Integrative clustering of multi-level omics data for disease subtype discovery using sequential double regularization

Biostatistics

Volumn 18, Issue 1, 2017, Pages 165-179

  Kim, Sunghwan ^a,d   Oesterreich, Steffi ^b   Kim, Seyoung ^c   Park, Yongseok ^a   Tseng, George C ^a  

^a UNIVERSITY OF PITTSBURGH GRADUATE SCHOOL OF PUBLIC HEALTH   (United States)

^b UNIVERSITY OF PITTSBURGH   (United States)

^c Carnegie Mellon University ^*  (United States)

^d Korea University   (South Korea)

Author keywords

Group structured lasso; Integrative clustering (iCluster); Penalized EM algorithm; The Cancer Genome Atlas (TCGA)

Indexed keywords

BREAST NEOPLASMS; CLASSIFICATION; CLUSTER ANALYSIS; GENETICS; GENOMICS; HUMAN; PERSONALIZED MEDICINE; PROCEDURES;

BREAST NEOPLASMS; CLUSTER ANALYSIS; GENOMICS; HUMANS; PRECISION MEDICINE;

EID: 85018828337     PISSN: 14654644     EISSN: 14684357     Source Type: Journal    
DOI: 10.1093/biostatistics/kxw039     Document Type: Article

References (35)

