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.
Research Higher Degree students enrolled at a University: $550 AUD (incl. GST) [discount code: MIXO_RHD]
Staff and members from Universities & Not-for-profit organisations: $900 (incl. GST) [discount code: MIXO_NFP_STAFF]
Other industries: $1450 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, 23 Feb 2026, 9:00 am AEST (note Australian time!)
Q&A live webinars are scheduled on Thursdays 6pm AEST / 8am CET during the first 4 weeks (26th Feb, 5th, 12th and 19th March).
An additional session might be added on Fridays 9am AEST ( = Thursdays 2pm PST / 5pm EST / 9pm CET)
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:
We are planning an advanced, hands-on asynchronous workshop for researchers who have completed our beginner course. The program will focus on complex study designs, including batch effects, longitudinal and time-course data and practical strategies for analysis with multivariate approaches.
We are still shaping the format and length, and how much support we can provide for a select group!
If this is of interest, please submit a short expression of interest so we can tailor the workshop to your needs. You will also be first to hear when registrations open.
We have a few spots left for an in-person mixOmics workshop, which we would like to open to our wider community!
Modern high-throughput technologies generate complex biological data that require powerful yet accessible tools for analysis. This beginner-level workshop introduces participants to data integration and multivariate analysis using the R package mixOmics.
Through a series of hands-on sessions, we will explore how multivariate methods can uncover biological patterns, identify key molecular features (or ‘markers’), and integrate multiple omics datasets. The approach is hypothesis-free, flexible, and does not rely on strict statistical assumptions.
By the end of the workshop, participants will be familiar with the core mixOmics workflows for exploratory and supervised analysis. There will also be an opportunity to apply the methods to your own dataset, with expert guidance throughout.
Pre-requisite: Basic proficiency in R is essential (e.g. working with data frames, basic calculations, simple plots). Participants without R experience have reported difficulty keeping up and gaining value from the course.
Instructor: Prof Kim-Anh Lê Cao, the University of Melbourne
WHERE: Mon 22 to Wed 24 Sept 2025: 9am – 5pm, Lund University (Room: Maskrosen (E121), Ekologihuset, Lunds Universitet,Kontaktvägen 10,Lund 22362, Sweden; google map pin)
REGISTER Request an invoice by emailing Maggie at MIG-EA [at] unimelb.edu.au and we will follow up with you with instructions pre-workshop and full schedule.
Fees
Research Higher Degree students enrolled at a University: 350 EUR Staff and members from Universities & Not-for-profit organisations: 575 EUR Other industries:: 1200 EUR
Workshop schedule
Monday 22 Sept and Tuesday 23rd Sept: methods and hands-on.
The following broad topics will be covered.
A. Key methodologies in mixOmics and their variants
Basic processing of count data
Exploration of one data set and how to estimate missing values
Identification of molecular signature to discriminate different treatment groups
Integration of two data sets and identification of biomarkers
Introduction to repeated measurements or longitudinal studies analysis
Integration of more than two data sets to identify multi omics signatures
Integration of independent but related studies (optional)
B. Review on the graphical outputs implemented in mixOmics
Sample plot representation
Variable plot representation for data integration
Other useful graphical outputs
C. Case studies and applications
Several microbiome and omics studies will be analysed using the methods presented above.
Wednesday 24th Sept: bring your own data. Participants will be given the opportunity to analyse their own data under the guidance and the advice of the instructor. Participants can also work in a team. Some data sets will also be provided for those unable to bring their own data.
Statistical concepts
The following statistical concepts will be introduced: covariance and correlation, multiple linear regression, classification and prediction, cross-validation, selection of markers, penalised regressions. Each methodology will be illustrated on a case study (theory and application will alternate).
Target group
The course is intended for computational biologists and biologists with some statistical knowledge and a good working knowledge in R. It will be particularly useful to those interested in:
Understanding and/or applying multivariate projection methodologies to large data sets.
Exploring data sets.
Selecting molecular / microbial features with methods implementing LASSO-based penalisations.
Using graphical techniques to better visualise data.
Anticipated outcomes
After completion of this workshop, participants will be able to
Apply those methods to high throughput microbiome studies, including their own studies.
Understand fundamental principles of multivariate projection-based dimension reduction technique.
