Multivariate methods are well suited to large omics data sets where the number of variables (e.g. genes, proteins, metabolites) is much larger than the number of samples (patients, cells, mice). They have the appealing properties of reducing the dimension of the data by using instrumental variables (components), which are defined as combinations of all variables. Those components are then used to produce useful graphical outputs that enable better understanding of the relationships and correlation structures between the different data sets that are integrated. mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. The package proposes several sparse multivariate models we have developed to identify the key variables that are highly correlated, and/or explain the biological outcome of interest. The data that can be analysed with mixOmics may come from high throughput sequencing technologies, such as omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but also beyond the realm of omics (e.g. spectral imaging). The methods implemented in mixOmics can also handle missing values without having to delete entire rows with missing data. A non exhaustive list of methods include generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. Recently we implemented integrative methods to combine multiple data sets: horizontal integration with regularised Generalised Canonical Correlation Analysis and vertical integration with multi-group Partial Least Squares.

Author | Kim-Anh Le Cao, Florian Rohart, Ignacio Gonzalez, Sebastien Dejean with key contributors Benoit Gautier, Francois Bartolo and contributions from Pierre Monget, Jeff Coquery, FangZou Yao, Benoit Liquet. |

Date of publication | 2016-10-19 13:07:29 |

Maintainer | Kim-Anh Le Cao <k.lecao@uq.edu.au> |

License | GPL (>= 2) |

Version | 6.1.1 |

http://www.mixOmics.org |

**auroc:** Area Under the Curve (AUC) and Receiver Operating...

**block.pls:** Horizontal Partial Least Squares (PLS) integration

**block.plsda:** Horizontal Partial Least Squares - Discriminant Analysis...

**block.spls:** Horizontal sparse Partial Least Squares (sPLS) integration

**block.splsda:** Horizontal sparse Partial Least Squares - Discriminant...

**breast.TCGA:** Breast Cancer multi omics data from TCGA

**breast.tumors:** Human Breast Tumors Data

**cim:** Clustered Image Maps (CIMs) ("heat maps")

**cimDiablo:** Clustered Image Maps (CIMs) ("heat maps") for DIABLO

**circosPlot:** circosPlot for DIABLO

**colors:** Color Palette for mixOmics

**diverse.16S:** 16S microbiome data: most diverse bodysites from HMP

**estim.regul:** Estimate the parameters of regularization for Regularized CCA

**explained_variance:** Calculation of explained variance

**image.estim.regul:** Plot the cross-validation score.

**image.tune.rcc:** Plot the cross-validation score.

**imgCor:** Image Maps of Correlation Matrices between two Data Sets

**ipca:** Independent Principal Component Analysis

**Koren.16S:** 16S microbiome atherosclerosis study

**linnerud:** Linnerud Dataset

**liver.toxicity:** Liver Toxicity Data

**logratio.transfo:** Log-ratio transformation

**map:** Classification given Probabilities

**mat.rank:** Matrix Rank

**mint.block.pls:** Horizontal and Vertical integration

**mint.block.plsda:** Horizontal and Vertical Discriminant Analysis integration

**mint.block.spls:** Horizontal and Vertical sparse integration with variable...

**mint.block.splsda:** Horizontal and Vertical Discriminant Analysis integration...

**mint.pca:** Vertical Principal Component integration

**mint.pls:** Vertical integration

**mint.plsda:** Vertical Discriminant Analysis integration

**mint.spls:** Vertical integration with variable selection

**mint.splsda:** Vertical Discriminant Analysis integration with variable...

**mixOmics:** PLS-derived methods: one function to rule them all

**multidrug:** Multidrug Resistence Data

**nearZeroVar:** Identification of zero- or near-zero variance predictors

**network:** Relevance Network for (r)CCA and (s)PLS regression

**nipals:** Non-linear Iterative Partial Least Squares (NIPALS) algorithm

**nutrimouse:** Nutrimouse Dataset

**pca:** Principal Components Analysis

**pcatune:** Tune the number of principal components in PCA

**perf:** Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA,...

**plotArrow:** Arrow sample plot

**plotContrib:** Contribution plot of variables

**plotDiablo:** Graphical output for the DIABLO framework

**plotIndiv:** Plot of Individuals (Experimental Units)

**plotLoadings:** Plot of Loading vectors

**plot.perf:** Plot for model performance

**plot.rcc:** Canonical Correlations Plot

**plot.tune:** Plot for model performance

**plotVar:** Plot of Variables

**pls:** Partial Least Squares (PLS) Regression

**plsda:** Partial Least Squares Discriminant Analysis (PLS-DA).

