MULTI-K: Accurate classification of microarray subtypes using ensemble k-means clustering

BMC Bioinformatics

Volumn 10, Issue , 2009, Pages 260-

  Kim, Eun Youn ^a   Kim, Seon Young ^b   Ashlock, Daniel ^c   Nam, Dougu ^a  

^a National Institute for Mathematical Sciences   (South Korea)

^b Korea Research Institute of Bioscience and Biotechnology (KRIBB)   (South Korea)

^c UNIVERSITY OF GUELPH   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ENSEMBLE CLUSTERING; GEOMETRIC COMPLEXITY; HIGH-DIMENSIONAL STRUCTURES; K-MEANS CLUSTERING; MICROARRAY CLUSTERS; ORIGINAL ALGORITHMS; SAMPLE CLASSIFICATION; UNSUPERVISED CLUSTERING METHODS;

DISEASE CONTROL; GENE EXPRESSION;

CLUSTERING ALGORITHMS;

ACCURACY; ARTICLE; BREAST CANCER; CLASSIFICATION ALGORITHM; CLUSTER ANALYSIS; CONTROLLED STUDY; FUNCTIONAL GENOMICS; GENE EXPRESSION PROFILING; LEUKEMIA; MICROARRAY ANALYSIS; PREDICTION; SURVIVAL TIME;

CLUSTER ANALYSIS; COMPUTATIONAL BIOLOGY; DATABASES, GENETIC; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PATTERN RECOGNITION, AUTOMATED; SOFTWARE;

EID: 70349730070     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-10-260     Document Type: Article

References (41)

