Category: Updates

mixOmics 6.30.0 on Bioconductor

At the end of October 2024 Bioconductor updated to version 3.20, and with it updated to the latest version of mixOmics 6.30.0. You can install the latest version of mixOmics on Bioconductor here. This latest release version of the package runs on R version 4.4 and includes some minor bug fixes and updated code and unit tests. See our Github page for more details on these updates.

[open] Self-paced online course Feb 24 – April 11, 2025

Single and multi-omics analysis and integration with mixOmics

This course is designed for:
  • Beginners looking for an introduction to mixOmics methods for single- and multi-omics analyses.
  • Current mixOmics users who want to deepen their understanding of the mixOmics methods.
  • Users who would like more guidance on analyzing their own data (we also provide exemplar datasets).

The workshop is self-paced and spans across 7 weeks. There are 4 Q&A live sessions, and many opportunities to interact with the cohort and your instructor Prof Kim-Anh Lê Cao via Slack. BYO data is encouraged: we provide advice so that you can analyse your own data with mixOmics tools as part of your learning process.  A good working knowledge in R programming (e.g. handling data frame, perform simple calculations and display simple graphical outputs) is essential to fully benefit from the course*. 

According to our past participants, a time commitment of 5-8h/week was sufficient to feel that they were progressing. Here is some feedback from a previous course.

We provide a certificate of attendance or completion.

Register here, places are limited!

Fees

Research Higher Degree students enrolled at a University: $495 AUD (incl. GST) [discount code: MIXO_RHD]

Staff and members from Universities & Not-for-profit organisations: $825 (incl. GST) [discount code: MIXO_NFP_STAFF]

Other industries: $1320 AUD (incl. GST)

discounts of 5% for a group of 3-9 learners and 10% for 10+ learners, however, this will require a single invoice per group.

These funds go towards the support of a software developer to maintain the package. If you need an invoice, contact Student Support at continuing-education[at]unimelb.edu.au

Teaching Period Dates

Teaching commences: Monday, 24 Feb 2025, 9:00 am AEST

Teaching concludes: Sunday, 23 March 2025, 11:59 pm AEST (after 4 weeks)

(non marked) Assessment due: Friday 4 April 2025 (2 weeks prep)

Peer-review of assessment due: Friday 11 April 2025 (1 week prep)

The course is divided into theory (50%) and hands-on practice, with the opportunity to analyse your own data. The exercises and assignments are in R. Participants are encouraged to use RStudio and Rmarkdown (template and R code provided).

*Need an R refresher?

Learners who are not proficient in R do not get the full benenefit of the course (based on their own, honest, feedback!) For those looking for an R refresher well ahead of the course:

[Closed] Self-paced online course Oct 31st – Nov 27 2022

The next iteration of the course will be in September 2023 for a likely duration of 6-8 weeks (it will be advertised 3 months before opening the course). This course is online, but at your own pace, meaning that you need to dedicate enough time (5-8h per week) to fully benefit from the program.

Feedback from the 2022 iteration:

  • You can do it at your own time since the resources provided (Webinars and reading material) are very helpful. Due to working hours I had to watch/read on demand (at my own time)
  • Kim-Anh has done a very good job in the webinars and was generally approachable and helpful. Thank you! The online course material was very good and explained the basics of the program quite well. The integration with the mixOmics online material and sample cases is very helpful.
  • It had the option to attend live webinars (two offered times) or watch recordings. – The possibility to ask questions was available for both live webinars and stack. – The assignments are designed to enhance further learning allowing to use of either own data or provided data at different challenge skills.
  • Course organisers were very responsive to our questions in Slack. Modules flowed nicely and were well organised. Webinars were useful.

This is our second round of online course ‘mixOmics R Essentials for Biological Data Integration‘ that includes 4 weeks of asynchronous learning (with one live summary + Q&A per week), numerous chats on Slack and an additional 3 weeks to complete the assignment. Some feedback from our last round can be found here. Our last survey seem to suggest most learners spent between 5-8h per week on the program.

  • Teaching Period Dates, asynchronised:
    • Start – Monday, 31st October 2022
    • End – Sunday, 27th November 2022
    • (non marked) Assessment due Sunday, 9th December 2022
    • Peer-review of assessment due Sunday, 16th December 2022
  • Fees vary for
    • Research Higher Degree students enrolled at a University: $495 AUD (incl. GST)
    • Staff and members from Universities & Not-for-profit organisations: $825 (incl. GST)
    • Other industries: $1320 AUD (incl. GST)
    • discounts of 5% for a group of 3-9 learners and 10% for 10+ learners, however, this will require a single invoice per group.

(these funds go towards the support of a software developer to maintain the package)

Information about the course and registration: https://study.unimelb.edu.au/find/short-courses/mixomics-r-essentials-for-biological-data-integration/

The number of places is limited, so first come first serve (we aim to run this course twice a year).