**predict:** Predict Method for (mint).(block).(s)pls(da) methods

**print.methods:** Print Methods for CCA, (s)PLS, PCA and Summary objects

**rcc:** Regularized Canonical Correlation Analysis

**scatterutil:** Graphical utility functions from ade4

**selectVar:** Output of selected variables

**sipca:** Independent Principal Component Analysis

**spca:** Sparse Principal Components Analysis

**spls:** Sparse Partial Least Squares (sPLS)

**splsda:** Sparse Partial Least Squares Discriminant Analysis (sPLS-DA)

**srbct:** Small version of the small round blue cell tumors of...

**stemcells:** Human Stem Cells Data

**study_split:** divides a data matrix in a list of matrices defined by a...

**summary:** Summary Methods for CCA and PLS objects

**tune:** Overall tuning function that can be used to tune several...

**tune.block.splsda:** Tuning function for block.splsda method

**tune.mint.splsda:** Estimate the parameters of mint.splsda method

**tune.multilevel:** Tuning functions for multilevel analyses

**tune.pca:** Tune the number of principal components in PCA

**tune.rcc:** Estimate the parameters of regularization for Regularized CCA

**tune.splsda:** Tuning functions for sPLS-DA method

**unmap:** Dummy matrix for an outcome factor

**vac18:** Vaccine study Data

**vac18.simulated:** Simulated data based on the vac18 study for multilevel...

**vip:** Variable Importance in the Projection (VIP)

**withinVariation:** Within matrix decomposition for repeated measurements...

**wrapper.rgcca:** mixOmics wrapper for Regularised Generalised Canonical...

**wrapper.sgcca:** mixOmics wrapper for Sparse Generalised Canonical Correlation...

**yeast:** Yeast metabolomic study

mixOmics

mixOmics/NAMESPACE

mixOmics/NEWS

mixOmics/data

mixOmics/data/liver.toxicity.rda

mixOmics/data/stemcells.rda

mixOmics/data/nutrimouse.rda

mixOmics/data/vac18.rda

mixOmics/data/yeast.rda

mixOmics/data/Koren.16S.rda

mixOmics/data/srbct.rda

mixOmics/data/linnerud.rda

mixOmics/data/datalist

mixOmics/data/breast.tumors.rda

mixOmics/data/diverse.16S.rda

mixOmics/data/vac18.simulated.rda

mixOmics/data/multidrug.rda

mixOmics/data/breast.TCGA.rda

mixOmics/R

mixOmics/R/plotLoadings.splsda.R
mixOmics/R/plot.tune.R
mixOmics/R/mint.pls.R
mixOmics/R/internal_predict.DA.R
mixOmics/R/cim.R
mixOmics/R/withinVariation.R
mixOmics/R/pca.R
mixOmics/R/vip.R
mixOmics/R/tune.mint.splsda.R
mixOmics/R/splsda.R
mixOmics/R/block.plsda.R
mixOmics/R/rcc.R
mixOmics/R/block.pls.R
mixOmics/R/LOGOCV.R
mixOmics/R/color.GreenRed.R
mixOmics/R/pls.R
mixOmics/R/plotDiablo.R
mixOmics/R/network.R
mixOmics/R/perf.mint.splsda.R
mixOmics/R/perf.diablo.R
mixOmics/R/check_entry.R
mixOmics/R/plotIndiv.pca.R
mixOmics/R/wrapper.rgcca.R
mixOmics/R/plotLoadings.spls.R
mixOmics/R/mint.spls.R
mixOmics/R/mint.splsda.R
mixOmics/R/spls.R
mixOmics/R/internal_mint.block_helpers.R
mixOmics/R/AUC_ROC.R
mixOmics/R/mint.pca.R
mixOmics/R/tune.pca.R
mixOmics/R/plsda.R
mixOmics/R/plotIndiv.pls.R
mixOmics/R/tune.rcc.R
mixOmics/R/nipals.R
mixOmics/R/selectVar.R
mixOmics/R/block.spls.R
mixOmics/R/internal_wrapper.mint.R
mixOmics/R/roc_utils.R
mixOmics/R/print.methods.R
mixOmics/R/tune.multilevel.R
mixOmics/R/plotLoadings.mint.splsda.R
mixOmics/R/mat.rank.R
mixOmics/R/explained_variance.R
mixOmics/R/wrapper.ilr.R
mixOmics/R/block.splsda.R
mixOmics/R/plotIndiv.mint.R
mixOmics/R/imgCor.R
mixOmics/R/nearZeroVar.R
mixOmics/R/internal_mint.block.R
mixOmics/R/unmap.R
mixOmics/R/circosPlot.R
mixOmics/R/image.tune.rcc.R
mixOmics/R/mixOmics.R
mixOmics/R/mint.block.plsda.R
mixOmics/R/bin.color.R
mixOmics/R/perf.R
mixOmics/R/plotContrib.R
mixOmics/R/MCVfold.R
mixOmics/R/tune.splslevel.R
mixOmics/R/plotIndiv.sgcca.R
mixOmics/R/plotLoadings.pca.R
mixOmics/R/sipca.R
mixOmics/R/cimDiablo.R
mixOmics/R/mint.block.spls.R
mixOmics/R/color.mixo.R
mixOmics/R/plot.pca.R
mixOmics/R/predict.mint.block.pls.R
mixOmics/R/mint.block.pls.R
mixOmics/R/scatterutil.R
mixOmics/R/internal_graphic.perf.R
mixOmics/R/plotIndiv.R
mixOmics/R/mint.block.splsda.R
mixOmics/R/pcasvd.R
mixOmics/R/summary.R
mixOmics/R/plot.rcc.R
mixOmics/R/plotArrow.R
mixOmics/R/plotLoadings.R
mixOmics/R/wrapper.sgcca.R
mixOmics/R/internal_wrapper.mint.block.R
mixOmics/R/ipca.R
mixOmics/R/color.spectral.R
mixOmics/R/imageMap.R
mixOmics/R/tune.diablo.R
mixOmics/R/plotVar.R
mixOmics/R/color.jet.R
mixOmics/R/plot.perf.R
mixOmics/R/tune.R
mixOmics/R/plotLoadings.mint.spls.R
mixOmics/R/zzz.R
mixOmics/R/mint.plsda.R
mixOmics/R/internal_graphicModule.R
mixOmics/R/ica.def.par.R
mixOmics/R/spca.R
mixOmics/R/tune.splsda.R
mixOmics/MD5

