Package: mixOmics 6.29.0

Max Bladen

mixOmics: 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 variants of generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. Recently we implemented integrative methods to combine multiple data sets: N-integration with variants of Generalised Canonical Correlation Analysis and P-integration with variants of multi-group Partial Least Squares.

Authors:Kim-Anh Le Cao [aut], Florian Rohart [aut], Ignacio Gonzalez [aut], Sebastien Dejean [aut], Al J Abadi [ctb, cre], Max Bladen [ctb], Benoit Gautier [ctb], Francois Bartolo [ctb], Pierre Monget [ctb], Jeff Coquery [ctb], FangZou Yao [ctb], Benoit Liquet [ctb]

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NEWS

# Install 'mixOmics' in R:
install.packages('mixOmics', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/mixomicsteam/mixomics/issues

Datasets:

On BioConductor:mixOmics-6.29.0(bioc 3.20)mixOmics-6.28.0(bioc 3.19)

bioconductor-package

67 exports 3.43 score 56 dependencies 19 dependents 375 mentions 234 downloads

Last updated 2 months agofrom:3eefa3cdf8

Exports:aurocbackground.predictblock.plsblock.plsdablock.splsblock.splsdacimcimDiablocircosPlotcolor.GreenRedcolor.jetcolor.mixocolor.spectralexplained_varianceget.BERget.confusion_matriximgCorimpute.nipalsipcalogratio.transfomapmat.rankmint.block.plsmint.block.plsdamint.block.splsmint.block.splsdamint.pcamint.plsmint.plsdamint.splsmint.splsdamixOmicsnearZeroVarnetworknipalspcaperfplotArrowplotDiabloplotIndivplotLoadingsplotMarkersplotVarplsplsdarccselectVarsipcaspcasplssplsdastudy_splittunetune.block.splsdatune.mint.splsdatune.pcatune.rcctune.spcatune.splstune.splsdatune.splslevelunmapvipwithinVariationwrapper.rgccawrapper.sgccawrapper.sgccda

Dependencies:BHBiocParallelclicodetoolscolorspacecorpcorcpp11dplyrellipsefansifarverformatRfutile.loggerfutile.optionsgenericsggplot2ggrepelgluegridExtragtableigraphisobandlabelinglambda.rlatticelifecyclemagrittrMASSMatrixmatrixStatsmgcvmunsellnlmepillarpkgconfigplyrpurrrR6rARPACKRColorBrewerRcppRcppEigenreshape2rlangRSpectrascalessnowstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithr

mixOmics vignette

Kim-Anh Le Cao

Rendered fromvignette.Rmdusingknitr::rmarkdownon Jun 17 2024.