1. BECK, A. AND TEBOULLE, M. (2009). A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM Journal on Imaging Sciences 2, 183-202.
2. BICKEL, P. J. AND DOKSUM, K. A. (2001). Mathematical Statistics, Volume I, Chapman and Hall/CRC.
3. CHEN, M., KERKELA, R. AND FORCE, T. (2008). Mechanisms of cardiomyopathy associated with tyrosine kinase inhibitor cancer therapeutics. Circulation 118, 84-95.
4. Chen, X., LIN, Q., KIM, S., CARBONELL, J. G. AND XING, E. P. (2012). Smoothing proximal gradient method for general structured sparse regression. The Annals of Applied Statistics 6, 719-752.
5. CLEVELAND, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association 74, 829-836.
6. DEMPSTER, A. P., LAIRD, N. M. AND RUBIN, D. B. (1977). Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 39 1-38.
7. DING, C. AND HE, X. (2004). K-means clustering via principal component analysis. In Greiner R., Schuurmans D. (editors), Proceedings of the 21st International Machine Learning Conference, Banff, AB, Canada: ACM Press, Volume 69, http://ranger.uta.edu/~chqding/papers/KmeansPCA1.pdf.
8. DUDOIT, S. AND FRIDLYAND, J. (2002). A prediction-based resampling method for estimating the number of clusters in a dataset. Genome Biology 3, 1-21.
9. GHOSH, D. AND CHINNAIYAN, A.M. (2002). Mixture modelling of gene expression data from microarray experiments. Bioinformatics 18, 275-286.
10. HU, J. AND TZENG, J.-Y. (2014). Integrative gene set analysis of multi-platform data with sample heterogeneity. Bioinformatics 30, 1501-1507.
11. HUAN, J., WANG, L., XING, L., QIN, X., FENG, L., PAN, X. AND ZHU, L. (2014). Insights into significant pathways and gene interaction networks underlying breast cancer cell line MCF-7 treated with 17β-estradiol (E2). Gene 533, 346-355.
12. JOHN, B., ENRIGHT, A. J., ARAVIN, A., TUSCHL, T., SANDER, C. AND MARKS, D. S. (2004). Human microRNA targets. PLoS Biology 2, e363.
13. LOCK, E. F. AND DUNSON, D. B. (2013). Bayesian consensus clustering. Bioinformatics 29, 2610-2616.
14. LOCK, E. F., HOADLEY, K. A., MARRON, J. S. AND NOBEL, A. B. (2013). Joint and individual variation explained for integrated analysis of multiple data types. The Annals of Applied Statistics 7, 523-542.
15. MAITRA R. AND RAMLER I. P. (2009). Clustering in the presence of scatter. Biometrics 65, 341-352.
16. NESTEROV, Y. (2005). Smooth minimization of nonsmooth functions. Mathematical Programming 103, 127-152.
17. OBOZINSKI, G., TASKAR, B. AND JORDAN, M. I. (2010). Joint covariate selection and joint subspace selection for multiple classification problems. Statistics and Computing 20, 231-252.
18. OBOZINSKI, G. R., WAINWRIGHT, M. J. AND JORDAN, M. I. (2008). High-dimensional support union recovery in multivariate regression. In Koller D., Schuurmans D., Bengio Y. and Bottou L. (editors), Advances in Neural Information Processing Systems, the Neural Information Processing Systems Conference, 2008, pp. 1217-1224, https://papers.nips.cc/paper/3432-high-dimensional-support-union-recovery-in-multivariate-regression.pdf.
19. PEROU, C. M., SRLIE, T., EISEN, M. B., RIJN, MATT. AND JEFFREY, S. S.(2000). Molecular portraits of human breast tumours. Nature 406, 747-752.
20. RAMASAMY, A., MONDRY, A., HOLMES, C. C. AND ALTMAN, D. G. (2008). Key issues in conducting a metaanalysis of gene expression microarray datasets. PLoS Medicine 5, e184.
21. REY, M. AND ROTH, V. (2012). Copula mixture model for dependency-seeking clustering. In Langford J. and Pineau J. (editors), Proceedings of the 29th International Conference on Machine Learning, Omnipress, pp. 775-782. http://icml.cc/2012/papers/486.pdf.
22. REYNOLDS, A. P., RICHARDS, G., DE LA IGLESIA, B. AND RAYWARD-SMITH, V. J. (2006). Clustering rules: a comparison of partitioning and hierarchical clustering algorithms. Journal of Mathematical Modelling and Algorithms 5, 475-504.
23. SEOANE, J. A., DAY, I. N., GAUNT, T. R. AND CAMPBELL, C. (2014).A pathway-based data integration framework for prediction of disease progression. Bioinformatics 30, 838-845.
24. SHEN, R., OLSHEN, A.B. AND LADANYI, M. (2009). Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics 25, 2906-2912.
25. SHEN, R., WANG, S. AND MO, Q. (2013). Sparse integrative clustering of multiple omics data sets. The Annals of Applied Statistics 7, 269.
26. SIMON, R. (2005). Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. Journal of the National Cancer Institute 97, 866-867.
27. TIBSHIRANI, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological) 58, 267-288.
28. TIBSHIRANI, R., WALTHER, G. AND HASTIE, T. (2001). Estimating the number of data clusters via the gap statistic. Journal of the Royal Statistical Society B 63, 411-423.
29. TSENG, G. C. (2007). Penalized and weighted K-means for clustering with scattered objects and prior information in high-throughput biological data. Bioinformatics 23, 2247-2255.
30. TSENG, G. C. AND WONG, W. H. (2005). Tight clustering: a resampling-based approach for identifying stable and tight patterns in data. Biometrics 61, 10-16.
31. TSENG, G. C., GHOSH, D. AND FEINGOLD, E. (2012). Comprehensive literature review and statistical considerations for microarray meta⣳analysis. Nucleic Acids Research 40, 3785-3799.
32. VINH, N. X., EPPS, J. AND BAILEY, J. (2009). Information theoretic measures for clusterings comparison: is a correction for chance necessary? In Danyluk A. and Wagstaff K. L. (editors), Proceedings of the 26th Annual International Conference on Machine Learning, ACM Press, pp. 1073-1080. http://www.machinelearning.org/archive/icml2009/papers/10.pdf.
33. WANG, W., BALADANDAYUTHAPANI, V., MORRIS, J. S., BROOM, B. M., MANYAM, G. AND DO, K. (2012). Integrative Bayesian analysis of high-dimensional multiplatform genomics data. Bioinformatics 29, 149-159.
34. XIE, B., DING, Q., HAN, H. AND WU, D. (2013) miRCancer: a microRNA-cancer association database constructed by text mining on literature Bioinformatics 29, 638-644.
35. YOUDEN, W. J. (1950). Index for rating diagnostic tests. Cancer 3, 32-35.