A. Tenenhaus and M. Tenenhaus, Regularized Generalized Canonical Correlation Analysis, Psychometrika, vol.12, issue.9, pp.257-84, 2011.
DOI : 10.1007/BFb0062108
URL : https://hal.archives-ouvertes.fr/hal-00578321

M. Tenenhaus, A. Tenenhaus, and P. Groenen, Regularized Generalized Canonical Correlation Analysis: A Framework for Sequential Multiblock Component Methods, Psychometrika, vol.10, issue.3, 2017.
DOI : 10.1002/(SICI)1099-128X(199609)10:5/6<463::AID-CEM445>3.0.CO;2-L

A. Tenenhaus, C. Philippe, and V. Guillemot, Variable selection for generalized canonical correlation analysis, Biostatistics, vol.15, issue.3, pp.569-83, 2014.
DOI : 10.1093/biostatistics/kxu001
URL : https://hal.archives-ouvertes.fr/hal-01071432

O. Gü-nther, H. Shin, and R. Nq, Novel Multivariate Methods for Integration of Genomics and Proteomics Data: Applications in a Kidney Transplant Rejection Study, OMICS: A Journal of Integrative Biology, vol.18, issue.11, pp.682-95, 2014.
DOI : 10.1089/omi.2014.0062

C. Meng, O. Zeleznik, and G. Thallinger, Dimension reduction techniques for the integrative analysis of multi-omics data, Briefings in Bioinformatics, vol.17, issue.4, pp.628-669, 2016.
DOI : 10.1093/bib/bbv108

C. Meng, B. Kuster, and A. Culhane, A multivariate approach to the integration of multi-omics datasets, BMC Bioinformatics, vol.15, issue.1, p.1, 2014.
DOI : 10.1073/pnas.1203085109

H. Hotelling, Analysis of a complex of statistical variables into principal components., Journal of Educational Psychology, vol.24, issue.6, pp.417-458, 1933.
DOI : 10.1037/h0071325

H. Hotelling, RELATIONS BETWEEN TWO SETS OF VARIATES, Biometrika, vol.28, issue.3-4, pp.321-77, 1936.
DOI : 10.1093/biomet/28.3-4.321

L. Tucker, An inter-battery method of factor analysis, Psychometrika, vol.6, issue.2, pp.111-147, 1958.
DOI : 10.1111/j.2044-8317.1950.tb00284.x

S. Wold, H. Martens, and H. Wold, The multivariate calibration problem in chemistry solved by the PLS method, Proc Conf Matrix Pencils, vol.20, pp.286-93, 1983.
DOI : 10.1080/00401706.1978.10489693

A. Van-den-wollenberg, Redundancy analysis an alternative for canonical correlation analysis, Psychometrika, vol.42, issue.2, pp.207-226, 1977.
DOI : 10.1007/BF02294050

J. Carroll, A generalization of canonical correlation analysis to three or more sets of variables. Proc 76th Conv Am Psych, Assoc, vol.3, pp.227-235, 1968.

J. Carroll, Equations and tables for a generalization of canonical correlation analysis to three or more sets of variables, 1968.

S. Wold, N. Kettaneh, and K. Tjessem, Hierarchical multiblock PLS and PC models for easier model interpretation and as an alternative to variable selection, Journal of Chemometrics, vol.10, issue.5-6, pp.463-82, 1996.
DOI : 10.1002/(SICI)1099-128X(199609)10:5/6<463::AID-CEM445>3.0.CO;2-L

D. Chessel and M. Hanafi, Analyses de la co-inertie de K nuages de points, Rev Stat Appl, vol.44, issue.2, pp.35-60, 1996.

