This repository contains the R package which is hosted on Bioconductor and our stable and development GitHub versions.

Installation

(macOS users only: Ensure you have installed XQuartz first.)

From Bioconductor

The best way to install mixOmics is using Bioconductor. You can see the landing page for the release version of mixOmics on Bioconductor here.

Make sure you have the latest R version and the latest BiocManager package installed following these instructions.

## install BiocManager if not installed
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

## install mixOmics
BiocManager::install('mixOmics')

## load mixOmics
library(mixOmics) 

You can also install the development version for new features yet to be widely tested:

BiocManager::install("mixOmicsTeam/mixOmics@development")

From Github

Bioconductor versions are updated twice a year, between these updates you can downlod the latest stable version of mixOmics from Github using:

devtools::install_github("mixOmicsTeam/mixOmics")

From Docker container

You can install our latest stable Github version of mixOmics via our Docker container. You can do this by downloading and using the Docker desktop application or via the command line as described below.

Click to expand

Note: this requires root privileges

1. Install Docker following instructions at https://docs.docker.com/docker-for-mac/install/

if your OS is not compatible with the latest version download an older version of Docker from the following link:

• MacOS: https://docs.docker.com/docker-for-mac/release-notes/
• Windows: https://docs.docker.com/docker-for-windows/release-notes/

Then open your system’s command line interface (e.g. Terminal for MacOS and Command Promot for Windows) for the following steps.

MacOS users only: you will need to launch Docker Desktop to activate your root privileges before running any docker commands from the command line.

2. Pull mixOmics container

docker pull mixomicsteam/mixomics

3. Ensure it is installed

The following command lists the running images:

docker images

This lists the installed images. The output should be something similar to the following:

$ docker images 
  > REPOSITORY                       TAG       IMAGE ID       CREATED         SIZE
  > mixomicsteam/mixomics            latest    e755393ac247   2 weeks ago     4.38GB

4. Activate the container

Running the following command activates the container. You must change your_password to a custom password of your own. You can also customise ports (8787:8787) if desired/necessary. see https://docs.docker.com/config/containers/container-networking/ for details.

docker run -e PASSWORD=your_password --rm -p 8787:8787 mixomicsteam/mixomics

5. Run

In your web browser, go to http://localhost:8787/ (change port if necessary) and login with the following credentials:

username: rstudio
password: (your_password set in step 4)

6. Inspect/stop

The following command lists the running containers:

sudo docker ps

The output should be something similar to the following:

$ sudo docker ps
  > CONTAINER ID   IMAGE                   COMMAND   CREATED         STATUS         PORTS                    NAMES
  > f14b0bc28326   mixomicsteam/mixomics   "/init"   7 minutes ago   Up 7 minutes   0.0.0.0:8787->8787/tcp   compassionate_mestorf

The listed image ID can then be used to stop the container (here f14b0bc28326)

docker stop f14b0bc28326

Contribution

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!) visit: https://github.com/mixOmicsTeam/mixOmics/issues. We fully welcome and appreciate well-formatted and detailed pull requests. Preferably with tests on our datasets.

Set up development environment

• Install the latest version of R
• Install RStudio
• Clone this repo, checkout master branch, pull origin and then run:

install.packages("renv", Ncpus=4)
install.packages("devtools", Ncpus=4)

# restore the renv environment
renv::restore()

# or to initialise renv
# renv::init(bioconductor = TRUE)

# update the renv environment if needed
# renv::snapshot()

# test installation
devtools::install()
devtools::test()

# complete package check (takes a while)
devtools::check()

Discussion forum

We wish to make our discussions transparent so please direct your analysis questions to our discussion forum https://mixomics-users.discourse.group. This forum is aimed to host discussions on choices of multivariate analyses, 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 - 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 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?

www.mixOmics.org (tutorials and resources)

Our latest bookdown vignette: https://mixomicsteam.github.io/mixOmics-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 preferred nomenclature), CCA for Canonical Correlation Analysis.

That’s it! Ready! Set! Go!

Thank you for using mixOmics!

What’s New

March 2022

• bug fix implemented for Issue #196. perf() can now handle features with a (s)pls which have near zero variance.
• bug fix implemented for Issue #192. predict() can now handle when the testing and training data have their columns in different orders.
• bug fix implemented for Issue #178. If the indY parameter is used in block.spls(), circosPlot() can now properly identify the $Y$ dataframe.
• bug fix implemented for Issue #172. perf() now returns values for the choice.ncomp component when nrepeat $< 3$ whereas before it would just return NAs.
• bug fix implemented for Issue #171. cim() now can take pca objects as input.
• bug fix implemented for Issue #161. tune.spca() can now handle NA values appropriately.
• bug fix implemented for Issue #150. Provided users with a specific error message for when plotArrow() is run on a (mint).(s)plsda object.
• bug fix implemented for Issue #122. Provided users with a specific error message for when a splsda object that has only one sample associated with a given class is passed to perf().
• bug fix implemented for Issue #120. plotLoadings() now returns the loading values for features from all dataframes rather than just the last one when operating on a (mint).(block).(s)plsda object.
• bug fix implemented for Issue #43. Homogenised the way in which tune.mint.splsda() and perf.mint.splsda() calculate balanced error rate (BER) as there was disparity between them. Also made the global BER a weighted average of BERs across each study.
• enhancement implemented for Issue #30/#34. The parameter verbose.call was added to most of the methods. This parameter allows users to access the specific values input into the call of a function from its output.
• bug fix implemented for Issue #24. background.predict() can now operate on mint.splsda objects and can be used as part of plotIndiv().

July 2021

• new function plotMarkers to visualise the selected features in block analyses (see #134)
• tune.spls now able to tune the selected variables on both X and Y. See ?tune.spls
• new function impute.nipals to impute missing values using the nipals algorithm
• new function tune.spca to tune the number of selected variables for pca components
• circosPlot now has methods for block.spls objects. It can now handle similar feature names across blocks. It is also much more customisable. See advanced arguments in ?circosPlot
• new biplot function for pca and pls objects. See ?mixOmics::biplot
• plotDiablo now takes col.per.group (see #119)

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 near.zero.variance=TRUE now fixed