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mixOmics (version 6.0.0)

Omics Data Integration Project

Description

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.

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Version

Install

install.packages('mixOmics')

Monthly Downloads

210

Version

6.0.0

License

GPL (>= 2)

Maintainer

Kim-Anh Cao

Last Published

June 14th, 2016

Functions in mixOmics (6.0.0)

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