Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data

Bioinformatics

Volumn 31, Issue 3, 2015, Pages 397-404

  Bastien, Philippe ^a   Bertrand, Freeric ^b   Meyer, Nicolas ^b   Maumy Bertrand, Myriam ^a  

^a L'ORÉAL   (France)

^b UNIVERSITÉ DE STRASBOURG   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; COMPUTER PROGRAM; GENE EXPRESSION PROFILING; HUMAN; INFORMATION PROCESSING; PROCEDURES; PROPORTIONAL HAZARDS MODEL; REGRESSION ANALYSIS; SAMPLE SIZE; SURVIVAL RATE;

ALGORITHMS; DATASETS AS TOPIC; GENE EXPRESSION PROFILING; HUMANS; LEAST-SQUARES ANALYSIS; PROPORTIONAL HAZARDS MODELS; REGRESSION ANALYSIS; SAMPLE SIZE; SOFTWARE; SURVIVAL RATE;

EID: 84929142356     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btu660     Document Type: Article

References (68)

1. Akritas, M.G. (1994) Nearest neighbor estimation of a bivariate distribution under random censoring. Ann. Stat., 22, 1299-1327.
2. Alizadeh, A.A. et al. (2000) Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature, 11, 403-503.
3. Antoniadis, A. et al. (2010) The Dantzig selector in Cox's proportional hazards model. Scand. Stat. Theory Appl., 37, 531-552.
4. Bair, E. et al. (2006) Prediction by supervised principal components. J. Am. Stat. Assoc, 101, 119-137.
5. Bastien, P. (2008) Deviance residual based PLS regression for censored data in high dimensional data. Chemom. Intell. Lab. Syst., 91, 78-86.
6. Bastien, P. et al. (2005) PLS generalised linear regression. Comput. Stat. Data Anal., 48, 17-46.
7. Bastien, P. and Tenenhaus, M. (2001) PLS generalised linear regression, Application to the analysis of life time data. In PLS and Related Methods, Proceedings of the PLS'01 International Symposium. CISIA-CERESTA Editeur, Montreuil, pp. 131-140.
8. Beer, D.G. et al. (2002) Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat. Med., 8, 816-824.
9. Bennett, K.P. and Embrecht, MJ. (2003) An optimization perspective on kernel partial least squares regression. Adv. Learn. Theory, 190, 227-250.
10. Bhattacharjee, A. et al. (2001) Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma sub-classes. Proc. Natl Acad. Sci. USA, 98, 13790-13795.
11. Bøvelstad, H.M. et al. (2007) Predicting survival from microarray data-A comparative study. Bioinformatics, 23, 2080-2087.
12. Chun, H. and Keles, S. (2010) Sparse partial least squares regression for simultaneous dimension reduction and variable selection. J. R. Stat. Soc. B, 72, 3-25.
13. Collett, D. (1994) Modelling Survival Data in Medical Research. Chapman and Hall, London.
14. Cox, D.R. (1972) Regression models and life tables. J. R. Stat. Soc. B, 74, 187-220.
15. De Jong, S. and ter Braak, C. (1994) Comments on the PLS kernel algorithm. J. Chemom., 8, 169-174.
16. Efron, B. et al. (2004) Least angle regression. Ann. Stat., 32, 407-451.
17. Fan, X. et al. (2002) SAS for Monte Carlo Studies: A Guide for Quantitative Researchers. SAS publishing, Cary, NY.
18. Fan, J. et al. (2010) High-dimensional variable selection for Cox's proportional hazards model. Borrowing Strength, 6, 70-86.
19. Fan, J. and Li, R. (2002) Variable selection for Cox's proportional hazards model and frailty model. Ann. Stat., 30, 74-99.
20. Friedman, J. et al. (2010) A note on the group lasso and a sparse group lasso. arXiv:1001.0736.
21. Friedman, J.H. and Popescu, B.E. (2004) Gradient directed regularization. In: Working Paper, Department of Statistics, Stanford University.
22. Fu, WJ. (1998) Penalized regression: the bridge versus the LASSO. J. Comput. Graph. Stat., 7, 397-416.
23. Garber, M. et al. (2001) Diversity of gene expression in adenocarcinoma of the lung. Proc. Natl Acad. Sci. USA, 98, 13784-13789.
24. Garthwaite, P.H. (1994) An interpretation of partial least squares. J. Am. Stat. Assoc, 89, 122-127.
25. Goeman, JJ. (2010) L1 Penalized estimation in the cox proportional hazards model. Biometric. J., 52, 70-84.
26. Gonen, M. and Heller, G. (2005) Concordance probability and discriminatory power in proportional hazards regression. Biometrika, 92, 1809-2005.
27. Gui, J. and Li, H. (2005) Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with application to microarray gene expression data. Bioinformatics, 21, 3001-3008.
28. Harrell, F.E. et al. (1996) Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing error. Stat. Med., 15, 361-387.
29. Heagerty, PJ. et al. (2000) Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics, 56, 337-344.
30. Heagerty, PJ. and Zheng, Y. (2005) Survival model predictive accuracy and ROC curves. Biometrics, 61, 92-105.
31. Jolliffe, I. et al. (2003) A modified principal component technique based on the lasso. J. Comput. Graph. Stat., 12, 531-547.
32. Kaiser, H.F. and Dickman, K. (1962) Sample and population score matrices and sample correlation matrices from an arbitrary population correlation matrix. Psychometrika, 27, 179-182.
33. Kim, J. et al. (2008) A gradient-based optimization algorithm for lasso. J. Comput. Graph. Stat., 17, 994-1009.
