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Omics Data Integration Project
Sebastien Dejean, Ignacio Gonzalez, Kim-Anh Le Cao with contributions from Pierre Monget, Jeff Coquery, FangZou Yao, Benoit Liquet
GPL (>= 2)
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.
Package Version Released
mixOmics 4.1-5 2 years 46 weeks ago
mixOmics 4.1-4 3 years 20 weeks ago
mixOmics 4.1-3 3 years 22 weeks ago
mixOmics 4.0-2 4 years 7 weeks ago
mixOmics 4.0-1 4 years 15 weeks ago
mixOmics 3.0 4 years 49 weeks ago
mixOmics 2.9-6 5 years 4 weeks ago
mixOmics 2.9-4 5 years 18 weeks ago
mixOmics 2.9-2 5 years 25 weeks ago
mixOmics 2.9-1 5 years 37 weeks ago
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