Last update: 2023-10-23
Started: 2018-10-04

Readme and manuals

Help Manual

Help pageTopics
'Omics Data Integration ProjectmixOmics-package
Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) curves for supervised classificationauroc auroc.list auroc.mint.block.plsda auroc.mint.block.splsda auroc.mint.plsda auroc.mint.splsda auroc.mixo_plsda auroc.mixo_splsda auroc.sgccda
Calculate prediction areasbackground.predict
biplot methods for 'pca' familybiplot biplot.mixo_pls biplot.pca
N-integration with Projection to Latent Structures models (PLS)block.pls
N-integration with Projection to Latent Structures models (PLS) with Discriminant Analysisblock.plsda
N-integration and feature selection with sparse Projection to Latent Structures models (sPLS)block.spls
N-integration and feature selection with Projection to Latent Structures models (PLS) with sparse Discriminant Analysisblock.splsda wrapper.sgccda
Breast Cancer multi omics data from TCGAbreast.TCGA
Human Breast Tumors Databreast.tumors
Clustered Image Maps (CIMs) ("heat maps")cim
Clustered Image Maps (CIMs) ("heat maps") for DIABLOcimDiablo
circosPlot for DIABLOcircosPlot circosPlot.block.pls circosPlot.block.plsda circosPlot.block.spls circosPlot.block.splsda
Color Palette for mixOmicscolor.GreenRed color.jet color.mixo color.spectral colors
16S microbiome data: most diverse bodysites from HMPdiverse.16S
Estimate the parameters of regularization for Regularized CCAestim.regul image.estim.regul pcatune
Calculates the proportion of explained variance of multivariate componentsexplained_variance
Create confusion table and calculate the Balanced Error Rateget.BER get.confusion_matrix
Plot the cross-validation score.image.tune.rcc plot.tune.rcc
Image Maps of Correlation Matrices between two Data SetsimgCor
Impute missing values using NIPALS algorithmimpute.nipals
Independent Principal Component Analysisipca
16S microbiome atherosclerosis studyKoren.16S
Linnerud Datasetlinnerud
Liver Toxicity Dataliver.toxicity
Log-ratio transformationlogratio-transformations logratio.transfo
Classification given Probabilitiesmap
Matrix Rankmat.rank
NP-integrationmint.block.pls
NP-integration with Discriminant Analysismint.block.plsda
NP-integration for integration with variable selectionmint.block.spls
NP-integration with Discriminant Analysis and variable selectionmint.block.splsda
P-integration with Principal Component Analysismint.pca
P-integrationmint.pls
P-integration with Projection to Latent Structures models (PLS) with Discriminant Analysismint.plsda
P-integration with variable selectionmint.spls
P-integration with Discriminant Analysis and variable selectionmint.splsda
PLS-derived methods: one function to rule them all!mixOmics
Multidrug Resistence Datamultidrug
Identification of zero- or near-zero variance predictorsnearZeroVar
Relevance Network for (r)CCA and (s)PLS regressionnetwork network.default network.pls network.rcc network.spls
Non-linear Iterative Partial Least Squares (NIPALS) algorithmnipals
Nutrimouse Datasetnutrimouse
Principal Components Analysispca
Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA, MINT and DIABLOperf perf.mint.pls perf.mint.plsda perf.mint.spls perf.mint.splsda perf.mixo_pls perf.mixo_plsda perf.mixo_spls perf.mixo_splsda perf.sgccda
Show (s)pca explained variance plotsplot.pca
Plot for model performance for PSLDA analysesplot.perf plot.perf.mint.plsda.mthd plot.perf.mint.splsda.mthd plot.perf.plsda.mthd plot.perf.sgccda.mthd plot.perf.splsda.mthd
Plot for model performance for PLS analysesplot.perf.pls plot.perf.pls.mthd plot.perf.spls.mthd
Canonical Correlations Plotplot.rcc
Plot model performanceplot.tune plot.tune.block.splsda plot.tune.spca plot.tune.spls plot.tune.spls1 plot.tune.splsda
Arrow sample plotplotArrow
Graphical output for the DIABLO frameworkplot.sgccda plotDiablo
Plot of Individuals (Experimental Units)plotIndiv plotIndiv.mint.pls plotIndiv.mint.plsda plotIndiv.mint.spls plotIndiv.mint.splsda plotIndiv.mixo_pls plotIndiv.pca plotIndiv.rgcca plotIndiv.sgcca
Plot of Loading vectorsplotLoadings plotLoadings.mint.pls plotLoadings.mint.plsda plotLoadings.mint.spls plotLoadings.mint.splsda plotLoadings.mixo_pls plotLoadings.mixo_plsda plotLoadings.mixo_spls plotLoadings.mixo_splsda plotLoadings.pca plotLoadings.pls plotLoadings.rcc plotLoadings.rgcca plotLoadings.sgcca plotLoadings.sgccda plotLoadings.spls
Plot the values for multivariate markers in block analysesplotMarkers
Plot of VariablesplotVar plotVar.pca plotVar.pls plotVar.plsda plotVar.rcc plotVar.rgcca plotVar.sgcca plotVar.spca plotVar.spls plotVar.splsda
Partial Least Squares (PLS) Regressionpls
Partial Least Squares Discriminant Analysis (PLS-DA).plsda
Predict Method for (mint).(block).(s)pls(da) methodspredict predict.block.pls predict.block.spls predict.mint.block.pls predict.mint.block.plsda predict.mint.block.spls predict.mint.block.splsda predict.mint.pls predict.mint.plsda predict.mint.spls predict.mint.splsda predict.mixo_pls predict.mixo_spls predict.pls predict.plsda predict.spls predict.splsda
Print Methods for CCA, (s)PLS, PCA and Summary objectsprint print.ipca print.mint.pls print.mint.plsda print.mint.spls print.mint.splsda print.mixo_pls print.mixo_plsda print.mixo_spls print.mixo_splsda print.pca print.perf.mint.splsda.mthd print.perf.pls.mthd print.perf.plsda.mthd print.perf.sgccda.mthd print.perf.splsda.mthd print.predict print.rcc print.rgcca print.sgcca print.sgccda print.sipca print.spca print.summary print.tune.block.splsda print.tune.mint.splsda print.tune.pca print.tune.pls print.tune.rcc print.tune.spca print.tune.spls1 print.tune.splsda
Regularized Canonical Correlation Analysisrcc rcc.default
Output of selected variablesselect.var selectVar selectVar.mixo_pls selectVar.mixo_spls selectVar.pca selectVar.rgcca selectVar.sgcca
Independent Principal Component Analysissipca
Sparse Principal Components Analysisspca
Sparse Partial Least Squares (sPLS)spls
Sparse Partial Least Squares Discriminant Analysis (sPLS-DA)splsda
Small version of the small round blue cell tumors of childhood datasrbct
Human Stem Cells Datastemcells
divides a data matrix in a list of matrices defined by a factorstudy_split
Summary Methods for CCA and PLS objectssummary summary.mixo_pls summary.mixo_spls summary.pca summary.rcc
Wrapper function to tune pls-derived methods.tune
Tuning function for block.splsda method (N-integration with sparse Discriminant Analysis)tune.block.splsda
Estimate the parameters of mint.splsda methodtune.mint.splsda
Tune the number of principal components in PCAtune.pca
Estimate the parameters of regularization for Regularized CCAtune.rcc
Tune number of selected variables for spcatune.spca
Tuning functions for sPLS and PLS functionstune.spls
Tuning functions for sPLS-DA methodtune.splsda
Tuning functions for multilevel sPLS methodtune.splslevel
Dummy matrix for an outcome factorunmap
Vaccine study Datavac18
Simulated data based on the vac18 study for multilevel analysisvac18.simulated
Variable Importance in the Projection (VIP)vip
Within matrix decomposition for repeated measurements (cross-over design)withinVariation
mixOmics wrapper for Regularised Generalised Canonical Correlation Analysis (rgcca)wrapper.rgcca
mixOmics wrapper for Sparse Generalised Canonical Correlation Analysis (sgcca)wrapper.sgcca
Yeast metabolomic studyyeast