# Omics Data Integration Project

## Documentation for package ‘mixOmics’ version 6.6.2

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

A B C D E G I K L M N P R S T U V W Y

## -- A --

auroc | Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) curves for supervised classification
auroc.mint.plsda | Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) curves for supervised classification
auroc.mint.splsda | Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) curves for supervised classification
auroc.mixo_plsda | Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) curves for supervised classification
auroc.mixo_splsda | Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) curves for supervised classification
auroc.sgccda | Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) curves for supervised classification

## -- B --

background.predict | Calculate prediction areas
block.pls | N-integration with Projection to Latent Structures models (PLS)
block.plsda | N-integration with Projection to Latent Structures models (PLS) with Discriminant Analysis
block.spls | N-integration and feature selection with sparse Projection to Latent Structures models (sPLS)
block.splsda | N-integration and feature selection with Projection to Latent Structures models (PLS) with sparse Discriminant Analysis
breast.TCGA | Breast Cancer multi omics data from TCGA
breast.tumors | Human Breast Tumors Data

## -- C --

cim | Clustered Image Maps (CIMs) ("heat maps")
cimDiablo | Clustered Image Maps (CIMs) ("heat maps") for DIABLO
circosPlot | circosPlot for DIABLO
color.GreenRed | Color Palette for mixOmics
color.jet | Color Palette for mixOmics
color.mixo | Color Palette for mixOmics
color.spectral | Color Palette for mixOmics

## -- D --

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

## -- E --

estim.regul | Estimate the parameters of regularization for Regularized CCA
estim.regul.default | Estimate the parameters of regularization for Regularized CCA
explained_variance | Calculation of explained variance

## -- G --

get.BER | Create confusion table and calculate the Balanced Error Rate
get.confusion_matrix | Create confusion table and calculate the Balanced Error Rate

## -- I --

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

## -- K --

Koren.16S | 16S microbiome atherosclerosis study

## -- L --

linnerud | Linnerud Dataset
liver.toxicity | Liver Toxicity Data
logratio.transfo | Log-ratio transformation

## -- M --

map | Classification given Probabilities
mat.rank | Matrix Rank
mint.block.pls | NP-integration
mint.block.plsda | NP-integration with Discriminant Analysis
mint.block.spls | NP-integration for integration with variable selection
mint.block.splsda | NP-integration with Discriminant Analysis and variable selection
mint.pca | P-integration with Principal Component Analysis
mint.pls | P-integration
mint.plsda | P-integration with Projection to Latent Structures models (PLS) with Discriminant Analysis
mint.spls | P-integration with variable selection
mint.splsda | P-integration with Discriminant Analysis and variable selection
mixOmics | PLS-derived methods: one function to rule them all!
multidrug | Multidrug Resistence Data

## -- N --

nearZeroVar | Identification of zero- or near-zero variance predictors
network | Relevance Network for (r)CCA and (s)PLS regression
network.default | Relevance Network for (r)CCA and (s)PLS regression
network.pls | Relevance Network for (r)CCA and (s)PLS regression
network.rcc | Relevance Network for (r)CCA and (s)PLS regression
network.spls | Relevance Network for (r)CCA and (s)PLS regression
nipals | Non-linear Iterative Partial Least Squares (NIPALS) algorithm
nutrimouse | Nutrimouse Dataset

