mixOmics v6.0.0

0

0th

Percentile

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 No Results!