Sparse canonical methods for biological data integration: application to a cross-platform study
Tóm tắt
In the context of systems biology, few sparse approaches have been proposed so far to integrate several data sets. It is however an important and fundamental issue that will be widely encountered in post genomic studies, when simultaneously analyzing transcriptomics, proteomics and metabolomics data using different platforms, so as to understand the mutual interactions between the different data sets. In this high dimensional setting, variable selection is crucial to give interpretable results. We focus on a sparse Partial Least Squares approach (sPLS) to handle two-block data sets, where the relationship between the two types of variables is known to be symmetric. Sparse PLS has been developed either for a regression or a canonical correlation framework and includes a built-in procedure to select variables while integrating data. To illustrate the canonical mode approach, we analyzed the NCI60 data sets, where two different platforms (cDNA and Affymetrix chips) were used to study the transcriptome of sixty cancer cell lines. We compare the results obtained with two other sparse or related canonical correlation approaches: CCA with Elastic Net penalization (CCA-EN) and Co-Inertia Analysis (CIA). The latter does not include a built-in procedure for variable selection and requires a two-step analysis. We stress the lack of statistical criteria to evaluate canonical correlation methods, which makes biological interpretation absolutely necessary to compare the different gene selections. We also propose comprehensive graphical representations of both samples and variables to facilitate the interpretation of the results. sPLS and CCA-EN selected highly relevant genes and complementary findings from the two data sets, which enabled a detailed understanding of the molecular characteristics of several groups of cell lines. These two approaches were found to bring similar results, although they highlighted the same phenomenons with a different priority. They outperformed CIA that tended to select redundant information.
Tài liệu tham khảo
Wold H: Multivariate Analysis. Edited by: krishnaiah pr. 1966, Academic Press, New York, Wiley
Hotelling H: Relations between two sets of variates. Biometrika. 1936, 28: 321-377.
Krämer N: An overview of the shrinkage properties of partial least squares regression. Computational Statistics. 2007, 22 (2): 249-273. 10.1007/s00180-007-0038-z.
Chun H, Keles S: Sparse Partial Least Squares Regression with an Application to Genome Scale Transcription Factor Analysis. Tech rep. 2007, Department of Statistics, University of Wisconsin, Madison, USA
Bylesjö M, Eriksson D, Kusano M, Moritz T, Trygg J: Data integration in plant biology: the O2PLS method for combined modeling of transcript and metabolite data. The Plant Journal. 2007, 52: 1181-1191.
Vijayendran C, Barsch A, Friehs K, Niehaus K, Becker A, Flaschel E: Perceiving molecular evolution processes in Escherichia coli by comprehensive metabolite and gene expression profiling. Genome Biology. 2008, 9 (4): R72-10.1186/gb-2008-9-4-r72.
Tibshirani R: Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B. 1996, 58: 267-288.
Zou H, Hastie T: Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society Series B. 2005, 67 (2): 301-320. 10.1111/j.1467-9868.2005.00503.x.
Lê Cao KA, Rossouw D, Robert-Granié C, Besse P: A Sparse PLS for Variable Selection when Integrating Omics data. Stat Appl Genet Mol Biol. 2008, 7: Article 35-
Waaijenborg S, de Witt Hamer V, Philip C, Zwinderman A: Quantifying the Association between Gene Expressions and DNA-Markers by Penalized Canonical Correlation Analysis. Stat Appl Genet Mol Biol. 2008, 7 (1): Article3-
Doledec S, Chessel D: Co-inertia analysis: an alternative method for studying species-environment relationships. Freshwater Biology. 1994, 31 (3): 277-294. 10.1111/j.1365-2427.1994.tb01741.x.
Culhane A, Perriere G, Higgins D: Cross-platform comparison and visualisation of gene expression data using co-inertia analysis. BMC Bioinformatics. 2003, 4: 59-10.1186/1471-2105-4-59.
Gittins R: Canonical Analysis: A Review with Applications in Ecology. 1985, Springer-Verlag
González I, Déjean S, Martin PGP, Baccini A: CCA: An R Package to Extend Canonical Correlation Analysis. Journal of Statistical Software. 2008, 23 (12):
Vinod HD: Canonical Ridge and Econometrics of Joint Production. Journal of Econometrics. 1976, 4 (2): 147-166. 10.1016/0304-4076(76)90010-5.
Combes S, González I, Déjean S, Baccini A, Jehl N, Juin H, Cauquil L, Gabinaud B, Lebas F, Larzul C: Relationships between sensorial and physicochemical measurements in meat of rabbit from three different breeding systems using canonical correlation analysis. Meat Science. 2008,
Wold S, Eriksson L, Trygg J, Kettaneh N: The PLS method-partial least squares projections to latent structures-and its applications in industrial RDP (research, development, and production). Tech rep. 2004, Umea University
de Jong S: SIMPLS: An alternative approach to partial least squares regression. Chemometrics and Intelligent Laboratory Systems. 1993, 18: 251-263. 10.1016/0169-7439(93)85002-X.
