Partial least squares: A versatile tool for the analysis of high-dimensional genomic data

Briefings in Bioinformatics

Volumn 8, Issue 1, 2007, Pages 32-44

  Boulesteix, Anne Laure ^a   Strimmer, Korbinian ^b  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^b UNIVERSITY OF MUNICH   (Germany)

Author keywords

Classification; Dimension reduction; Gene expression; High dimensional genomic data; Partial least squares (PLS)

Indexed keywords

ANTINEOPLASTIC AGENT; DNA BINDING PROTEIN; TRANSCRIPTION FACTOR; TRANSCRIPTOME;

ARTICLE; BIOINFORMATICS; CANCER SURVIVAL; ENZYME ACTIVITY; GENETIC ANALYSIS; GENETIC IDENTIFICATION; GENETIC MODEL; HUMAN; MALIGNANT NEOPLASTIC DISEASE; MULTIVARIATE ANALYSIS; NONHUMAN; PARTIAL LEAST SQUARES REGRESSION; STATISTICAL ANALYSIS; SYSTEMS THEORY; TREATMENT OUTCOME; TUMOR CLASSIFICATION; UNIVARIATE ANALYSIS;

ANIMALS; COMPUTATIONAL BIOLOGY; DATA INTERPRETATION, STATISTICAL; GENOMICS; HUMANS; LEAST-SQUARES ANALYSIS; MODELS, STATISTICAL; MULTIVARIATE ANALYSIS; SOFTWARE; SURVIVAL ANALYSIS;

EID: 33846515112     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bbl016     Document Type: Article

References (65)

