This repository contains the R package which is hosted on Bioconductor and our development GitHub versions. Go to www.mixomics.org for information on how to use mixOmics.

Installation

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

From Bioconductor (recommended)

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) 

From Github

Bioconductor versions are updated twice a year, between these updates you can download the latest version of mixOmics from Github. Note that this latest version of mixOmics is under development and may not be stable, check the gitHub page for releases which have passed package testing.

## install devtools
install.packages("devtools")

## install latest github version of mixOmics
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!

What’s New

November 2024

• enhancement request #216 implemented parallel processing using BPPARAM across all tune() functions
• feature request #335 added seed argument to perf() functions for better reproducibility
• feature request #334 added seed argument to tune() functions for better reproducibility
• bug fix implemented for #303 multiple solutions found in perf() returns error
• bug fix implemented for #307 plotIndiv() ellipses colours not matching points, now sample group order is respected and colours can be customised for points and ellipses
• updated documentation to fix issue #297 broken link in bookdown vignette
• updated documentation to fix issue #296 typo in vignette

The performance assessment and parameter tuning workflow has been streamlined as described in issue #343

• New function: perf.assess() This function essentially runs perf() on final model but only returns performance metrics for the number of components used in the final model. Designed to be used in the final step of the workflow for quantifying final model performance. Outputs a list of values but no plotting functionality avaliable. See PR 344 for more details.

• Additional functionality for tune() functions and new tune() functions created tune() can now be used in its original capacity (to tune number of variables and components simultaneously) or just to tune number of components by internally calling perf(). Designed to be used for tuning both components and variables to keep across (s)PCA, (s)PLS, (s)PLSDA, block (s)PLSDA and mint (s)PLSDA models See PR #348 for more details.

October 2024

** Version 6.30.0 **

Bioconductor release version 6.30.0 released end of October 2024 Minor bug fixes and updated deprecated code and unit tests, no major code changes and no changes to user experience of mixOmics.

• bug fix implemented for #293 splsda() example code error

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