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README.Rmd
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---
output: github_document
description: |
"This file creates README.md, README.md MUST NOT BE CHANGED MANUALLY"
"The last chunk includes the What's New file, can safely be excluded by eval=FALSE"
---
```{r setup, include=FALSE}
library(knitr)
opts_chunk$set( echo = TRUE, eval = FALSE, warning = FALSE, fig.path = "./inst/whatsnew-files/", fig.asp = 1, out.width = "50%", cache = FALSE)
opts_knit$set( cache.path = "./inst/whatsnew-files/")
```
This repository contains the `R` package [now hosted on Bioconductor](http://bioconductor.org/packages/release/bioc/html/mixOmics.html) and our current `GitHub` version.
## Installation
**(Mac Users Only:)** Ensure you have installed [XQuartz](https://www.xquartz.org/) first.
#### Latest Bioconductor Release
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), you can then install `mixOmics` using the following code:
```{r}
## install BiocManager if not installed
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
## install mixOmics
BiocManager::install('mixOmics')
```
#### Latest `GitHub` Version
Install the [devtools](https://github.com/r-lib/devtools) package in R, then load it and install the latest stable version of `mixOmics` from `GitHub` (as bug-free as it can be):
```{r}
## install devtools if not installed
if (!requireNamespace("devtools", quietly = TRUE))
install.packages("devtools")
## install mixOmics
devtools::install_github("mixOmicsTeam/mixOmics")
```
You can also install the development version:
```{r}
devtools::install_github("mixOmicsTeam/mixOmics", ref="devel")
```
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 latest version as in the latest package [DESCRIPTION file](https://github.com/mixOmicsTeam/mixOmics/blob/master/DESCRIPTION#L4):
```{r}
library(mixOmics)
#> Loaded mixOmics ?.?.?
```
## 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 consistenncy.
### 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.
### Discussion forum
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!
## 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 prefered nomenclature), _CCA_ for _Canonical Correlation Analysis_.
That's it! Ready! Set! Go!
Thank you for using `mixOmics`!
```{r, eval=TRUE, child = './inst/README-WhatsNew.Rmd'}
```