34. Kramer, N. (2007) An overview on the shrinkage properties of partial least squares regression. Comput. Stat., 22, 249-273.
35. Lambert-Lacroix, S. and Letue, F. (2011) Partial least squares and cox model with application to gene expression. Technical report. http://sites.uclouvain. be/IAP-Stat-Phase-V-VI/PhaseVI/publications-2011/TR/Lambert-LacroixLet-ueIAP.pdf
36. Langfelder, P. et al. (2013) When is hub gene selection better than standard metaanalysis? PloS One, 8, e61505.
37. Li, H. and Gui, J. (2004) Partial cox regression analysis for high-dimensional micro-array gene expression data. Bioinformatics, 20, 208-215.
38. Li, L. (2006) Survival prediction of diffuse large-B-cell lymphoma based on both clinical and gene expression information. Bioinformatics, 22, 466-471.
39. Lindgren, F. et al. (1993) The kernel algorithm for PLS. J. Chemom., 7, 45-59.
40. Magidson, J. and Wassmann, K. (2010) The role of proxy genes in predictive models: an application to early detection of prostate cancer. In: Joint Statistical Meetings Proceedings. American Statistical Association, Vancouver, pp. 2739-2753.
41. Marx, B.D. (1996) Iteratively reweighted partial least squares estimation for generalized linear regression. Technometrics, 38, 374-381.
42. Metzeler, K.H. et al. (2008) An 86-probe-set gene-expression signature predicts survival in cytogenetically normal acute myeloid leukemia. Blood, 112, 4193-4201.
43. Nguyen, D.V. and Rocke, D. (2002) Partial least squares proportional hazard regression for application to DNA microarray survival data. Bioinformatics, 18, 1625-1632.
44. Park, M.Y. and Hastie, T. (2007) L1 regularization path algorithm for generalized linear models. J. R. Stat. Soc. B, 69, 659-677.
45. Rannar, S. et al. (1994) A PLS kernel algorithm for data sets with many variables and fewer objects. Part I: theory and algorithm. J. Chemom., 8, 111-125.
46. Romain, B. et al. (2010) Allelotyping identification of genomic alterations in rectal chromosomally unstable tumors without preoperative treatment. BMC Cancer, 10, 561.
47. Rosenwald, A. et al. (2002) The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N. Engl. J. Med., 346, 1937-1947.
48. Rosipal, R. and Trejo, L.J. (2001) Kernel partial least squares regression in reproducing kernel hilbert space. J. Mach. Learn. Res., 2, 97-123.
49. Schmid, M. et al. (2011) A robust alternative to the Schemper-Henderson estimator of prediction error. Biometrics, 67, 524-535.
50. Segal, M.R (2006) Microarray gene expression data with linked survival pheno-types: diffuse large-Bcell lymphoma revisited. Biostatistics, 7, 268-285.
51. Simon, N. et al. (2011) Regularization paths for cox's proportional hazards model via coordinate descent. J. Stat. Softw., 39, 1-13.
52. Simon, N. et al. (2013) A sparse-group Lasso. J. Comput. Graph. Stat., 22, 231-245.
53. Sohn, I. et al. (2009) Gradient lasso for cox proportional hazards model. Bioinformatics, 25, 1775-1781.
54. Tenenhaus, A. et al. (2007) Kernel logistic PLS: a tool for supervised nonlinear dimensionality reduction and binary classification. Comput. Stat. Data Anal., 51, 4083-4100.
55. Tenenhaus, M. (1998) La regression PLS. Technip, Paris.
56. Tenenhaus, M. (1999) La regression logistique PLS. In Proceedings of the 32emes journees de Statistique de la Societe francaise de Statistique. Fes, pp. 721-723.
57. Tibshirani, R. (1996) Regression shrinkage and selection via the Lasso. J. R. Stat. Soc. B, 58, 267-288.
58. Tibshirani, R. (1997) The lasso method for variable selection in the Cox model. Stat. Med., 16, 385-395.
59. Tibshirani, R. (2009) Univariate shrinkage in the cox model for high dimensional data. Stat. Appl. Genet. Mol. Biol., 8, 1-18.
60. van Wieringen, N. et al. (2009) Survival prediction using gene expression data: a review and comparison. Comput. Stat. Data Anal., 53, 1590-1603.
61. Wang, Y. et al. (2005) Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet, 365, 671-679.
62. Wang, S. et al. (2009) Hierarchically penalized Cox regression with grouped variables. Biometrika, 96, 307-322.
63. Wold, H. (1966) Estimation of principal components and related models by iterative least squares. Krishnaiah, P.R. (ed.) (1982) Multivariate Analysis. Academic Press, New York, NY, pp. 391-420.
64. Wold, S. et al. (1983) The multivariate calibration problem in chemistry solved by the PLS method. In: Ruhe, A. and Kastrøm, B. (eds) Proceeding Conference Matrix Pencils, March 1982, Lecture Notes in Mathematics. Springer Verlag, Heidelberg, pp. 286-293.
65. Xu, R and O'Quigley, J. (1999) A R type measure of dependence for proportional hazards models. J. Nonparametr. Stat., 12, 83-107.
66. Zhang, H.H. and Lu, W. (2007) Adaptive lasso for Cox's proportional hazards model. Biometrika, 94, 691-703.
67. Zou, H. and Hastie, T. (2005) Regularization and variable selection via the elastic net. J. R. Stat. Soc. B, 67, 301-320.
68. Zou, H. et al. (2006) Sparse principal component analysis. J. Comput. Graph. Stat., 15, 265-286.