.
22. Kaufman, L. and Rouosseeuw, P. (1990). Finding groups in data: an introduction to cluster analysis, chapter 5. Wiley, New York.
23. Kersey, J. H. (1997). Fifty years of studies of the biology and therapy of childhood leukemia. Blood 90, 4243-4251.
24. Kim, A. S. (2004). Clinical impact of gene expression proling on oncology diagnosis, prognosis, and treatment. Combinatorial Chemistry & High Throughput Screening. 7, 183-206.
25. Kim, C. and Falkow, S. (2003). Significance analysis of lexical bias in microarray data. BMC Bioinformatics 4, 12-12.
26. Krzanowski, W. (1987). Cross-validation in principal component analysis. Biometrics 43, 575-584.
27. Kuss, O., Gromann, C., and Diepgen, T. L. (2006). Model-based clustering of binary longitudinal atopic dermatitis disease histories by latent class mixture models. Biometrical Journal 48, 105-116.
28. Lottaz, C. and Spang, R. (2005). Molecular decomposition of complex clinical phenotypes using biologically structured analysis of microarray data. Bioinformatics 21, 1971-1978.
29. Maclachlan, G. (1987). On bootstrapping the likelihood ratio test statistic for the number of components in a normal mixture. Applied statistics 36, 318-324.
30. Madeira, S. and Oliveira, A. (2004). Biclustering algorithms for biological data analysis: A survey. IEEE/ACM Transactions on computational biology and bioinformatics 1, 24-45.
31. Mansmann, U. and Meister, R. (2005). Testing differential gene expression in functional groups. Goemans global test versus an ANCOVA approach. Methods of Information in Medicine 44, 449-453.
32. Marshall, E. (2004). Getting the Noise Out of Gene Arrays. Science 306, 630-631.
33. Michielis, S., Koscielny, S., and Hill, C. (2005). Prediction of cancer outcome with microarrays: a multiple random validation study. The Lancet 365, 488-492.
34. Mootha, V. K., Lindgren, C. M., Eriksson, K. F., Subramanian, A., Sihag, S., Lehar, J., Puigserver, P., Carlsson, E., Ridderstrale, M., Laurila, E., Houstis, N., Daly, M. J., Patterson, N., Mesirov, J. P., Golub, T. R., Tamayo, P., Spiegelman, B., Lander, E. S., Hirschhorn, J. N., Altshuler, D., and Groop, L. C. (2003). PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nature Genetics 34, 267-273.
35. Palenchar, P., Kouranov, A., Lejay, L., and Coruzzi, G. (2004). Genome-wide patterns of carbon and nitrogen regulation of gene expression validate the combined carbon and nitrogen (CN)-signaling hypothesis in plants. Genome Biology 5.
36. R Development Core Team (2004). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0.
37. Rand, W. M. (1971). Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association 66, 846-850.
38. Rehfeld, J. F. and Goetze, J. P. (2003). The posttranslational phase of gene expression: new possibilities in molecular diagnosis. Current Molecular Medicine 3, 25-38.
39. Spira, A., Beane, J., Shah, V., Liu, G., Schembri, F., Yang, X., Palma, J., and Brody, J. S. (2004). Effects of cigarette smoke on the human airway epithelial cell transcriptome. Proceeedings of the National Academy of Sciences of the United States of America 101, 10143-10148.
40. Spivack, S. D., Hurteau, G. J., Reilly, A. A., Aldous, K. M., Ding, X., and Kaminsky, L. S. (2001). Cyp1b1 expression in human lung. Drug Metabolism and Disposition 29, 916-922.
41. Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E. S., and Golub, T. R. (1999). Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. Proceeedings of the National Academy of Sciences of the United States of America 96, 2907-2912.
42. The Gene Ontology Consortium (2000). Gene ontology: tool for the unication of biology. Nature Genetics 25, 25-29.
43. Tomfohr, J., Lu, J., and Kepler, T. (2005). Pathway level analysis of gene expression using singular value decomposition. BMC Bioinformatics 6, 225.