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This repository contains the R package now hosted on Bioconductor and our current GitHub version.


(Mac OS Users Only:) Ensure you have installed XQuartz first.

Make sure you have the latest R version and the latest BiocManager package installed following these instructions (if you use legacy R versions (<=3.5.0) refer to the instructions at the end of the mentioned page).

## install BiocManager if not installed
if (!requireNamespace("BiocManager", quietly = TRUE))
## ensure the following returns TRUE, or follow guidelines

Latest Bioconductor Release

You can then install mixOmics using the following code:

## install mixOmics

GitHub Versions

Stable version

Install the latest stable version (see below for latest development version) of mixOmics from GitHub (as bug-free as it can be):


Check after installation that the following code does not throw any error (especially Mac users - refer to installation instructions) and that the welcome message confirms you have installed the latest version:

#> Loaded mixOmics ?.?.?
Development version

You can also install the development version for new features yet to be widely tested (see What's New):



We welcome community contributions concordant with our code of conduct. We strongly recommend adhering to Bioconductor’s coding guide for software consistency if you wish to contribute to mixOmics R codes.

Bug reports and pull requests

To report a bug (or offer a solution for a bug!): We fully welcome and appreciate well-formatted and detailed pull requests. Preferrably with tests on our datasets.

Discussion forum

We wish to make our discussions transparent so please direct your questions to our discussion forum This forum is aimed to host discussions on choices of multivariate analyses, bug report as well as comments and suggestions to improve the package. We hope to create an active community of users, data analysts, developers and R programmers alike! Thank you!

About the mixOmics team

mixOmics is collaborative project between Australia (Melbourne), France (Toulouse), and Canada (Vancouver). The core team includes Kim-Anh Lê Cao - (University of Melbourne), Florian Rohart - (Toulouse) and Sébastien Déjean - We also have key contributors, past (Benoît Gautier, François Bartolo) and present (Al Abadi, University of Melbourne) and several collaborators including Amrit Singh (University of British Columbia), Olivier Chapleur (IRSTEA, Paris), Antoine Bodein (Universite de Laval) - it could be you too, if you wish to be involved!.

The project started at the Institut de Mathématiques de Toulouse in France, and has been fully implemented in Australia, at the University of Queensland, Brisbane (2009 – 2016) and at the University of Melbourne, Australia (from 2017). We focus on the development of computational and statistical methods for biological data integration and their implementation in mixOmics.

Why this toolkit?

mixOmics offers a wide range of novel multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. Single ‘omics analysis does not provide enough information to give a deep understanding of a biological system, but we can obtain a more holistic view of a system by combining multiple ‘omics analyses. Our mixOmics R package proposes a whole range of multivariate methods that we developed and validated on many biological studies to gain more insight into ‘omics biological studies.

Want to know more? (tutorials and resources)

Our latest bookdown vignette:

Different types of methods

We have developed 17 novel multivariate methods (the package includes 19 methods in total). The names are full of acronyms, but are represented in this diagram. PLS stands for Projection to Latent Structures (also called Partial Least Squares, but not our prefered nomenclature), CCA for Canonical Correlation Analysis.

That’s it! Ready! Set! Go!

Thank you for using mixOmics!

What’s New

September 2020

  • New biplot now available for pca family. See the examples in this issue

April 2020

  • weighted consensus plots for DIABLO objects now consider per-component weights

March 2020

  • plotIndiv now supports (weighted) consensus plots for block analyses. See the example in this issue

  • plotIndiv(..., ind.names=FALSE) warning issue now fixed

January 2020

  • perf.block.splsda now supports calculation of combined AUC
  • block.splsda bug which could drop some classes with now fixed

November 2019

  • Parallel computing improved for tune and perf functions, and even more on Unix-like systems

  • Fixed margin error problem with plotLoadings. See the example in this issue

  • cim bug which overestimated correlations for single component now fixed

  • perf.sgccda now supports calculation of average combined AUC

September 2019

  • You can now customise auroc plots in version 6.8.3. See example here