Lorber A, Wangen L, Kowalski B: A theoretical foundation for the PLS algorithm. Journal of Chemometrics. 1987, 1 (19–31): 13-
Tenenhaus M: La régression PLS: théorie et pratique. 1998, Editions Technip
Wegelin J: A survey of Partial Least Squares (PLS) methods, with emphasis on the two-block case. Tech Rep 371. 2000, Department of Statistics, University of Washington, Seattle
Zou H, Hastie T, Tibshirani R: Sparse principal component analysis. Journal of Computational and Graphical Statistics. 2006, 15 (2): 265-286. 10.1198/106186006X113430.
Shen H, Huang JZ: Sparse Principal Component Analysis via Regularized Low Rank Matrix Approximation. Journal of Multivariate Analysis. 2008, 99: 1015-1034. 10.1016/j.jmva.2007.06.007.
Robert P, Escoufier Y: A unifying tool for linear multivariate statistical methods: the RV-coefficient. Applied Statistics. 1976, 25 (3): 257-265. 10.2307/2347233.
Thioulouse J, Chessel D, Dolédec S, Olivier J: ADE-4: a multivariate analysis and graphical display software. Statistics and Computing. 1997, 7: 75-83. 10.1023/A:1018513530268.
Butte A, Tamayo P, Slonim D, Golub T, Kohane I: Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Proc Nat Acad Sci U S A. 2000, 97 (22): 12182-12186. 10.1073/pnas.220392197.
Staunton J, Slonim D, Coller H, Tamayo P, Angelo M, Park J, Scherf U, Lee J, Reinhold W, Weinstein J, Mesirov J, Lander E, Golub T: Chemosensitivity prediction by transcriptional profiling. Proceedings of the National Academy of Sciences. 2001, 98 (19): 10787-10.1073/pnas.191368598.
Ross D, Scherf U, Eisen M, Perou C, Rees C, Spellman P, Iyer V, Jeffrey S, Rijn Van de M, Waltham M, Pergamenschikov A, Lee J, Lashkari D, Shalon D, Myers T, Weinstein J, Botstein D, Brown P: Systematic variation in gene expression patterns in human cancer cell lines. Nat Genet. 2000, 24 (3): 227-35. 10.1038/73432.
Scherf U, Ross D, Waltham M, Smith L, Lee J, Tanabe L, Kohn K, Reinhold W, Myers T, Andrews D, Scudiero D, Eisen M, Sausville E, Pommier Y, Botstein D, Brown P, Weinstein J: A gene expression database for the molecular pharmacology of cancer. Nat Genet. 2000, 24 (3): 236-244. 10.1038/73439.
Fredman P, Hedberg K, Brezicka T: Gangliosides as Therapeutic Targets for Cancer. BioDrugs. 2003, 17 (3): 155-10.2165/00063030-200317030-00002.
González I, Déjean S, Martin P, Goncalves O, Besse P, Baccini A: Highlighting Relationships Between Heteregeneous Biological Data Through Graphical Displays Based On Regularized Canonical Correlation Analysis. Journal of Biological Systems. 2008,
Jolliffe I, Trendafilov N, Uddin M: A Modified Principal Component Technique Based on the LASSO. Journal of Computational & Graphical Statistics. 2003, 12 (3): 531-547. 10.1198/1061860032148.
Calvano S, Xiao W, Richards D, Felciano R, Baker H, Cho R, Chen R, Brownstein B, Cobb J, Tschoeke S, Miller-Graziano C, Moldawer L, Mindrinos M, Davis R, Tompkins R, Lowry S: A network-based analysis of systemic in ammation in humans. nature. 2005, 437 (7061): 1032-10.1038/nature03985.
Yang J, Weinberg R: Epithelial-Mesenchymal Transition: At the Crossroads of Development and Tumor Metastasis. Developmental Cell. 2008, 14 (6): 818-829. 10.1016/j.devcel.2008.05.009.
Portoukalian J, Zwingelstein G, Dore J: Lipid composition of human malignant melanoma tumors at various levels of malignant growth. Eur J Biochem. 1979, 94 (1): 19-23. 10.1111/j.1432-1033.1979.tb12866.x.
Juliano R, Reddig P, Alahari S, Edin M, Howe A, Aplin A: Integrin regulation of cell signalling and motility. Biochemical Society Transactions. 2004, 32: 443-446. 10.1042/BST0320443.