## -- P --

pca | Principal Components Analysis
pcatune | Tune the number of principal components in PCA
perf | Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA, MINT and DIABLO
perf.mint.splsda | Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA, MINT and DIABLO
perf.mixo_pls | Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA, MINT and DIABLO
perf.mixo_plsda | Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA, MINT and DIABLO
perf.mixo_spls | Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA, MINT and DIABLO
perf.mixo_splsda | Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA, MINT and DIABLO
perf.sgccda | Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA, MINT and DIABLO
plot.perf | Plot for model performance
plot.perf.mint.plsda.mthd | Plot for model performance
plot.perf.mint.splsda.mthd | Plot for model performance
plot.perf.pls.mthd | Plot for model performance
plot.perf.plsda.mthd | Plot for model performance
plot.perf.sgccda.mthd | Plot for model performance
plot.perf.spls.mthd | Plot for model performance
plot.perf.splsda.mthd | Plot for model performance
plot.rcc | Canonical Correlations Plot
plot.tune | Plot for model performance
plot.tune.block.splsda | Plot for model performance
plot.tune.rcc | Plot the cross-validation score.
plot.tune.splsda | Plot for model performance
plotArrow | Arrow sample plot
plotDiablo | Graphical output for the DIABLO framework
plotIndiv | Plot of Individuals (Experimental Units)
plotIndiv.mint.spls | Plot of Individuals (Experimental Units)
plotIndiv.mint.splsda | Plot of Individuals (Experimental Units)
plotIndiv.mixo_pls | Plot of Individuals (Experimental Units)
plotIndiv.mixo_spls | Plot of Individuals (Experimental Units)
plotIndiv.pca | Plot of Individuals (Experimental Units)
plotIndiv.rcc | Plot of Individuals (Experimental Units)
plotIndiv.rgcca | Plot of Individuals (Experimental Units)
plotIndiv.sgcca | Plot of Individuals (Experimental Units)
plotIndiv.sipca | Plot of Individuals (Experimental Units)
plotLoadings | Plot of Loading vectors
plotLoadings.block.pls | Plot of Loading vectors
plotLoadings.block.plsda | Plot of Loading vectors
plotLoadings.block.spls | Plot of Loading vectors
plotLoadings.block.splsda | Plot of Loading vectors
plotLoadings.mint.pls | Plot of Loading vectors
plotLoadings.mint.plsda | Plot of Loading vectors
plotLoadings.mint.spls | Plot of Loading vectors
plotLoadings.mint.splsda | Plot of Loading vectors
plotLoadings.mixo_pls | Plot of Loading vectors
plotLoadings.mixo_plsda | Plot of Loading vectors
plotLoadings.mixo_spls | Plot of Loading vectors
plotLoadings.mixo_splsda | Plot of Loading vectors
plotLoadings.pca | Plot of Loading vectors
plotLoadings.rcc | Plot of Loading vectors
plotLoadings.rgcca | Plot of Loading vectors
plotLoadings.sgcca | Plot of Loading vectors
plotLoadings.sgccda | Plot of Loading vectors
plotVar | Plot of Variables
plotVar.pca | Plot of Variables
plotVar.pls | Plot of Variables
plotVar.plsda | Plot of Variables
plotVar.rcc | Plot of Variables
plotVar.rgcca | Plot of Variables
plotVar.sgcca | Plot of Variables
plotVar.spca | Plot of Variables
plotVar.spls | Plot of Variables
plotVar.splsda | Plot of Variables
pls | Partial Least Squares (PLS) Regression
plsda | Partial Least Squares Discriminant Analysis (PLS-DA).
predict.mint.block.pls | Predict Method for (mint).(block).(s)pls(da) methods
predict.mint.block.plsda | Predict Method for (mint).(block).(s)pls(da) methods
predict.mint.block.spls | Predict Method for (mint).(block).(s)pls(da) methods
predict.mint.block.splsda | Predict Method for (mint).(block).(s)pls(da) methods
predict.mint.pls | Predict Method for (mint).(block).(s)pls(da) methods
predict.mint.plsda | Predict Method for (mint).(block).(s)pls(da) methods
predict.mint.spls | Predict Method for (mint).(block).(s)pls(da) methods
predict.mint.splsda | Predict Method for (mint).(block).(s)pls(da) methods
predict.pls | Predict Method for (mint).(block).(s)pls(da) methods
predict.plsda | Predict Method for (mint).(block).(s)pls(da) methods
predict.spls | Predict Method for (mint).(block).(s)pls(da) methods
predict.splsda | Predict Method for (mint).(block).(s)pls(da) methods
print | Print Methods for CCA, (s)PLS, PCA and Summary objects
print.mixo_pls | Print Methods for CCA, (s)PLS, PCA and Summary objects
print.mixo_spls | Print Methods for CCA, (s)PLS, PCA and Summary objects
print.pca | Print Methods for CCA, (s)PLS, PCA and Summary objects
print.rcc | Print Methods for CCA, (s)PLS, PCA and Summary objects
print.rgcca | Print Methods for CCA, (s)PLS, PCA and Summary objects
print.sgcca | Print Methods for CCA, (s)PLS, PCA and Summary objects
print.spca | Print Methods for CCA, (s)PLS, PCA and Summary objects
print.summary | Print Methods for CCA, (s)PLS, PCA and Summary objects

## -- R --

rcc | Regularized Canonical Correlation Analysis
rcc.default | Regularized Canonical Correlation Analysis

## -- S --

select.var | Output of selected variables
selectVar | Output of selected variables
selectVar.mixo_pls | Output of selected variables
selectVar.mixo_spls | Output of selected variables
selectVar.pca | Output of selected variables
selectVar.rgcca | Output of selected variables
selectVar.sgcca | 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 childhood data
stemcells | Human Stem Cells Data
study_split | divides a data matrix in a list of matrices defined by a factor
summary | Summary Methods for CCA and PLS objects
summary.mixo_pls | Summary Methods for CCA and PLS objects
summary.mixo_spls | Summary Methods for CCA and PLS objects
summary.rcc | Summary Methods for CCA and PLS objects

## -- T --

tune | Generic function to choose the parameters in the different methods in mixOmics
tune.block.splsda | Tuning function for block.splsda method (N-integration with sparse Discriminant Analysis)
tune.mint.splsda | Estimate the parameters of mint.splsda method
tune.pca | Tune the number of principal components in PCA
tune.rcc | Estimate the parameters of regularization for Regularized CCA
tune.rcc.default | Estimate the parameters of regularization for Regularized CCA
tune.spls | Tuning functions for sPLS method
tune.splsda | Tuning functions for sPLS-DA method
tune.splslevel | Tuning functions for multilevel sPLS method

## -- U --

unmap | Dummy matrix for an outcome factor

## -- V --

vac18 | Vaccine study Data
vac18.simulated | Simulated data based on the vac18 study for multilevel analysis
vip | Variable Importance in the Projection (VIP)

## -- W --

withinVariation | Within matrix decomposition for repeated measurements (cross-over design)
wrapper.rgcca | mixOmics wrapper for Regularised Generalised Canonical Correlation Analysis (rgcca)
wrapper.sgcca | mixOmics wrapper for Sparse Generalised Canonical Correlation Analysis (sgcca)
wrapper.sgccda | N-integration and feature selection with Projection to Latent Structures models (PLS) with sparse Discriminant Analysis

## -- Y --

yeast | Yeast metabolomic study