mixOmics

Development repository for the Bioconductor package 'mixOmics '

173

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

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:

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.

Pull mixOmics container

docker pull mixomicsteam/mixomics

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

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

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)

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 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
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