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.
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References in zbMATH (referenced in 6 articles )
Showing results 1 to 6 of 6.
- 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)
- Marttinen, Pekka; Gillberg, Jussi; Havulinna, Aki; Corander, Jukka; Kaski, Samuel: Genome-wide association studies with high-dimensional phenotypes (2013)
- Yoshida, Hisako; Kawaguchi, Atsushi; Tsuruya, Kazuhiko: Radial basis function-sparse partial least squares for application to brain imaging data (2013)
- Lykou, Anastasia; Whittaker, Joe: Sparse CCA using a lasso with positivity constraints (2010)
- Zhang, Dabao; Lin, Yanzhu; Zhang, Min: Penalized orthogonal-components regression for large $p$ small $n$ data (2009)
- Cao, Kim-Anh L^e; Rossouw, Debra; Robert-Granié, Christèle; Besse, Philippe: A sparse PLS for variable selection when integrating omics data (2008)