Perform statistical integration and feature selection using recently developed multivariate methodologies.
Workshop registration cancellation policy
To confirm your place in this workshop, the registration fee is payable at the time of booking. This commitment helps us plan and deliver the workshop effectively for all participants.
Cancellations and Refunds: Refunds are only available if the workshop is cancelled or postponed by the organiser. In that case, a full refund (including any service fees) will be issued automatically.
No-Show Policy: If you do not attend the workshop, your registration fee will be non-refundable.
Illness or Exceptional Circumstances: We understand that unexpected situations can arise. If you are unable to attend due to illness or other exceptional circumstances, please contact us as soon as possible. While refunds cannot be issued, we will review your situation with care and may consider alternative options at the organiser’s discretion.
This policy is designed to ensure fairness to all participants and to support the smooth delivery of our workshops.
The MixOmics Vignette The R markdown template Kim-Anh’s Teaching Style and approachability
the modules explained in details the main principles of statistical analyses and the webinars were clear and brought additional informations to better understand the online courses.
That we started from basics and went to more advanced analysis
The online content that supported the live webinars
Hands-on experience and being able to use own data. The quality of the material was excellent, and the teacher is very knowledgeable and helpful.
Practical approach, resources to learn more, and the videoconference Q&A.
The course was extremely well structured, the continuous building of knowledge in a layered approach was very effective. I see how the block integration was easy to understand only because of the pre-work in the previous weeks to fully understand PCA, PLS, PLS-DA. Also, the instructor was very flexible in incorporating all our wishes for the little extras we were interested in. The discussions on slack were also very helpful. All in all, the best course I have attended.
I found the assignment really good and challenging.
Assignment on own work, slack
The MixOmics course allows one to learn at their own pace given international schedules. The course material was stimulating and the teacher took time and care to address students’ questions.
The weekly webinar
The assignment and slack and working with own data.
Nice exposure of different methodologies of omics data analysis, including the mathematical reasoning behind them. The assignment was useful to apply and reinforce the knowledge of the theory. The Q&As and the Slack channel were also very good sources for topic discussion.
Very usefull course, many hands on options, it is great that you can work on your own data for the assignment, I liked the live tutorials a lot
The support was very helpful but the data for the assignments could work on my R version for which I cannot changed it because of my platform
Learning how to use all the tools of the mixomics package
The practical application of the different analyses which was taught.
Practical work mixed with theoretical part of the program.
Hands on activities
Additional comments
Genuinely a very well run and well taught course. I will be recommending it to people!
Professor Kim-Anh Lê Cao was amazing and did a very good job, teaching, organizing us and replying very fast to every question
Please make the access to online content available for 6 months after the course. Need to improve the login link for external users. I could only access by going to the link in the email sent by Melbourne uni with was time consuming. The link would not work if copied and pasted to online favourites.
The training proved to be exceptionally valuable. Despite its considerable challenges, especially given my initial lack of knowledge in the field, I acquired a wider knowledge in the area and very valuable new skills. This experience has significantly contributed to my scientific development. Committing to this training was a wise decision, as it provided an excellent introduction to mixomics and multi-omics analysis in general. I look forward to applying these new skills in my future research studies.
Kim-Anh was a great instructor, very smart, and always available to help and answer any questions.
Thank you to the teachers of the course for their availability. It was a much appreciated experience.
The connexion to the website as external was only accessible from the first e-mail sent.
Longer course, also exploring more options of mixOmix – or an additional course.
Prof. Kim-Anh was exceptionally kind and ready to help. Her teaching was highly appreciated.
Very well organized and structured workshop but I missed more theoretical part of the course so I would not be lost when started my hands-on.
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.
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
Q&A live webinars are scheduled on Thursdays 6pm AEST / 8am CET during the first 4 weeks (27th Feb, 6th, 13th and 20th March).
An additional session might be added on Fridays 9am AEST ( = Thursdays 2pm PST / 5pm EST / 9pm CET)
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:
Unfortunately we had to cancel the workshop as we did not receive a sufficient number of participants to justify running the workshop at this time. These workshops involve peer review and a cohort feel to provide the best experience to our learners.
Register your EOI here and we will let you know when the registration page is up. Our next intake is scheduled for February 2025.
