Classification using generalized partial least squares

Journal of Computational and Graphical Statistics

Volumn 14, Issue 2, 2005, Pages 280-298

  Ding, Belying ^a   Gentleman, Robert ^b  

^a AMGEN INC   (United States)

^b Fred Hutchinson Cancer Research Center   (United States)

Author keywords

(Quasi)separation; Cross validation; Firth's procedure; Gene expression; Iteratively reweighted partial least squares; Two stage PLS

Indexed keywords

EID: 20744452472     PISSN: 10618600     EISSN: None     Source Type: Journal    
DOI: 10.1198/106186005X47697     Document Type: Article

References (37)

1. Albert, A., and Anderson, J. A. (1984), "On the Existence of Maximum Likelihood Estimates in Logistic Regression Models," Biometrika, 71, 1-10.
2. Alon, U., Barkai, N., Notterman, D., Gish, K., Ybarra, S., Mack, D., and Levine, A. (1999), "Broad Patterns of Gene Expression Revealed by Clustering Analysis of Tumor and Normal Colon Tissues Probed by Oligonucleotide Arrays," in Proceedings of the National Academy of Sciences, 96, pp. 6745-6750.
3. Ambroise, C., and McLachlan, G. (2002), "Selection Bias in Gene Extraction on the Basis of Microarray Gene-Expression Data," PNAS, 99, 6562-6566.
4. Breiman, L. (2001), "Random Forests," Machine Learning, 45, 5-32.
5. _ (2002), Manual on Setting Up, Using, and Understanding Random Forests V3.1, Dept. of Statistics, UC Berkeley.
6. Chen, Y., Dougherty, E., and Bitterner, M. (1997), "Ratio-Based Decisions and the Quantitative Analysis of cDNA Microarray Images," Journal of Biomedical Optics, 2, 364-374.
7. Dudoit, S., Fridlyand, J., and Speed, T. P. (2002), "Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data," Journal of the American Statistical Association, 97, 77-87.
8. Eilers, P. H., Boer, J. M., van Ommen, G.-J., and van Houwelingen, H. C. (2001), "Classification of Microarray Data with Penalized Logistic Regression," in Proceedings of SPIE, Progress in Biomedical Optics and Image, Volume 4266, pp. 187-198.
9. Fahrmeir, L., and Tutz, G. (2001). Multivariate Statistical Modelling Based on Generalized Linear Models, New York: Springer Series in Statistics.
10. Firth, D. (1992a), "Bias Reduction, the Jeffreys Prior and Glim," in Advances in GLIM and Statistical Modelling, eds. L. Fahrmeir, B. Francis, R. Gilchrist, and G. Tutz, New York: Springer-Verlag, pp. 91-100.
11. Firth, D. (1992b), "Generalized Linear Models and Jeffreys Priors: An Iterative Weighted Least-Squares Approach," in Computational Statistics (vol. 1), eds. Y. Dodge, and J. Whittaker, Vienna: Physica-Verlag, pp. 553-557.
12. _ (1993), "Bias Reduction of Maximum Likelihood Estimates," Biometrika, 80, 27-38. Correction: 82, 667.
13. Fisher, R. (1936), "The Use of Multiple Measurements in Taxonomic Problems," Annals of Eugenics, 7, 179-88.
14. Fort, G., and Lambert-Lacroix, S. (2003), "Classification Using Partial Least Squares With Penalized Logistic Regression," IAP-statistics, TR0331.
15. Frank, I. E., and Friedman, J. H. (1993), "A Statistical View of Some Chemometrics Regression Tools" (with discussion), Technometrics, 35, 109-48.
16. Furey, T., Cristianini, N., Duffy, N., Bednarski, D., Schummer, M., and Haussler, D. (2000), "Support Vector Machine Classification and Validation of Cancer Tissue Samples using Microarray Expression Data," Bioinformatics, 16, 906-914.
17. Geladi, P., and Kowalski, B. (1986), "Partial Least Squares Regression: A Tutorial," Analytica Chimica Acta, 185, 1-17.
18. Guyon, I., Weston, J., Barnhill, S., and Vapnik, V. (2002), "Gene Selection for Cancer Classification Using Support Vector Machines," Machine Learning, 46, 389-422.