mixOmics/README

mixOmics/DESCRIPTION

mixOmics/man

mixOmics/man/plotIndiv.Rd
mixOmics/man/mint.spls.Rd
mixOmics/man/spca.Rd
mixOmics/man/scatterutil.Rd
mixOmics/man/srbct.Rd
mixOmics/man/mint.block.splsda.Rd
mixOmics/man/plot.perf.Rd
mixOmics/man/tune.Rd
mixOmics/man/wrapper.rgcca.Rd
mixOmics/man/vip.Rd
mixOmics/man/print.methods.Rd
mixOmics/man/cimDiablo.Rd
mixOmics/man/block.pls.Rd
mixOmics/man/sipca.Rd
mixOmics/man/plsda.Rd
mixOmics/man/plotLoadings.Rd
mixOmics/man/tune.rcc.Rd
mixOmics/man/tune.multilevel.Rd
mixOmics/man/map.Rd
mixOmics/man/stemcells.Rd
mixOmics/man/mint.block.pls.Rd
mixOmics/man/estim.regul.Rd
mixOmics/man/mint.block.spls.Rd
mixOmics/man/pls.Rd
mixOmics/man/auroc.Rd
mixOmics/man/mint.pls.Rd
mixOmics/man/image.tune.rcc.Rd
mixOmics/man/circosPlot.Rd
mixOmics/man/mint.splsda.Rd
mixOmics/man/logratio.transfo.Rd
mixOmics/man/diverse.16S.Rd
mixOmics/man/breast.tumors.Rd
mixOmics/man/block.spls.Rd
mixOmics/man/tune.block.splsda.Rd
mixOmics/man/withinVariation.Rd
mixOmics/man/plotContrib.Rd
mixOmics/man/tune.pca.Rd
mixOmics/man/explained_variance.Rd
mixOmics/man/breast.TCGA.Rd
mixOmics/man/predict.Rd
mixOmics/man/multidrug.Rd
mixOmics/man/spls.Rd
mixOmics/man/plot.tune.Rd
mixOmics/man/image.estim.regul.Rd
mixOmics/man/imgCor.Rd
mixOmics/man/perf.Rd
mixOmics/man/network.Rd
mixOmics/man/unmap.Rd
mixOmics/man/rcc.Rd
mixOmics/man/plotDiablo.Rd
mixOmics/man/tune.mint.splsda.Rd
mixOmics/man/nipals.Rd
mixOmics/man/selectVar.Rd
mixOmics/man/block.plsda.Rd
mixOmics/man/plotArrow.Rd
mixOmics/man/pca.Rd
mixOmics/man/vac18.Rd
mixOmics/man/yeast.Rd
mixOmics/man/plotVar.Rd
mixOmics/man/ipca.Rd
mixOmics/man/nutrimouse.Rd
mixOmics/man/tune.splsda.Rd
mixOmics/man/mixOmics.Rd
mixOmics/man/linnerud.Rd
mixOmics/man/mat.rank.Rd
mixOmics/man/wrapper.sgcca.Rd
mixOmics/man/study_split.Rd
mixOmics/man/plot.rcc.Rd
mixOmics/man/pcatune.Rd
mixOmics/man/mint.plsda.Rd
mixOmics/man/Koren.16S.Rd
mixOmics/man/colors.Rd
mixOmics/man/mint.block.plsda.Rd
mixOmics/man/vac18.simulated.Rd
mixOmics/man/summary.Rd
mixOmics/man/mint.pca.Rd
mixOmics/man/cim.Rd
mixOmics/man/liver.toxicity.Rd
mixOmics/man/splsda.Rd
mixOmics/man/nearZeroVar.Rd
mixOmics/man/block.splsda.Rd
Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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