<!-- PLEASE DO NOT EDIT ./README.md BY HAND, EDIT ./inst/README.Rmd AND RENDER TO CREATE ./README.md --> [](https://bioconductor.org/packages/stats/bioc/mixOmics) [](https://github.com/mixOmicsteam/mixOmics/actions) [](https://github.com/mixOmicsTeam/mixOmics/commits/master) [](https://app.codecov.io/gh/mixOmicsTeam/mixOmics) [-lightgrey.svg)](https://choosealicense.com/) [](http://bioconductor.org/packages/release/bioc/html/mixOmics.html#since)  This repository contains the `R` package which is [hosted on Bioconductor](http://bioconductor.org/packages/release/bioc/html/mixOmics.html) and our stable and development `GitHub` versions. ## Installation (**macOS users only:** Ensure you have installed [XQuartz](https://www.xquartz.org/) 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](https://bioconductor.org/packages/release/bioc/html/mixOmics.html). Make sure you have the latest R version and the latest `BiocManager` package installed following [these instructions](https://www.bioconductor.org/install/). ``` r ## 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 downlod the latest stable version of `mixOmics` from `Github` using: ``` r BiocManager::install('mixOmicsTeam/mixOmics') ``` You can also install the [development version](https://github.com/mixOmicsTeam/mixOmics/tree/development) for new features yet to be widely tested: ``` r BiocManager::install("mixOmicsTeam/mixOmics@development") ``` ### 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. <details> <summary> Click to expand </summary> **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 ``` bash docker pull mixomicsteam/mixomics ``` 3) Ensure it is installed The following command lists the running images: ``` bash 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. ``` bash 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: ``` bash sudo docker ps ``` The output should be something similar to the following: ``` bash $ 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`) ``` bash docker stop f14b0bc28326 ``` </details> ## Contribution We welcome community contributions concordant with [our code of conduct](https://github.com/mixOmicsTeam/mixOmics/blob/master/CODE_OF_CONDUCT.md). We strongly recommend adhering to [Bioconductor’s coding guide](https://bioconductor.org/developers/how-to/coding-style/) 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. <details> <summary> Set up development environment </summary> - Install the latest version of R - Install RStudio - Clone this repo, checkout master branch, pull origin and then run: ``` r 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() ``` </details> ### 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 #### November 2024 - enhancement request [\#216](https://github.com/mixOmicsTeam/mixOmics/issues/216) implemented parallel processing using `BPPARAM` across all `tune()` functions - feature request [\#335](https://github.com/mixOmicsTeam/mixOmics/issues/335) added `seed` argument to `perf()` functions for better reproducibility - feature request [\#334](https://github.com/mixOmicsTeam/mixOmics/issues/334) added `seed` argument to `tune()` functions for better reproducibility - bug fix implemented for [\#303](https://github.com/mixOmicsTeam/mixOmics/issues/303) multiple solutions found in `perf()` returns error - bug fix implemented for [\#307](https://github.com/mixOmicsTeam/mixOmics/issues/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](https://github.com/mixOmicsTeam/mixOmics/issues/297) broken link in bookdown vignette - updated documentation to fix issue [\#296](https://github.com/mixOmicsTeam/mixOmics/issues/296) typo in vignette The performance assessment and parameter tuning workflow has been streamlined as described in issue [\#343](https://github.com/mixOmicsTeam/mixOmics/issues/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](https://github.com/mixOmicsTeam/mixOmics/pull/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](https://github.com/mixOmicsTeam/mixOmics/pull/348) for more details. #### October 2024 \*\* Version 6.30.0 \*\* [Bioconductor release version 6.30.0](https://bioconductor.org/packages/release/bioc/html/mixOmics.html) 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](https://github.com/mixOmicsTeam/mixOmics/issues/293) `splsda()` example code error #### March 2022 - bug fix implemented for [Issue \#196](https://github.com/mixOmicsTeam/mixOmics/issues/196). `perf()` can now handle features with a `(s)pls` which have near zero variance. - bug fix implemented for [Issue \#192](https://github.com/mixOmicsTeam/mixOmics/issues/192). `predict()` can now handle when the testing and training data have their columns in different orders. - bug fix implemented for [Issue \#178](https://github.com/mixOmicsTeam/mixOmics/issues/178). If the `indY` parameter is used in `block.spls()`, `circosPlot()` can now properly identify the $Y$ dataframe. - bug fix implemented for [Issue \#172](https://github.com/mixOmicsTeam/mixOmics/issues/172). `perf()` now returns values for the `choice.ncomp` component when `nrepeat` $< 3$ whereas before it would just return `NA`s. - bug fix implemented for [Issue \#171](https://github.com/mixOmicsTeam/mixOmics/issues/171). `cim()` now can take `pca` objects as input. - bug fix implemented for [Issue \#161](https://github.com/mixOmicsTeam/mixOmics/issues/161). `tune.spca()` can now handle `NA` values appropriately. - bug fix implemented for [Issue \#150](https://github.com/mixOmicsTeam/mixOmics/issues/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](https://github.com/mixOmicsTeam/mixOmics/issues/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](https://github.com/mixOmicsTeam/mixOmics/issues/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](https://github.com/mixOmicsTeam/mixOmics/issues/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](https://github.com/mixOmicsTeam/mixOmics/issues/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](https://github.com/mixOmicsTeam/mixOmics/issues/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 <https://github.com/mixOmicsTeam/mixOmics/issues/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](https://github.com/mixOmicsTeam/mixOmics/issues/57) - `plotIndiv(..., ind.names=FALSE)` [warning issue](https://github.com/mixOmicsTeam/mixOmics/issues/59) 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