# mixOmics v6.0.0

Monthly downloads

## 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 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! |

## Last month downloads

## Details

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 |

imports | corpcor , dplyr , ellipse , igraph , methods , parallel , plyr , RColorBrewer , reshape2 , rgl , tidyr |

depends | ggplot2 , lattice , MASS , R (>= 2.10) |

Contributors | Sebastien Dejean, Ignacio Gonzalez, Kim-Anh Cao, Florian Rohart |

#### Include our badge in your README

```
[![Rdoc](http://www.rdocumentation.org/badges/version/mixOmics)](http://www.rdocumentation.org/packages/mixOmics)
```