What if I need an invoice? Contact Student Support at continuing-education[at]unimelb.edu.au

Prerequisites. A good working knowledge in R programming (e.g. handling data frame, perform simple calculations and display simple graphical outputs) is essential to fully benefit from the course*. The course is divided into theory (50%) and hands-on practice, with the opportunity to analyse your own data. The exercises and assignments are in R. Participants are encouraged to use RStudio and Rmarkdown (template and R code provided).

*Learners who are not proficient in R do not get the full benefit of the course (based on their own, honest, feedback!)

Our book is out!

We are excited to announce that our book is out, along with several case studies and R scripts available online. Check out this page.

It’s been a very (very) long term project, and a great collaboration with Zoe Welham whose dedication and patience helped shape this project into a readable whole! A huge thank you to Al Abadi, who tirelessly helped updating the package as we developed the content.

We are moving …. to bioC!

Dear all,

After 9 years hosted at the R CRAN we are migrating to bioconductor! It’s been a great first journey and we are grateful to the R CRAN for hosting our package. We are now ready for the next adventure.

Why are we moving?

  • It is our aspiration to empower computational and molecular biologists, which aligns with bioC vision.
  • We will be able to link with new experimentClass S4 objects and existing data packages using them in bioC, ranging from multi omics, microbiome and single cell.
  • We will be able to provide better vignettes and examples that will complement our website.

What has changed? What should I do? Should I panic?

So far we have allowed as little disruptions as possible, so the call of the functions and objects are the same. Gradually we will be adding more capabilities, which will grandly improve your usability (see above for the S4 class).

We are almost on bioC but the full acceptance is pending on the removal of mixOmics on the R CRAN. We fixed a few bugs, if you would like to install this new version:

The development version is now accessible on gitHub (feel free to fork / help* / comment on gitHub):

R>install_github("mixOmicsTeam/mixOmics")

Or alternatively, once we will be in bioConductor:

R> if (!requireNamespace("BiocManager", quietly = TRUE))  install.packages("BiocManager")
R> BiocManager::install("mixOmics", version = "3.8")

Then, business as usual!

* We would like to formally acknowledge the help of Lluís Revilla (Centre Esther Koplowitz, Barcelona) for helping us with setting up some testthat checks for our bioC version.

As we enter this new journey, we also thank you for this.
And also for this!

PS: a one-day microbiome workshop is scheduled in chilly Vancouver on November 6.

News 2018, workshops 2018 and DIABLO

Dear all,

The first few months of the year have been busy for us. Thanks to your support, we have been ranked second to the Bioinformatics Peer Prize (57 votes, so close after the winner with 59 votes!). Our entry is listed at this link if you would like to watch a basic introduction to the package.

For those who are new to mixOmics, I also cooked some prezi slides to introduce the broad context of where mixOmics sits, which was presented at the University of Melbourne ResBaz event in February.

We have now scheduled our 2018 workshops:

  • An advanced workshop focusing on omics data integration 7-8 June in the Parisian region. The registration will be in two stages: Expression of Interest due on April 29, followed by registration. The workshop will accommodate 30 participants. More details here. 
  • A 3-day beginner workshop 23-25 July at the University of Melbourne. More details will be populated very soon.

We have pushed the second version of our DIABLO manuscript on bioRxiv. The codes are currently on gitHub but they will also be rendered on our website soon.

For some little news, you can also follow us on Twitter @mixOmics_team.

 

Kim-Anh for the mixOmics team

A quick video introduction for mixOmics, vote for us!

Dear mixOmics friends, users, and adventurers,

We are reaching out to you to get your unbiased vote 😉 for the Bioinformatics PeerPrize III where we promote our latest publication in PLoS Computational Biology as a software article.

For those not familiar with the package, the little 3min video will give you a brief introduction to the topics of

  • `omics data integration in systems biology
  • multivariate dimension reduction techniques
  • mixOmics: what is it?
  • our main integrative methods DIABLO and MINT

This prize is a great opportunity for us to disseminate the toolkit. As you know, software development and obtaining resources to do so is not a piece of cake, but we managed, along the years. In 2017 the package was downloaded 29,000 times and is still going strong! thanks to your support and your invaluable feedback.

Vote for us if you like our entry! Votes closeon Feb 19. Thank you!

https://bioinformatics-peer-prize-iii.thinkable.org
(it will require the entry of your organisation and a ref of a paper where you were co-author on. They take this seriously!)

More news about what is coming up in 2018 for mixOmics very soon. We wish you many successful mixOmics analyses to you all for 2018!