1. Spellman P.T, Sherlock G, Zhang M.Q, Iyer V.R, Anders K, Eisen M.B, Brown P.O, Botstein D, Futcher B. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell 1998, 9(12):3273-3297. 25624, 9843569.
2. Eisen M.B, Spellman P.T, Brown P.O, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 1998, 95(25):14863-14868. 10.1073/pnas.95.25.14863, 24541, 9843981.
3. Andre F, Pusztai L. Molecular classification of breast cancer: implications for selection of adjuvant chemotherapy. Nat Clin Pract Oncol 2006, 3(11):621-632. 10.1038/ncponc0636, 17080180.
4. Perou C.M, Sorlie T, Eisen M.B, Rijn M, Jeffrey S.S, Rees C.A, Pollack J.R, Ross D.T, Johnsen H, Akslen L.A. Molecular portraits of human breast tumours. Nature 2000, 406(6797):747-752. 10.1038/35021093, 10963602.
5. Sorlie T, Tibshirani R, Parker J, Hastie T, Marron J.S, Nobel A, Deng S, Johnsen H, Pesich R, Geisler S. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA 2003, 100(14):8418-8423. 10.1073/pnas.0932692100, 12829800, 166244.
6. Golub T.R, Slonim D.K, Tamayo P, Huard C, Gaasenbeek M, Mesirov J.P, Coller H, Loh M.L, Downing J.R, Caligiuri M.A. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999, 286(5439):531-537. 10.1126/science.286.5439.531, 10521349.
7. Handl J, Knowles J, Kell D.B. Computational cluster validation in post-genomic data analysis. Bioinformatics 2005, 21(15):3201-3212. 10.1093/bioinformatics/bti517, 15914541.
8. Rosenwald A, Wright G, Chan W.C, Connors J.M, Campo E, Fisher R.I, Gascoyne R.D, Muller-Hermelink H.K, Smeland E.B, Giltnane J.M. The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N Engl J Med 2002, 346(25):1937-1947. 10.1056/NEJMoa012914, 12075054.
9. Dudoit S, Fridlyand J. A prediction-based resampling method for estimating the number of clusters in a dataset. Genome Biol 2002, 3(7):RESEARCH0036. 10.1186/gb-2002-3-7-research0036, 126241, 12184810.
10. Milligan G, Cooper M. An examination of procedures for determining the number of clusters in a data set. Psychometrika 1985, 50:159-179.
11. Tibshirani R, Walther G, Hastie T. Estimating the number of clusters in a data set via the gap statistic. J R Stat Soc B 2001, 63:411-423.
12. Ben-Hur A, Elisseeff A, Guyon I. A stability based method for discovering structure in clustered data. Pac Symp Biocomput 2002, 7:6-17.
13. Strehl A, Ghosh J. Cluster ensembles - a knowledge reuse framework for combining multiple partitions. J Mach Learn Res 2002, 3:583-617.
14. Swift S, Tucker A, Vinciotti V, Orengo C, Liu X, Kellam P. Consensus clustering and functional interpretation of gene-expression data. Genome Biol 2003, 5:R94.
15. Mc Shane L.M. Methods for assessing reproducibility of clustering patterns observed in analyses of microarray data. Bioinformatics 2002, 18:1462-1469. 10.1093/bioinformatics/18.11.1462, 12424117.
16. Monti S, Tamayo P, Mesirov J, Golub T. Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Mach Learn 2003, 52:91-118.
17. Smolkin M, Ghosh D. Cluster stability scores for microarray data in cancer studies. BMC Bioinformatics 2003, 4:36. 10.1186/1471-2105-4-36, 200969, 12959646.
18. Bertoni A, Valentini G. Ensembles based on random projections to improve the accuracy of clustering algorithms. Neural Nets, (WIRN 2005), LNCS 2005, 3931:31-37.
19. Yu Z, Wong H-S, Wang H. Graph-based consensus clustering for class discovery from gene expression data. Bioinformatics 2007, 23(21):2888-2896. 10.1093/bioinformatics/btm463, 17872912.
20. Ashlock D.A, Kim E.Y, Guo L. Multi-clustering: avoiding the natural shape of underlying metrics. ANNIE: 2005 2005, 15:453-461.
21. Fred A.L.N, Jain A.K. Robust data clustering. Proc IEEE CS Conf Computer Vision and Pattern Recognition 2003, 2:128-133.
22. Kuncheva L.I, Vetrov D.P. Evaluation of stability of k-means cluster ensembles with respect to random initialization. IEEE Trans Pattern Anal Mach Intell 2006, 28(11):1798-1808. 10.1109/TPAMI.2006.226, 17063684.
23. Shannon C.E. A mathematical theory of communication. Bell Syst Tech J 1948, 27:379-413.
24. Medvedovic M, Sivaganesan S. Bayesian infinite mixture model based clustering of gene expression data. Bioinformatics 2002, 18(9):1194-1206. 10.1093/bioinformatics/18.9.1194, 12217911.
25. McLachlan G.J, Bean R.W, Peel D. A mixture model-based approach to the clustering of microarray expression data. Bioinformatics 2002, 18(3):413-422. 10.1093/bioinformatics/18.3.413, 11934740.
26. Qin Z.S. Clustering microarray gene expression data using weighted Chinese restaurant process. Bioinformatics 2006, 22(16):1988-1997. 10.1093/bioinformatics/btl284, 16766561.
27. Hubert L, Arabie P. Comparing partitions. Journal of classification 1985, 2:193-218.
28. Milligan G, Cooper M. A study of comparability of external criteria for hierarchical cluster analysis. Multivariate Behavioral Research 1986, 21:441-458.
29. Tenenbaum J.B, de Silva V, Langford J.C. A global geometric framework for nonlinear dimensionality reduction. Science 2000, 290:2319-2322. 10.1126/science.290.5500.2319, 11125149.
30. Alizadeh A.A, Eisen M.B, Davis R.E, Ma C, Lossos I.S, Rosenwald A, Boldrick J.C, Sabet H, Tran T, Yu X. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 2000, 403(6769):503-511. 10.1038/35000501, 10676951.
31. Yeoh E.J, Ross M.E, Shurtleff S.A, Williams W.K, Patel D, Mahfouz R, Behm F.G, Raimondi S.C, Relling M.V, Patel A. Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. Cancer Cell 2002, 1(2):133-143. 10.1016/S1535-6108(02)00032-6, 12086872.
32. Ramaswamy S, Tamayo P, Rifkin R, Mukherjee S, Yeang C.H, Angelo M, Ladd C, Reich M, Latulippe E, Mesirov J.P. Multiclass cancer diagnosis using tumor gene expression signatures. Proc Natl Acad Sci USA 2001, 98(26):15149-15154. 10.1073/pnas.211566398, 64998, 11742071.
33. He H, Jazdzewski K, Li W, Liyanarachchi S, Nagy R, Volinia S, Calin G.A, Liu C.G, Franssila K, Suster S. The role of microRNA genes in papillary thyroid carcinoma. Proc Natl Acad Sci USA 2005, 102(52):19075-19080. 10.1073/pnas.0509603102, 1323209, 16365291.
34. Yanai I, Benjamin H, Shmoish M, Chalifa-Caspi V, Shklar M, Ophir R, Bar-Even A, Horn-Saban S, Safran M, Domany E. Genome-wide midrange transcription profiles reveal expression level relationships in human tissue specification. Bioinformatics 2005, 21(5):650-659. 10.1093/bioinformatics/bti042, 15388519.
35. Sorlie T, Tibshirani R, Parker J, Hastie T, Marron J.S, Nobel A, Deng S, Johnsen H, Pesich R, Geisler S. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA 2003, 100(14):8418-8423. 10.1073/pnas.0932692100, 166244, 12829800.
36. Dudoit S, Fridlyand J. Bagging to improve the accuracy of a clustering procedure. Bioinformatics 2003, 19:1090-1099. 10.1093/bioinformatics/btg038, 12801869.
37. Martelli P.L, Fariselli P, Casadio R. An ENSEMBLE machine learning approach for the prediction of all-alpha membrane proteins. Bioinformatics 2003, 19:i205-i211. 10.1093/bioinformatics/btg1027, 12855459.
38. Shen H-B, Chou K-C. Ensemble classifier for protein fold recognition. Bioinformatics 2006, 22:1717-1722. 10.1093/bioinformatics/btl170, 16672258.
39. Nam D, Yoon S.H, Kim J.F. Ensemble learning of genetic networks from time-series expression data. Bioinformatics 2007, 23:3225-3231. 10.1093/bioinformatics/btm514, 17977884.
40. Qiu P, Wang Z.J, Liu K.J.R. Ensemble dependence model for classification and prediction of cancer and normal gene expression data. Bioinformatics 2005, 21:3114-3121. 10.1093/bioinformatics/bti483, 15879455.
41. Kim D.W, Lee K.H, Lee D. Detecting clusters of different geometrical shapes in microarray gene expression data. Bioinformatics 2005, 21(9):1927-1934. 10.1093/bioinformatics/bti251, 15647300.