19. Heinze, G., and Schemper, M. (2002), "A Solution to the Problem of Separation in Logistic Regression," Statistics in Medicine, 21, 2409-2419.
20. Helland, I. S. (1988), "On the Structure of Partial Least Squares Regression," Communications in Statistics, Part B - Simulation and Computation, 17, 581-607.
21. Höskuldsson, A. (1988), "PLS Regression Methods," Journal of Chemometrics, 2, 211-228.
22. Kruskal, J. (1978), "Factor Analysis and Principal Components I. Bilinear Methods," in International Encyclopedia of Statistics, London: Collier Macmillan.
23. Martens, H., and Naes, T. (1989), Multivariate Calibration, New York: Wiley.
24. Marx, B. D. (1996), "Iteratively Reweighted Partial Least Squares Estimation for Generalized Linear Regression," Technometrics, 38, 374-381.
25. Massy, W. F. (1965), "Principal Components Regression in Exploratory Statistical Research," Journal of the American Statistical Associaton, 60, 234-246.
26. McCullagh, P., and Nelder, J. (1989), Generalized Linear Models (2nd ed.), New York: Chapman and Hall.
27. Newton, M., Kendziorski, C., Richmond, C., Blatterner, F., and Tsui, K. (2001), "On Differential Variability of Expression Ratios: Improving Statistical Inference About Gene Expression Changes from Microarray Data," Journal of Computational Biology, 8, 37-52.
28. Nguyen, D. V., and Rocke, D. M. (2002a), "Multi-class Cancer Classification via Partial Least Squares With Gene Expression Profiles," Bioinformatics, 18, 1216-1226.
29. _ (2002b), "Tumor Classification by Partial Least Squares using Microarray Gene Expression Data," Bioinformatics, 18, 39-50.
30. Ripley, B. (1996), Pattern Recognition and Neural Networks, New York: Cambridge University Press.
31. Ross, D., Scherf, U., Eisen, M., Perou, C., Rees, C.and Spellman, P., Iyer, V., Jeffrey, S., Van de Rijn, M., Waltham, M., Pergamenschikov, A., Lee, J., Lashkari, D., Shalon, D.and Myers, T., Weinstein, J., Botstein, D., and Brown, P. (2000), "Systematic Variation in Gene Expression Patterns in Human Cancer Cell Lines," Nature Genetics, 24, 227-235.
32. Santner, T. J., and Duffy, D. E. (1986), "A Note on A. Albert and J. A. Anderson's Conditions for the Existence of Maximum Likelihood Estimates in Logistic Regression Models," Biometrika, 73, 755-758.
33. Scherf, U., Ross, D. T., Waltham, M., Smith, L. H., Lee, J. K., Tanabe, L., Kohn, K. W., Reinhold, W. C., Myers, T. G., Andrews, D. T., Scudiero, D. A., Eisen, M. B., Sausville, E. A., Pommier, Y., Botstein, D., Brown, P. O., and Weinstein, J. N. (2000), "A Gene Expression Database for the Molecular Pharmacology of Cancer Nature Genetics," Nature Genetics, 236-244.
34. Smith, J. W., Everhart, J. E., Dickson, W. C., and Knowler, W. C., and Johannes, R. S. (1988), "Using the Adap Learning Algorithm to Forecast the Onset of Diabetes Mellitus," in Proceedings of the Symposium on Computer Applications in Medical Care (Washington, 1988), ed. R. A. Greenes, IEEE Computer Society Press, pp. 261-265.
35. Venables, W., and Ripley, B. (2002), Modern Applied Statistics with S (4th ed.), New York: Springer.
36. Wang, C.-Y., Chen, C.-T., Chiang, C.-P., Young, S.-T., Chow, S.-N., and Chiang, H. K. (1999), "A Probability-Based Multivariate Statistical Algorithm for Autofluoresence Spectroscopic Identification of Oral Carcinogenesis," Photochemistry and Photobiology, 69, 471-477.
37. Wold, H. (1975), "Soft Modeling by Latent Variables: The Nonlinear Iterative Partial Least Squares Approach," in Perspectives in Probability and Statistics, Papers in Honour of M.S. Bartlett, ed. J. Gani, London: Academic Press.