mixOmicsTeam / Mixomics
Projects that are alternatives of or similar to Mixomics
This repository contains the
R package now hosted on
and our current
(Mac OS Users Only:) Ensure you have installed XQuartz first.
Make sure you have the latest R version and the latest
package installed following these
instructions (if you use legacy
R versions (<=3.5.0) refer to the instructions at the end of the
## install BiocManager if not installed if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") ## ensure the following returns TRUE, or follow guidelines BiocManager::valid()
Latest Bioconductor Release
You can then install
mixOmics using the following
## install mixOmics BiocManager::install('mixOmics')
Install the latest stable version (see below for latest development version) of
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:
library(mixOmics) #> Loaded mixOmics ?.?.?
We welcome community contributions concordant with our code of
We strongly recommend adhering to Bioconductor’s coding
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!): https://github.com/mixOmicsTeam/mixOmics/issues. We fully welcome and appreciate well-formatted and detailed pull requests. Preferrably with tests on our datasets.
We wish to make our discussions transparent so please direct your questions to our discussion forum https://mixomics-users.discourse.group. 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!
mixOmics is collaborative project between Australia (Melbourne),
France (Toulouse), and Canada (Vancouver). The core team includes
Kim-Anh Lê Cao - https://lecao-lab.science.unimelb.edu.au (University
of Melbourne), Florian Rohart - http://florian.rohart.free.fr
(Toulouse) and Sébastien Déjean -
https://perso.math.univ-toulouse.fr/dejean/. 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
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?
www.mixOmics.org (tutorials and resources)
Our latest bookdown vignette: https://mixomicsteam.github.io/Bookdown/.
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
- New biplot now available for
pcafamily. See the examples in this issue
- weighted consensus plots for DIABLO objects now consider per-component weights
plotIndivnow supports (weighted) consensus plots for block analyses. See the example in this issue
plotIndiv(..., ind.names=FALSE)warning issue now fixed
perf.block.splsdanow supports calculation of combined AUC
block.splsdabug which could drop some classes with
Parallel computing improved for
perffunctions, and even more on Unix-like systems
Fixed margin error problem with
plotLoadings. See the example in this issue
cimbug which overestimated correlations for single component now fixed
perf.sgccdanow supports calculation of average combined AUC
- You can now customise
aurocplots in version 6.8.3. See example here