Feedback from a previous iteration can be found here.
Key summary
The new course is open and will run for 7 weeks. 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.
There are 4 weeks of asynchronous learning (you work at our own pace to cover the material each week).
There are 4 live webinars organised on the first 4 Thursdays at 5pm AEST (convert your time here) to summarise some key concepts and ask your questions (the webinars will be recorded, as there are daylight savings during this period).
You will have the opportunity to chat on Slack and ask your questions during the whole course.
You can analyse your own data for the assessment (due in week 6) or use the data provided. You will reinforce your learning by marking the assignments of 2-3 other learners.
Teaching Period Dates, asynchronised:
Teaching commences: Monday, 21 Oct 2024, 9:00 am AEST
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).
For those looking for an R refresher well ahead of the course:
We will be running a 2-day workshop at Frazer Institute, University of Queendland. The workshop will cover 1.5 days of lectures and hands-on, and an additional 0.5 day for discussions and opportunities to analyse your own data (assuming the data are already processed and normalised).
Fill the survey so that you can register your interest and needs for this workshop. We can only allow a limited number of participants, so lock in those dates in your calendar before we confirm your participation! Priority will be given to postgraduate students and early career researchers. Results will be announced to the participants with details for registration on 17th February.
Context. Advances in high-throughput technologies have transformed the way we examine molecular information, including microbial communities. However, analytical tool development is critically trailing behind data generation, which hinders the analysis, understanding or integration of omics data. Data integration adopt a holistic, data-driven and hypothesis-free approach. This new approach is necessary to understand the role of biological systems and posit new hypotheses.
The workshop will introduce concepts of multivariate dimension methods developed in mixOmics for statistical analysis. Our methods make no distributional assumptions, are highly flexible for unsupervised (exploratory), supervised (classification) and integration analyses. Various analytical frameworks will be presented ranging from data exploration, selection of markers, integration with other omics datasets and introduction to time-course analysis. There will be an opportunity also to analyse your own data.
Each method will be illustrated on real biological studies. The last afternoon is ‘BYO data’ where you can reinforce your learnings on your own study!
Instructor: A/Prof Kim-Anh Lê Cao;Tutor: Nick Matigian (QCIF)
Organized and hosted by: Frazer institute, University of Queensland
There are no registration fees for this workshop. We do expect your attendance as the number of places is limited. The workshop is fully catered. Slides, R code and data will be provided.
Registration Fill the survey and lock the dates in your calendar! As we have a limited number of participants (30), priority will be given to postgraduate students and early career researchers. Results will be announced to the participants with details for registration after the survey’s deadline. Online attendance is also available for a limited number of participants (but with reduced opportunities for interactions).
Prerequisite and requirements. We require from the trainees a good working knowledge in R programming (e.g. handling data frame, perform simple calculations and display simple graphical outputs) to fully benefit from the workshop. Participants are requested to bring their own laptop, having installed the software RStudio http://www.rstudio.com/and the R package mixOmics (instructions will be provided prior to the training).
Outline
The following broad topics will be covered during these two days:
A. Key methodologies in mixOmics and their variants:
Exploration of one data set with Principal Component Analysis (the basics!)
Identification of a molecular signature to discriminate different treatment groups with PLS-Discriminant Analysis
Integration of two data sets and identification of markers with PLS
Integration of more than two data sets to identify multi omics signatures (if sufficient interest) with PLS-DIABLO
B. Graphical outputs implemented in mixOmics
Sample plot representation
Variable plot representation for data integration
Other useful graphical outputs
C. Case studies and applications
Several omics studies (and microbiome if there is some interest) will be analysed using the methods presented above.
Day 2: bring your own data. Participants will be given the opportunity to analyse their own data under the guidance and the advice of the instructors. Participants can also work in a team. Your data need to be processed and normalised beforehand.
The following statistical concepts will be introduced: covariance and correlation, multiple linear regression, classification and prediction, cross-validation, selection of markers, penalised regressions. Each methodology will be illustrated on a case study (theory and application will alternate).
Target group The course is intended for molecular biologists working in the fields of bioinformatics, computational biology and applied statistics with some statistical knowledge and a good working knowledge in R. It will be particularly useful to those interested in:
Exploring data sets.