J. Westerhuis, T. Kourti, and J. Macgregor, Analysis of multiblock and hierarchical PCA and PLS models, Journal of Chemometrics, vol.12, issue.5, pp.301-322, 1998.
DOI : 10.1002/(SICI)1099-128X(199809/10)12:5<301::AID-CEM515>3.0.CO;2-S

A. Smilde, J. Westerhuis, and S. De-jong, A framework for sequential multiblock component methods, Journal of Chemometrics, vol.1, issue.6, pp.323-360, 2003.
DOI : 10.1111/j.2044-8317.1994.tb01027.x

B. Escofier and J. Pagè-s, Multiple factor analysis (AFMULT package), Computational Statistics & Data Analysis, vol.18, issue.1, pp.121-161, 1994.
DOI : 10.1016/0167-9473(94)90135-X

P. Horst, Relations among m sets of variables, Psychometrika, vol.26, pp.126-175, 1961.

J. Kettenring, Canonical analysis of several sets of variables, Biometrika, vol.58, issue.3, pp.433-51, 1971.
DOI : 10.1093/biomet/58.3.433

M. Hanafi, PLS Path modelling: computation of latent variables with the estimation mode B, Computational Statistics, vol.48, issue.2, pp.275-92, 2007.
DOI : 10.1007/978-3-642-52512-4

J. Van-de-geer, Linear relations among k sets of variables, Psychometrika, vol.49, pp.70-94, 1984.

M. Hanafi and H. Kiers, Analysis of K sets of data, with differential emphasis on agreement between and within sets, Computational Statistics & Data Analysis, vol.51, issue.3, pp.1491-508, 2006.
DOI : 10.1016/j.csda.2006.04.020

N. Kramer, Analysis of high dimensional data with partial least squares and boosting. Doctoral dissertation, 2007.

H. Wold, Soft modeling: the basic design and some extensions In Systems under Indirect Observation: Part 2. North- Holland, pp.1-54, 1982.

A. Tenenhaus, V. Guillemot, and . Rgcca, Regularized and Sparse Generalized Canonical Correlation Analysis forMulti- Block Data. R package version 2.12. 2017. https://cran.r-project
DOI : 10.1007/s11336-011-9206-8

A. Tenenhaus, C. Philippe, and V. Frouin, Kernel Generalized Canonical Correlation Analysis, Computational Statistics & Data Analysis, vol.90, pp.114-145, 2015.
DOI : 10.1016/j.csda.2015.04.004
URL : https://hal.archives-ouvertes.fr/hal-01238943

M. Tenenhaus, V. Vinzi, and Y. Chatelin, PLS path modeling, Computational Statistics & Data Analysis, vol.48, issue.1, pp.159-205, 2005.
DOI : 10.1016/j.csda.2004.03.005
URL : https://hal.archives-ouvertes.fr/hal-00869374

S. Bro and A. Smilde, Centering and scaling in component analysis, Journal of Chemometrics, vol.40, issue.1, pp.16-33, 2003.
DOI : 10.1557/PROC-28-75

K. Van-deun, A. Smilde, and M. Van-der-werf, A structured overview of simultaneous component based data integration, BMC Bioinformatics, vol.10, issue.1, p.246, 2009.
DOI : 10.1186/1471-2105-10-246

O. Ledoit and M. Wolf, A well-conditioned estimator for large-dimensional covariance matrices, Journal of Multivariate Analysis, vol.88, issue.2, pp.365-411, 2004.
DOI : 10.1016/S0047-259X(03)00096-4
URL : https://doi.org/10.1016/s0047-259x(03)00096-4

J. Sch?-afer and K. Strimmer, Shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics, Stat Appl Genet Mol Biolgy, vol.4, issue.1, p.32, 2005.

M. Barker and W. Rayens, Partial least squares for discrimination, Journal of Chemometrics, vol.10, issue.3, pp.166-73, 2003.
DOI : 10.1002/0471725293

P. Bickel and E. Levina, Some theory for Fisher's linear discriminant function, `naive Bayes', and some alternatives when there are many more variables than observations, Bernoulli, vol.10, issue.6, pp.989-1010, 2004.
DOI : 10.3150/bj/1106314847
URL : http://doi.org/10.3150/bj/1106314847

H. Chun and K. , Sparse partial least squares regression for simultaneous dimension reduction and variable selection, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.15, issue.1, pp.3-25, 2010.
DOI : 10.1091/mbc.9.12.3273
URL : http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9868.2009.00723.x/pdf

