mixOmics v6.0.0

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by Kim-Anh Cao

Omics Data Integration Project

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.

Functions in mixOmics

Name Description
cimDiablo Clustered Image Maps (CIMs) ("heat maps") for DIABLO
block.splsda Horizontal sparse Partial Least Squares - Discriminant Analysis (sPLS-DA) integration
ipca Independent Principal Component Analysis
Koren.16S 16S microbiome atherosclerosis study
liver.toxicity Liver Toxicity Data
diverse.16S 16S microbiome data: most diverse bodysites from HMP
breast.TCGA Breast Cancer multi omics data from TCGA
block.spls Horizontal sparse Partial Least Squares (sPLS) integration
block.pls Horizontal Partial Least Squares (PLS) integration
color.jet Color Palette for mixOmics
block.plsda Horizontal Partial Least Squares - Discriminant Analysis (PLS-DA) integration
cim Clustered Image Maps (CIMs) ("heat maps")
circosPlot circosPlot for DIABLO
breast.tumors Human Breast Tumors Data
image Plot the cross-validation score.
explained_variance Calculation of explained variance
mint.plsda Vertical Discriminant Analysis integration
map Classification given Probabilities
logratio.transfo Log-ratio transformation
mint.block.splsda Horizontal and Vertical Discriminant Analysis integration with variable selection
mint.block.pls Horizontal and Vertical integration
mint.splsda Vertical Discriminant Analysis integration with variable selection
pca Principal Components Analysis
mixOmics PLS-derived methods: one function to rule them all
image.estim.regul Plot the cross-validation score.
estim.regul Estimate the parameters of regularization for Regularized CCA
multilevel Multilevel analysis for repeated measurements (cross-over design)
nearZeroVar Identification of zero- or near-zero variance predictors
linnerud Linnerud Dataset
imgCor Image Maps of Correlation Matrices between two Data Sets
nipals Non-linear Iterative Partial Least Squares (NIPALS) algorithm
nutrimouse Nutrimouse Dataset
mat.rank Matrix Rank
mint.block.plsda Horizontal and Vertical Discriminant Analysis integration
mint.pls Vertical integration
mint.spls Vertical integration with variable selection
mint.block.spls Horizontal and Vertical sparse integration with variable selection
pcatune Tune the number of principal components in PCA
multidrug Multidrug Resistence Data
network Relevance Network for (r)CCA and (s)PLS regression
plotArrow Arrow sample plot
pls Partial Least Squares (PLS) Regression
perf Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA, MINT and DIABLO
plot.rcc Canonical Correlations Plot
plotLoadings Plot of Loading vectors
plotVar Plot of Variables
plotContrib Contribution plot of variables
plot.perf Plot for model performance
plotIndiv Plot of Individuals (Experimental Units)
plotDiablo Graphical output for the DIABLO framework
spca Sparse Principal Components Analysis
rcc Regularized Canonical Correlation Analysis
spls Sparse Partial Least Squares (sPLS)
sipca Independent Principal Component Analysis
print Print Methods for CCA, (s)PLS, PCA and Summary objects
srbct Small version of the small round blue cell tumors of childhood data
tune.splsda Tuning functions for sPLS-DA method
tune.rcc Estimate the parameters of regularization for Regularized CCA
tune.mint.splsda Estimate the parameters of mint.splsda method
selectVar Output of selected variables
withinVariation Within matrix decomposition for repeated measurements (cross-over design)
unmap Dummy matrix for an outcome factor
wrapper.rgcca mixOmics wrapper for Regularised Generalised Canonical Correlation Analysis (rgcca)
wrapper.sgcca mixOmics wrapper for Sparse Generalised Canonical Correlation Analysis (sgcca)
plsda Partial Least Squares Discriminant Analysis (PLS-DA).
predict Predict Method for (mint).(block).(s)pls(da) methods
scatterutil Graphical utility functions from ade4
splsda Sparse Partial Least Squares Discriminant Analysis (sPLS-DA)
tune.pca Tune the number of principal components in PCA
study_split divides a data matrix in a list of matrices defined by a factor
stemcells Human Stem Cells Data
tune.multilevel Tuning functions for multilevel analyses
summary Summary Methods for CCA and PLS objects
tune Overall tuning function that can be used to tune several methods
yeast Yeast metabolomic study
vac18.simulated Simulated data based on the vac18 study for multilevel analysis
vip Variable Importance in the Projection (VIP)
vac18 Vaccine study Data
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Type Package
Date 2016-06-13
License GPL (>= 2)
URL http://www.mixOmics.org
BugReports mixomics@math.univ-toulouse.fr or https://bitbucket.org/klecao/package-mixomics/issues
Repository CRAN
Date/Publication 2016-06-14 12:08:22
Packaged 2016-06-14 06:49:17 UTC; klecao
NeedsCompilation no
LazyData true

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