An Extension of PPLS-DA for Classification and Comparison to Ordinary PLS-DA

PLoS ONE

Volumn 8, Issue 2, 2013, Pages

  Telaar, Anna ^a   Liland, Kristian Hovde ^b   Repsilber, Dirk ^a   Nürnberg, Gerd ^a  

^a LEIBNIZ INSTITUTE FOR FARM ANIMAL BIOLOGY FBN   (Germany)

^b NORWEGIAN UNIVERSITY OF LIFE SCIENCES   (Norway)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ARTICLE; CLASSIFICATION; CONTROLLED STUDY; CORRELATION ANALYSIS; ERROR; GENE EXPRESSION; INTERMETHOD COMPARISON; LINEAR DISCRIMINANT ANALYSIS; LINEAR KERNEL; LINEAR SYSTEM; PARTIAL LEAST SQUARES DISCRIMINANT ANALYSIS; POWERED PARTIAL LEAST SQUARES DISCRIMINANT ANALYSIS; PREDICTION; SIMULATION; STATISTICAL ANALYSIS; SUPPORT VECTOR MACHINE;

DISCRIMINANT ANALYSIS; GENE EXPRESSION PROFILING; LEAST-SQUARES ANALYSIS;

EID: 84873660712     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0055267     Document Type: Article

References (22)

1. Wold S, Albano C, Dunn II W, Esbensen K, Hellberg S, et al. (1983) Pattern recognition: Finding and using regularites in multivariate data, applied Sciences publ. London. 147-188.
2. Martens SWH, Wold H (1983) The multivariate calibration problem in chemistry solved by the pls method. Proc Conf Matrix Pencils (ARuhe, BKaagstroem, eds) March 1982 Lecture notes in Mathematics: 286-293.
3. Barker M, Rayens W, (2003) Partial least squares for discrimination. Journal of Chemometrics 17(3): 166-173.
4. Nocairi H, Qannari EM, Vigneau E, Bertrand D, (2005) Discrimination on latent components with respect to patterns. application to multicollinear data. Computational Statistics & Data Analysis 48 (1): 139-147.
5. Liland KH, Indahl U, (2009) Powered partial least squares discriminant analysis. Journal of Chemometrics 23: 7-18.
6. Indahl UG, (2005) A twist to partial least squares regression. Journal of Chemometrics 19: 32-44.
7. Telaar A, Repsilber D, Nürnberg G (2011) Biomarker Discovery: Classification using pooled samples- a simulation study. Computational Statistics.
8. Indahl UG, Martens H, Næs T, (2007) From dummy regression to prior probabilities in pls-da. Journal of Chemometrics 21: 529-536.
9. Indahl UG, Liland KH, Næs T, (2009) Canonical partial least squares- a unified pls approach to classification and regression problems. Journal of Chemometrics 23: 495-504.
10. Dimitriadou E, Hornik K, Leisch F, Meyer D, Weingessel A (2009) e1071: Misc functions of the department of statistics (e1071). TU Wien.
11. Dettling M, (2004) Bagboosting for tumor classification with gene expression data. Bioinformatics 20: 3583-3593.
12. Sæbø S, Almøy T, Aarøe J, Aastveit AH, (2008) ST-PLS: a multi-directional nearest shrunken centroid type classifier via PLS. Journal of Chemometrics 20: 54-62.
13. Belsley DA, Kuh E, Welsch RE (1980) Regression Diagnostics: Identifying Inuential Data and Sources of Collinearity. John Wiley & Sons.
14. Helland IS, Almøy T, (1994) Comparison of prediction methods when only a few components are relevant. Journal of the American Statistical Association 89: 583-591.
15. Storey J, Tibshirani R, (2003) Statistical signilficance for genomewide studies. Proc Natal Acad Sci 100: 9440-5.
16. Dudoit S, Fridlyand J, Speed TP, (2002) Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association 97: 77-87.
17. van't Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, et al. (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415: 530-536.
18. Dettling M, Buehlmann P (2003) Boosting for tumor classification with gene expression data. Bioinformatics: 1061-1069.
19. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, et al. (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286: 531-537.
20. Shipp MA, Ross KN, Tamayo P, Weng AP, Kutok JL, et al. (2002) Diffuse large b-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med 8: 68-74.
21. Singh D, Febbo PG, Ross K, Jackson DG, Manola J, et al. (2002) Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1: 203-209.
22. Lapointe J, Li C, Higgins JP, van de Rijn M, Bair E, et al. (2004) Gene expression profiling identifies clinically relevant subtypes of prostate cancer. Proc Natl Acad Sci U S A 101: 811-816.