B. Efron, Bootstrap Methods: Another Look at the Jackknife, The Annals of Statistics, vol.7, issue.1, pp.1-26, 1979.
DOI : 10.1214/aos/1176344552
URL : http://doi.org/10.1214/aos/1176344552

B. Efron, Better Bootstrap Confidence Intervals, Journal of the American Statistical Association, vol.11, issue.397, pp.171-85, 1987.
DOI : 10.1214/aos/1176346163

J. Fleiss, Measuring nominal scale agreement among many raters., Psychological Bulletin, vol.76, issue.5, p.378, 1971.
DOI : 10.1037/h0031619

N. Meinshausen and P. Bü-hlmann, Stability selection, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.7, issue.4, pp.417-73, 2010.
DOI : 10.1186/1471-2105-9-307
URL : http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9868.2010.00740.x/pdf

Z. Gu and K. Van-deun, A variable selection method for simultaneous component based data integration, Chemometrics and Intelligent Laboratory Systems, vol.158, pp.187-99, 2016.
DOI : 10.1016/j.chemolab.2016.07.013

M. Keiser, J. Kordower, and P. Gonzalez-alegre, Broad distribution of ataxin 1 silencing in rhesus cerebella for spinocerebellar ataxia type 1 therapy, Brain, vol.138, issue.12, pp.3555-66, 2015.
DOI : 10.1093/brain/awv292

U. Rü-b, L. Schö-ls, and H. Paulson, Clinical features, neurogenetics and neuropathology of the polyglutamine spinocerebellar ataxias type 1, 2, 3, 6 and 7, Progress in Neurobiology, vol.104, pp.38-66, 2013.
DOI : 10.1016/j.pneurobio.2013.01.001

A. Durr, Autosomal dominant cerebellar ataxias: polyglutamine expansions and beyond, The Lancet Neurology, vol.9, issue.9, pp.885-94, 2010.
DOI : 10.1016/S1474-4422(10)70183-6

A. Klaes, E. Reckziegel, M. Franca, and J. , MR Imaging in Spinocerebellar Ataxias: A Systematic Review, American Journal of Neuroradiology, vol.37, issue.8, pp.1405-1417, 2016.
DOI : 10.3174/ajnr.A4760
URL : http://www.ajnr.org/content/ajnr/37/8/1405.full.pdf

H. Jacobi, K. Reetz, and S. Montcel, Biological and clinical characteristics of individuals at risk for spinocerebellar ataxia types 1, 2, 3, and 6 in the longitudinal RISCA study: analysis of baseline data, The Lancet Neurology, vol.12, issue.7, pp.650-658, 2013.
DOI : 10.1016/S1474-4422(13)70104-2

F. Mochel, P. Charles, and F. Seguin, Early Energy Deficit in Huntington Disease: Identification of a Plasma Biomarker Traceable during Disease Progression, PLoS ONE, vol.21, issue.7, p.647, 2007.
DOI : 10.1371/journal.pone.0000647.t002

F. Mochel and R. Haller, Energy deficit in Huntington disease: why it matters, Journal of Clinical Investigation, vol.121, issue.2, pp.493-502, 2011.
DOI : 10.1172/JCI45691
URL : http://www.jci.org/articles/view/45691/files/pdf

I. Adanyeguh, D. Rinaldi, and P. Henry, Triheptanoin improves brain energy metabolism in patients with Huntington disease, Neurology, vol.84, issue.5, pp.490-495, 2015.
DOI : 10.1212/WNL.0000000000001214
URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4336068/pdf

T. Schmitz-hubsch, S. Du-montcel, and L. Baliko, Scale for the assessment and rating of ataxia: Development of a new clinical scale, Neurology, vol.66, issue.11, pp.1717-1737, 2006.
DOI : 10.1212/01.wnl.0000219042.60538.92

