Integrative clustering methods for high-dimensional molecular data

Translational Cancer Research

Volumn 3, Issue 3, 2014, Pages 202-216

  Chalise, Prabhakar ^a   Koestler, Devin C ^a   Bimali, Milan ^a   Yu, Qing ^a   Fridley, Brooke L ^a  

^a UNIVERSITY OF KANSAS SCHOOL OF MEDICINE   (United States)

Author keywords

Consensus clustering; Cophenetic correlation; Latent models; Mixture models; Non negative matrix factorization

Indexed keywords

EID: 84962699589     PISSN: 2218676X     EISSN: 22196803     Source Type: Journal    
DOI: 10.3978/j.issn.2218-676X.2014.06.03     Document Type: Review

References (58)

1. Sørlie T, Perou CM, Tibshirani R, et al. Gene expression patterns of breast cancer carcinomas distinguish tumor subclasses with clinical implications. PNAS 2001;98:10869-74.
2. Verhaak RG, Hoadley KA, Purdom E, et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 2010;17:98-110.
3. Sotiriou C, Neo SY, McShane LM, et al. Breast Cancer Classification and prognosis based on gene expression profiles from a population-based study. PNAS 2003;100:10393-8.
4. Alizadeh AA, Eisen MB, Davis RE, et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 2000;403:503-11.
5. Lapointe J, Li C, Higgins JP, et al. Gene xpression profiling identifies clinically relevant subtype of prostate cancer. PNAS 2004;101:811-6.
6. Eisen MB, Spellman PT, Brown PO, et al. Cluster Analysis and display of genome-wide expression patterns. PNAS 1998;95:14863-8.
7. Wirapati P, Sotiriou C, Kunkel S, et al. Meta-Analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signature. Breast Cancer Res 2008;10:R65.
8. Ang PW, Loh M, Liem N, et al. Comprehensive profiling of DNA methylation in colorectal cancer reveals subgroups with distinct clinicopathological and molecular features. BMC Cancer 2010;10:227.
9. Marsit CJ, Christensen BC, Houseman EA, et al. Epigenetic profiling reveals etiologically distinct patterns of DNA methylation in head and neck squamous cell carcinoma. Carcinogenesis 2009;30:416-22.
10. Issa JP. CpG island methylator phenotype in cancer. Nat Rev Cancer 2004;4:988-93.
11. Shen R, Olshen AB, Ladanyi M. Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast cancer subtype analysis. Bioinformatics 2009;25:2906-12.
12. Zhang S, Liu CC, Li W, et al. Discovery of multi-dimensional modules by integrative analysis of cancer genomic data. Nucleic Acids Res 2012;40:9379-91.
13. Kormaksson M, Booth JG, Figueroa ME, et al. Integrative Model-based Clustering of microarray methylation and expression data. Ann Appl Stat 2012;6:1327-47.
14. Tibshirani R, Hastie T, Narashimhan B, et al. Diagnosis of Multiple Cancer types by Shrunken Centroids of Gene Expression. PNAS 2002;99:6567-72.
15. Brunet JP, Tamayo P, Golub TR, et al. Metagenes and molecular pattern discovery using matrix factorization. Proc Natl Acad Sci U S A 2004;101:4164-9.
16. Koestler DC, Marsit CJ, Christensen BC, et al. Semi-supervised recursively partitioned mixture models for identifying cancer subtypes. Bioinformatics 2010;26:2578-85.
17. Handl J, Knowles J, Kell DB. Computational Cluster Validation in post-genomic data analysis. Bioinformatics 2005;21:3201-12.
18. Hastie T, Tibshirani R, Friedman J. eds. The Elements of Statistical Learning: Data Mining, Inference and Prediction. New York: Springer, 2001.
19. Tavazoie S, Hughes JD, Campbell MJ, et al. Systematic determination of genetic architecture. Nat Genet 1999;22:281-5.
20. Kim EY, Kim SY, Ashlock D, et al. MULTI-K: accurate classification of microarray subtypes using ensemble k-means clustering. BMC Bioinformatics 2009;10:260.
21. Kaufman L, Rousseeuw PJ. eds. Finding groups in data: An Introduction to Cluster Analysis. New Jersey: John Wiley & Sons, 1990.
22. Bhattacharjee A, Richards WG, Staunton J, et al. Classification of Human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. PNAS 2001;98:13790-5.
23. Beer DG, Kardia SL, Huang CC, et al. Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat Med 2002;8:816-24.
24. Sherlock G. Analysis of large-scale gene expression data. Curr Opin Immunol 2000;12:201-5.
25. Kerr G, Ruskin HJ, Crane M, et al. Techniques for clustering gene expression data. Comput Biol Med 2008;38:283-93.
26. Bezdek JC. eds. Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Springer, 1981.
