<!-- PLEASE DO NOT EDIT ./README.md BY HAND, EDIT ./inst/README.Rmd AND RENDER TO CREATE ./README.md --> [](https://github.com/mixOmicsteam/mixOmics/actions) [](https://www.bioconductor.org/packages/mixOmics) [](https://app.codecov.io/gh/mixOmicsTeam/mixOmics) [](https://bioconductor.org/packages/stats/bioc/mixOmics) [](https://github.com/mixOmicsTeam/mixOmics/commits/master) [-lightgrey.svg)](https://choosealicense.com/) [](http://bioconductor.org/packages/release/bioc/html/mixOmics.html#since)  This repository contains the `R` package [now 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.) Make sure you have the latest R version and the latest `BiocManager` package installed following [these instructions](https://www.bioconductor.org/install/) (if you use legacy R versions (\<=3.5.0) refer to the instructions at the end of the mentioned page). ``` r ## install BiocManager if not installed if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") ``` Ensure the following returns `TRUE`, or follow the guidelines provided by the output. ``` r BiocManager::valid() ``` For installation in R, see options a) and b). For Docker containers, see c). #### a) Latest `Bioconductor` Release You can then install `mixOmics` using the following code: ``` r ## install mixOmics BiocManager::install('mixOmics') ``` #### b) `GitHub` Versions ##### Stable version Install the latest stable version (see below for latest [development](https://github.com/ajabadi/mixOmics#development-version) version) of `mixOmics` from `GitHub` (as bug-free as it can be): ``` r BiocManager::install("mixOmicsTeam/mixOmics") ``` Check after installation that the following code does not throw any error (especially Mac users - refer to [installation instructions](#installation)) and that the welcome message confirms you have installed [the latest version](https://github.com/mixOmicsTeam/mixOmics/blob/master/DESCRIPTION#L4): ``` r library(mixOmics) #> Loaded mixOmics ?.?.? ``` ##### Development version You can also install the [development version](https://github.com/mixOmicsTeam/mixOmics/blob/devel/DESCRIPTION#L4) for new features yet to be widely tested (see [What’s New](/https://github.com/ajabadi/mixOmics#whats-new)): ``` r BiocManager::install("mixOmicsTeam/mixOmics@devel") ``` #### c) `Docker` container of the stable GitHub version <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) Active 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/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 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](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  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`  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