D. Wishart, D. Tzur, and C. Knox, HMDB: the Human Metabolome Database, Nucleic Acids Research, vol.35, issue.Database, pp.521-527, 2007.
DOI : 10.1093/nar/gkl923
URL : https://academic.oup.com/nar/article-pdf/35/suppl_1/D521/3870909/gkl923.pdf

D. Wishart, C. Knox, and A. Guo, HMDB: a knowledgebase for the human metabolome, Nucleic Acids Research, vol.37, issue.Database, pp.603-613, 2009.
DOI : 10.1093/nar/gkn810

D. Wishart, C. Knox, and A. Guo, HMDB 3.0???The Human Metabolome Database in 2013, Nucleic Acids Research, vol.41, issue.D1, pp.801-808, 2013.
DOI : 10.1093/nar/gks1065
URL : https://academic.oup.com/nar/article-pdf/35/suppl_1/D521/3870909/gkl923.pdf

M. Kanehisa, Y. Sato, and M. Kawashima, KEGG: Kyoto Encyclopedia of Genes and Genomes, Nucleic Acids Research, vol.28, issue.1, pp.27-30, 2000.
DOI : 10.1093/nar/28.1.27

M. Kanehisa, Y. Sato, and M. Kawashima, KEGG as a reference resource for gene and protein annotation, Nucleic Acids Research, vol.44, issue.D1, pp.457-62, 2016.
DOI : 10.1093/nar/gkv1070
URL : https://academic.oup.com/nar/article-pdf/44/D1/D457/9482226/gkv1070.pdf

F. Lamari, F. Mochel, and F. Sedel, Disorders of phospholipids, sphingolipids and fatty acids biosynthesis: toward a new category of inherited metabolic diseases, Journal of Inherited Metabolic Disease, vol.28, issue.88, pp.411-436, 2013.
DOI : 10.1038/ng572

R. Caspi, H. Foerster, and C. Fulcher, The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases, Nucleic Acids Res, vol.3636, issue.1, pp.623-654, 2008.

J. Mckeon, Canonical analysis: some relation between canonical correlation, factor analysis, discriminant analysis, and scaling theory, Psychom Monogr, vol.13, 1966.

I. Lastres-becker, S. Brodesser, and D. Lü-tjohann, Insulin receptor and lipid metabolism pathology in ataxin-2 knock-out mice, Human Molecular Genetics, vol.17, issue.10, pp.1465-81, 2008.
DOI : 10.1093/hmg/ddn035
URL : https://academic.oup.com/hmg/article-pdf/17/10/1465/17244844/ddn035.pdf

R. Martin, M. Elliott, and R. Brush, Detailed Characterization of the Lipid Composition of Detergent-Resistant Membranes from Photoreceptor Rod Outer Segment Membranes, Investigative Opthalmology & Visual Science, vol.46, issue.4, pp.1147-54, 2005.
DOI : 10.1167/iovs.04-1207

A. Mcmahon, I. Butovich, and W. Kedzierski, Epidermal expression of an Elovl4 transgene rescues neonatal lethality of homozygous Stargardt disease-3 mice, Journal of Lipid Research, vol.3, issue.6, pp.1128-1166, 2011.
DOI : 10.1016/j.biochi.2009.04.001

F. Lamari, F. Mochel, and J. Saudubray, An overview of inborn errors of complex lipid biosynthesis and remodelling, Journal of Inherited Metabolic Disease, vol.28, issue.3, pp.3-18, 2015.
DOI : 10.1038/ng572

A. Tenenhaus, L. Brusquet, and G. Lechuga, Multiway regularized generalized canonical correlation analysis, 47è mes Journée de Statistique de la SFdS, 2015.
DOI : 10.1007/s11336-011-9206-8
URL : https://hal.archives-ouvertes.fr/hal-01235979

A. Tenenhaus and M. Tenenhaus, Regularized generalized canonical correlation analysis for multiblock or multigroup data analysis, European Journal of Operational Research, vol.238, issue.2, pp.391-403, 2014.
DOI : 10.1016/j.ejor.2014.01.008
URL : https://hal.archives-ouvertes.fr/hal-01071421