6.3.1 on CRAN: bug fixes and latest news

We pushed 6.3.1 following a major bug in 6.3.0 when dealing with missing values (especially with DIABLO). Another bug related to the one-sided t-test in the tune functions.  All good now. Nipals is also faster to run.

A big thank to the users who give us feedback via our bitbucket issue list, this is very useful to us to continue improving the package.

The 3 workshops we ran in October and November 2017 were a success. The first Advanced workshop resulted in many stimulating discussions that will help the development team to move forward.  The two beginner workshops were also a lot of fun. We are particularly pleased to see how the small mixOmics community is growing!

Our paper has finally been published in Plos Computational Biology as a software article. The main methods are described in the poster below. We are now working on the long awaited DIABLO manuscript so that it leaves bioaRxiv and has its life of its own!

In the next few months these are the changes we are planning ahead:

  • a conversion to bioconductor. Ain’t no fear, it should not affect the function calls. We think it is now the right time to reach the bioconductor community, but that implies a fair amount of implementation on our side. Consequently the methods development will slow down in the coming few months.
  • a mixOmics forum to encourage discussions around the 19 methods we have now currently available.

Summary of the mixOmics article in PLoS Comp Biol

Version 6.3.0 and workshop

A new CRAN version is now available. We have considerably improved the computational time for the tune and perf functions! (see example below). We also fixed some reproducibility issues when using parallel computing with a set seed.

The update of the package will require new dependencies: ‘matrixStats’, ‘rARPACK’, ‘gridExtra’

There are still some spots left for the beginner mixOmics workshop in Toulouse, 9-10 Nov. Details here.

 

Enhancements:
————-
– huge gain in computation time for the tune functions tune.splsda and tune.block.splsda. The larger the data, the bigger the gain. Requires new dependencies: ‘matrixStats’, ‘rARPACK’, ‘gridExtra’
– a plot for an object `tune.block.splsda’
– tune.multilevel function was deprecated a while ago and now removed.

Bug fixes:
———-
– fixed reproducibility problem when using parallel coding in tune.block.splsda (via the `cpus’ argument)
– network: correlation with missing values fixed, label names fixed
– fixed perf for block.splsda objects with prediction distances
– some NA issues reported in 6.2.0 fixed (hopefully)

 

The gain in computational time is reported below for our different supervised frameworks. It all depends on your operating system, but generally, the user time  =  execution of the code, the system time = system processes (e.g opening and closing files), and the elapsed time is the difference in times since we started the stopwatch.

6.2.0, 2 postdoc positions and workshops

Dear mixOmics users,

Our new update 6.2.0 is now available on CRAN as part of our new version of our manuscript.

manuscript & package update:

The mixOmics manuscript introducing the supervised and integrative frameworks (PLS-DA, DIABLO block.plsda and MINT) has be updated, along with all the R / Sweave case studies, manuscript and codes are available at this link.  The case studies are also published on our website (sPLSDA:SRBCT, Case study: TCGA and Case study: MINT).

The manuscript describes in more details the difference prediction distances (see also the supplemental material) and the interpretation of the AUROC for our supervised methods.

The constraint argument was removed from all our methods, due to a risk of overfitting.

New features:

– The constraint argument (version 6.1.0 – 6.1.3) was removed in the functions perf and tune for all supervised objects because of a risk of overfitting

Enhancements:

– AUROC aded for MINT objects mint.plsda and mint.splsda where the study name needs to be specified, e.g. auroc( .., roc.study = “study4”). See ?auroc

– choice.ncomp output added on all perf and tune functions for all supervised methods.

– mat.c output for pls and plsda objects (matrix of coefficients from the regression of X / residual matrices X on the X-variates).

Bug fixes (thank you to the users who notified us on bitbucket):

– fixed bug when using predict, perf or tune with the error msg: ‘Error in predict.spls(spls.res, X.test[, nzv]) : ‘newdata’ must include all the variables of ‘object$X”

Workshops:

We advertised two workshops at this link. The advanced workshop 23-24 Oct 2017 is fully subscribed. This is our first MAW (mixOmics advanced workshop), but there will be more planned in 2018. We still have a few spots left for the classic workshop on the 9-10 Nov 2017 in Toulouse, contact us for more information (priority will be given to students and early career researchers).

Two senior postdoc positions (2 year and 3 year) still open!

The Australian mixOmics team now based at the University of Melbourne is recruiting two senior postdocs in the fields of computational biology or statistics, 1 full time 2-year position to work with the Stemformatics team on exciting omics integrating problems (‘omics and single cell omics) to improve stem cell classification, and 1 full time 3-year position for innovative multivariate methods developments for ‘omics time course, microbiome and P-integration. Contact us for more information.

Website update:

With the invaluable help from the bioinformatics masters students Danielle Davenport and Zoe Welham we are currently revamping the website to ensure all codes are running correctly. Thank you for those who sent us some feedback!