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 <email@example.com>|
|License||GPL (>= 2)|
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