27. Dembélé D, Kastner P. Fuzzy C-means method for clustering microarray data. Bioinformatics 2003;19:973-80.
28. Tari L, Baral C, Kim S. Fuzzy c-means clustering with prior biological knowledge. J Biomed Inform 2009;42:74-81.
29. Kohonen T. The Self-organizing Map. Proc IEEE Conf 1990;78:1464-80.
30. Kohonen T. eds. Self-Organizing Maps. Berlin: Springer, 2001.
31. Tamayo P, Slomin D, Mesirov J, et al. Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. PNAS 1999;96:2907-12.
32. Torkkola K, Gardner RM, Kaysser-Kranich T, et al. Self-Organizing maps in mining gene expression data. Information Sciences 2001;139:79-96.
33. Nikkilä J, Törönen P, Kaski S, et al. Analysis and Visualization of gene expression data using Self-Organizing Maps. Neural Networks 2002;15:953-66.
34. Patterson AD, Li H, Eichler GS, et al. UPLC-ESI-TOFMS-Based Metabolomics and Gene Expression Dynamics Inspector Self-Organizing Metabolomic Maps as Tools for Understanding the Cellular Response to Ionizing Radiation. Anal Chem 2008;80:665-74.
35. Tseng GC, Wong WH. Tight clustering: A resampling based approach for identifying stable and tight patterns in the data. Biometrics 2005;61:10-6.
36. Tseng GC. A Comparative Review of Gene Clustering in Expression Profile. Pro 8th International Conference on Control, Automation, Robotics and Vision (ICARCV) 2004;2:1320-4.
37. Fraley C, Raftery AE. How many clusters? Which cluster method? Answer via model based cluster analysis. Computer J 1998;41:578-88.
38. Pan W, Lin J, Le CT. Model based cluster analysis of microarray gene-expression data. Genome Biol 2002;3:RESEARCH0009.
39. McLachlan GJ, Bean RW, Peel D. A mixture model based approach to the clustering of microarray expression data. Bioinformatics 2002;18:413-22.
40. McNicholas PD, Murphy TB. Model-based clustering of microarray expression data via latent Gaussian Mixture models. Bioinformatics 2010;26:2705-12.
41. Yeung KY, Fraley C, Murua A, et al. Model based clustering and data transformations for gene expression data. Bioinformatics 2001;17:977-87.
42. Schwartz G. Estimating the dimension of a model. Ann Stat 1978;6:461-4.
43. Paatero P, Tapper U. Positive Matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values. Environmerics 1994;5:111-26.
44. Lee DD, Seung HS. Learning the parts of objects by non-negative matrix factorization. Nature 1999;401:788-91.
45. Kim PM, Tidor B. Subsystem identification through dimensionality reduction of large scale gene expression data. Genome Res 2003;13:1706-18.
46. Liu W, Yuan K, Ye D. Reducing microarray data via non-negative matrix factorization for visualization and clustering analysis. J Biomed Inform 2008;41:602-6.
47. Monti S, Tamayo P, Mesirov J, et al. Consesus Clustering: A resampling based method for class discovery and visualization of gene expression microarray data. Machine Learning 2003;52:91-118.
48. Tipping ME, Bishop CM. Probabilistic Principal Component Analysis. J Royal Statistical Society 1999;61:611-22.
49. Zha H, He X, Ding C, et al. Spectral Relaxation for K-means clustering. Available online: http://ranger.uta.edu/~chqding/papers/KmeansNIPS2001.pdf
50. Dempster AP, Laird NM, Rubin DB. Maximum Likelihood from Incomplete Data via The EM Algorithm (with discussion). Journal of the Royal Statistical Society 1977;39:1-38.
51. Tibshirani R. Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society 1996;58:267-88.
52. Shen R, Wang S, Mo Q. Sparse Integrative clustering of multiple omics data sets. Ann Appl Stat 2013;7:269-94.
53. Zou H, Hastie T. Regularization and variable selection via the Elastic Net. J Royal Statistical Society 2005;67:301-20.
54. Tibshirani R, Saunders M, Rosset S, et al. Sparsity and smoothness via the fused lasso. J Royal Statistical Society 2005;67:91-108.
55. Bair E, Tibshirani R. Semi-Supervised Methods to Predict Patient Survival from Gene Expression Data. PLoS Biology 2004;2:E108.
56. Hastie T, Tibshirani R, Friedman J. eds. The Elements of Statistical Learning: Data Mining, Inference and Prediction. New York: Springer, 2001.
57. Shipp MA, Ross KN, Tamayo P, et al. Diffuse large cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nature Medicine 2002;8:68-74.
58. Marcel D, Bühlmann P. Supervised clustering of genes. Genome Biol 2002;3:RESEARCH0069.