mixOmics: Omics Data Integration Project. The package provide statistical integrative techniques and variants to analyse highly dimensional data sets: regularized CCA and sparse PLS to unravel relationships between two heterogeneous data sets of size (nxp) and (nxq) where the p and q variables are measured on the same samples or individuals n. These data may come from high throughput technologies, such as omics data (e.g. transcriptomics, metabolomics or proteomics data) that require an integrative or joint analysis. However, mixOmics can also be applied to any other large data sets where p + q >> n. rCCA is a regularized version of CCA to deal with the large number of variables. sPLS allows variable selection in a one step procedure and two frameworks are proposed: regression and canonical analysis. Numerous graphical outputs are provided to help interpreting the results. Recent methodological developments include: sparse PLS-Discriminant Analysis, Independent Principal Component Analysis and multilevel analysis using variance decomposition of the data.

References in zbMATH (referenced in 29 articles )

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  1. Aguilera-Morillo, M. Carmen; Aguilera, Ana M.: Multi-class classification of biomechanical data: a functional LDA approach based on multi-class penalized functional PLS (2020)
  2. Broc, Camilo; Calvo, Borja; Liquet, Benoit: Penalized partial least square applied to structured data (2020)
  3. Genuer, Robin; Poggi, Jean-Michel: Random forests with R (2020)
  4. Kobak, Dmitry; Lomond, Jonathan; Sanchez, Benoit: The optimal ridge penalty for real-world high-dimensional data can be zero or negative due to the implicit ridge regularization (2020)
  5. Martin, Ryan; Tang, Yiqi: Empirical priors for prediction in sparse high-dimensional linear regression (2020)
  6. Zhang, Fan; Miecznikowski, Jeffrey C.; Tritchler, David L.: Identification of supervised and sparse functional genomic pathways (2020)
  7. Zhu, Guangyu; Su, Zhihua: Envelope-based sparse partial least squares (2020)
  8. De Micheaux, Pierre Lafaye; Liquet, Benoît; Sutton, Matthew: PLS for Big Data: a unified parallel algorithm for regularised group PLS (2019)
  9. Feuerriegel, Stefan; Gordon, Julius: News-based forecasts of macroeconomic indicators: a semantic path model for interpretable predictions (2019)
  10. Jain, Yashita; Ding, Shanshan; Qiu, Jing: Sliced inverse regression for integrative multi-omics data analysis (2019)
  11. Sutton, Matthew; Mengersen, Kerrie; Liquet, Benoit: [HDDA] sparse subspace constrained partial least squares (2019)
  12. Imbert, Alyssa; Vialaneix, Nathalie: Exploring, handling, imputing and evaluating missing data in statistical analyses: a review of existing approaches (2018)
  13. Jalal K. Siddiqui, Elizabeth Baskin, Mingrui Liu, Carmen Z. Cantemir-Stone, Bofei Zhang, Russell Bonneville, Joseph P. McElroy, Kevin R. Coombes, Ewy A. Mathé: IntLIM: Integration using Linear Models of metabolomics and gene expression data (2018) arXiv
  14. Perrot-Dockès, Marie; Lévy-Leduc, Céline; Chiquet, Julien; Sansonnet, Laure; Brégère, Margaux; Étienne, Marie-Pierre; Robin, Stéphane; Genta-Jouve, Grégory: A variable selection approach in the multivariate linear model: an application to LC-MS metabolomics data (2018)
  15. Benoît Liquet and Leonardo Bottolo and Gianluca Campanella and Sylvia Richardson and Marc Chadeau-Hyam: R2GUESS: A Graphics Processing Unit-Based R Package for Bayesian Variable Selection Regression of Multivariate Responses (2016) not zbMATH
  16. Chen, Mingkun; Vigneau, Evelyne: Supervised clustering of variables (2016)
  17. Robin Genuer, Jean-Michel Poggi, Christine Tuleau-Malot: VSURF: An R Package for Variable Selection Using Random Forests (2015) not zbMATH
  18. Lock, Eric F.; Hoadley, Katherine A.; Marron, J. S.; Nobel, Andrew B.: Joint and individual variation explained (JIVE) for integrated analysis of multiple data types (2013)
  19. Marttinen, Pekka; Gillberg, Jussi; Havulinna, Aki; Corander, Jukka; Kaski, Samuel: Genome-wide association studies with high-dimensional phenotypes (2013)
  20. Yoshida, Hisako; Kawaguchi, Atsushi; Tsuruya, Kazuhiko: Radial basis function-sparse partial least squares for application to brain imaging data (2013)

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