1. Wold H. Estimation of principal components and related models by iterative least squares. In: Krishnaiah PR (ed). Multivariate Analysis. New York: Academic Press, 1966; 391-420.
2. Wold H. Nonlinear Iterative Partial Least Squares (NIPALS) modeling: some current developments. In: Krishnaiah PR (ed). Multivariate Analysis. New York: Academic Press, 1973;383-407.
3. Wold H. Path models with latent variables: The NIPALS approach. In: Blalock HM (ed). Quantitative Sociology: International Perspectives on Mathematical and Statistical Model Building. New York: Academic Press, 1975.
4. Wold S, Ruhe A, Wold H, et al. Collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses. SIAM J Sci Comput Stat 1984;5:735-43.
5. Martens H. Reliable and relevant modeling of real world data: A personal account of the development of PLS regression. Chemom Intell Lab Syst 2001;58:85-95.
6. Wold S. Personal memories of the early PLS development. Chemom Intell Lab Syst 2001;58:83-4.
7. Phatak A, Dehoog F. Exploiting the connection between PLSR, Lanczos, and conjugate gradients: Alternative proofs of some properties of PLSR. J Chemom 2002;16:361-7.
8. Helland I. On the structure of Partial Least Squares. Comm Stat Simul Comp 1988;17:581-607.
9. Stone M, Brook RJ. Continuum regression: Cross-validated sequentially constructed prediction embracing ordinary least squares, partial least squares and principal component regression. J Roy Stat Soc B 1990;52:237-69.
10. Frank IE, Friedman JH. A statistical view of some chemometrics regression tools. Technometrics 1993;35: 109-35.
11. Garthwaite PH. An interpretation of partial least squares. J Am Stat Assoc 1994;89:122-7.
12. Martens H, Naes T. Multivariate Calibration. New York: Wiley, 1989.
13. Braak CJF, de Jong S. The objective function of partial least squares. J Chemom 1998;12:41-54.
14. Jong S. SIMPLS: An alternative approach to partial least squares regression. Chemom Intell Lab Syst 1993;18:251-63.
15. Rao CR. Linear Statistical Inference and its Application. New York: Wiley, 1993.
16. Boulesteix AL, Strimmer K. Predicting transcription factor activities from combined analysis of microarray and ChIP data: A partial least squares approach. Theor Biol Med Model 2005;2:23.
17. Datta S. Exploring the relationships in gene expressions: A partial least squares approach. Gene Expression 2001;9: 257-64.
18. Bras LP, Menezes JC. Dealing with gene expression missing data. IEE Syst Biol 2006;153:105-19.
19. Nguyen DV, Wang N, Caroll RJ. Evaluation of missing value estimation for microarray data. J Data Sci 2004;2: 347-70.
20. Huang X, Pan W, Park S, et al. Modeling the relationship between LVAD support time and gene expression changes in the human heart by penalized partial least squares. Bioinformatics 2004;20:888-94.
21. Johansson D, Lindgren P, Berglund A. A multivariate approach applied to microarray data for identification of genes with cell cycle-coupled transcription. Bioinformatics 2003;19:467-73.
22. Clementi M, Clementi S, Cruciani G, et al. Robust multivariate statistics and the prediction of protein secondary structure content. Protein Eng 1997;10:747-9.
23. Barker M, Rayens W. Partial least squares for discrimination. J Chemom 2003;17:166-73.
24. Man MZ, Dyson G, Johnson K, et, al. Evaluating methods for classifying expression data. J Biopharm Stat 2004;14:1065-84.
25. Huang X, Pan W, Grindle S, et al. A comparative study of discriminating human heart failure etiology using gene expression profiles. BMC Bioinformatics 2005;6:205.
26. Huang X, Pan W. Linear regression and two-class classification with gene expression data. Bioiformatics 2003;19:2072-8.
27. Golub TR, Slonim DK, Tamayo P, et al. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 1999;286:531-7.
28. Alon U, Barkai DA, Notterman K. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci 1999;96:6745-50.
29. Perez-Enciso M, Tenenhaus M. Prediction of clinical outcome with microarray data: A partial least squares approach. Hum Genet 2003;112:581-92.
30. Musumarra G, Barresi V, Condorelli DF, et al. Potentialities of multivariate approaches in genome-based cancer research: Identification of candidate genes for new diagnostics by PLS discrimmant analysis. J Chemom 2004; 18:125-32.
31. Ross DT, Scherf U, Eisen MB, et al. Systematic variation in gene expression patterns in human cancer cell lines. Nat Genet 2000;24:227-34.
32. Alaiya AA, Franzen B, Hagman A, et al. Classification of human ovarian tumors using multivariate data analysis of polypeptide expression patterns. Int J Cancer 2000;86:731-6.
33. Musumarra G, Condorelli DF, Scire S, et al. Shortcuts in genome-scale cancer pharmacology. research from multivariate analysis of the National Cancer Institute gene expression data base. Biochem Pharmacol 2001;62:547-53.
34. Cho JH, Lee D, Park JH, et al. Optimal approach for classification of acute leukemia subtypes based on gene expression data. Biotech Progress 2002;18:847-54.
35. Tan Y, Shi L, Tong W, et al. Multi-class tumor classification by discriminant partial least squares using microarray gene expression data and assessment of classification models. Comput Biol Chem 2004;28:235-44.
36. Modlich O, Prisack HB, Munnes M, et al. Predictors of primary breast cancers responsiveness to preoperative epirubicin// cyclophosphamide-based chemotherapy: Translation of microarray data into clinically useful predictive signatures. J Transl Med 2005;3:32.
37. Huang X, Pan W, Han X, et al. Borrowing information from relevant microarray studies for sample classification using weighted partial least squares. Comput Biol Chem 2005; 29:204-11.
38. Nguyen DV, Rocke D. Tumor classification by partial least squares using microarray gene expression data. Bioinformatics 2002;18:39-50.
39. Nguyen DV, Rocke D. Multi-class cancer classification via partial least squares with gene expression profiles. Bioinformatics 2002;18:1216-26.
40. Boulesteix AL. PLS dimension reduction for classification with high-dimensional microarray data. Stat Appl Genet Mol Biol 2004;3:33.
41. Dai JJ, Lieu L, Rocke D. Dimension reduction for classification with gene expression data. Stat Appl Genet Mol Biol 2006;5:6.
42. Tibshirani R, Hastie T, Narasimhan B, et al. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci 2002;99:6567-72.
43. Musumarra G, Barresi V, Condorelli DF, et al. A bioinformatics approach to the identification of candidate genes for the development of new cancer diagnostics. Biol Chem 2003;384:321-7.
44. Cox DR. Regression models and life-tables (with discussion). J Roy Stat Soc B 1972;34:187-220.
45. Nguyen DV, Rocke D. Partial least squares proportional hazards regression for application to DNA microarray survival data. Bioinformatics 2002;18:1625-32.
46. 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.
47. Sorlie T, Perou CM, Tibshirani R, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci 2001;98: 10869-74.
48. Nguyen DV. Partial least squares dimension reduction for microarray gene expression data with a censored response. Math Biosci 2005;193:119-37.
49. Park PJ, Tian L, Kohane IS. Linking gene expression data with patient survival times using partial least squares. Bioinformatics 2002;20:208-15.
50. Li H, Gui J. Partial Cox regression analysis for high-dimensional microarray gene expression data. Bioinformatics 2004;20:208-15.
51. Fort G, Lambert-Lacroix S. Classification using partial least squares with penalized logistic regression. Bioinformatics 2005;21:1104-11.
52. Ding B, Gentleman R. Classification using penalized partial least squares. J Comput Graph Stat 2005;14:280-98.
53. Durand JF. Local polynomial additive rLgbression through PLS and splines: PLSS. Chemom Intell Lab Syst 2001;58: 235-46.
54. Bastien P. PLS-Cox model: Application to gene expression data. In: Proceedings COMPSTAT'04. Springer: Physica-Verlag, 2004;655-62.
55. Bastien P, Esposito-Vinzi V, Tenenhaus M. PLS generalized linear regression. Comput Stat Data Anal 2005;48:17-46.
56. Nguyen DV, Rocke D. On partial least squares dimension reduction for microarray-based classification: A simulation study. Comput Stat Data Anal 2004;46:407-25.
57. Perez-Enciso M, Toro MA, Tenenhaus M, et al. Combining gene expression and molecular marker information for mapping complex trait genes: A simulation, study. Genetics 2003;164:1597-606.
58. Marx BD. Iteratively reweighted partial least squares. Technometrics 1996;38:374-81.
59. Green P. Iteratively reweighted least squares for maximum likelihood estimation and some robust and resistant alternatives. J Roy Stat Soc B 1984;46:149-92.
60. Albert A, Anderson J. On the existence of maximum likelihood estimates in logistic regression models. Biometrika 1984;71:1-10.
61. Firth D. Bias reduction of maximum likelihood estimates. Biometrika 1993;80:27-38.
62. Heinze G, Schemper M. A solution to the problem of separation in logistic regression. Stat Med 2002;21:2409-19.
63. Singh D, Febbo PG, Ross K, et al. Gene expression correlates of clinical prostate cancer behaviour. Cancer Cell 2002;1:203-9.
64. Whitehead J. Fitting Cox's regression model to survival data using GLIM. J Roy Stat Soc C 1980;29:268-75.
65. Bhattacharjee A, Richards WG, Staunton J, et al. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci 2001;98:13790-5.