Selecting molecular signatures with methods implementing LASSO-based penalisations.
Using graphical techniques to better visualise data.
Understanding and/or applying multivariate projection methodologies to large data sets.
Anticipated learning outcomes After completion of this workshop, participants will be able to
Understand fundamental principles of multivariate projection-based dimension reduction technique.
Perform statistical integration and feature selection using recently developed multivariate methodologies.
Apply those methods to high throughput microbiome studies, including their own studies.
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
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).
For those looking for an R refresher well ahead of the course:
Our participants for our first microbiome-dedicated workshop
Feedback from the workshop: This time we included several new case studies specifically focused on microbiome applications. We presented new material, including the problem of compositional data, how to detect and assess existing methods for batch effects (Ms Yiwen (Eva) Wang, PhD student) and our first timeOmics pipeline (Dr Olivier Chapleur).
‘I was especially pleased with the pace of the workshop. There was time to ask questions during lectures and practice. The pracs were designed to be relevant to our actual research questions.’
The event was sponsored by AFRAN and we obtained 50% bursaries from EMRI UoM for 5 PhD students.
‘Good contextualisation of methods before application of them, lots of depth on the background to methods which was important even when concepts were very complex. ‘I think the case studies were really helpful. The R code is written in such a clear and digestible way that it was easy to apply to my own data’
‘The pace and depth was good. All topics covered were highly relevant, and techniques were directly applicable. The ‘mood’ of the workshop was very friendly.’
Complex microbial networks have a central role in the provision and regulation of ecosystems. Multiple microbial biotechnology applications are contributing to global efforts to achieve sustainability – through purification of wastewater, waste valorisation, bioenergy production, or to understand the role of microbiome in human disease and healthy states.
Statistical analysis of microbiome data is challenging due to the inherent characteristics of the data, such as high sparsity and compositional structure. Our workshop will introduce major concepts including multivariate dimension methods developed in mixOmics. Our methods make no distributional assumptions, are highly flexible for unsupervised (exploratory), supervised (classification) and integration analyses.
This hands-on course will cover basic processing and inherent characteristics of microbiome data (compositionality, batch effects), various analytical frameworks ranging from data exploration, selection of microbial markers, integration with other omics datasets and introduction to time-course analysis. Each methodology introduced in the workshop will be illustrated on real biological studies. The third day is ‘BYO data’ day where you can reinforce your learnings on your own study!
Instructor: Dr Kim-Anh Lê Cao and Dr Olivier Chapleur; Tutor: Ms Laetitia Cardonna. The travels of Olivier and Laetitia is proudly sponsored by AFRAN, the Australian-French Association for Research and Innovation.
Fees for 3 days are AUD500 for RHD students, AUD900 for research non-profit organisations and AUD1500 for industry / government. The Environmental Microbiology Research Initiative EMRI (University of Melbourne) proudly sponsors registration bursaries ($225 to support some of the registration costs) to 5 RHD students enrolled at UoM. Apply at the EOI survey link below.
Registrations fees include coffee breaks, lunch, lecture notes and electronic material (slides, R code, data).
Registration Express your interest at this survey link. As we have a limited number of participants (30), priority will be given to postgraduate students and early career researchers. EOI closes on March 11.
Prerequisite and requirements We require from the trainees a good working knowledge in R programming (e.g. handling data frame, perform simple calculations and display simple graphical outputs) to fully benefit from the workshop. Participants are requested to bring their own laptop, having installed the software RStudio http://www.rstudio.com/and the R package mixOmics (instructions will be provided prior to the training).
Outline
Day 1 & 2: methods and hands-on. The following broad topics will be covered.
A. Key methodologies in mixOmics and their variants:
Basic processing of count data (scaling, how to handle compositional data)
Exploration of one data set and how to estimate missing values
Identification of a microbial signature to discriminate different treatment groups
Integration of two data sets and identification of microbial markers
Introduction to repeated measurements or longitudinal studies analysis
How to deal with batch effects
Integration of more than two data sets to identify multi omics signatures (if applicable)
Integration of independent but related studies (if applicable)
B. Review on the graphical outputs implemented in mixOmics
Sample plot representation
Variable plot representation for data integration
Other useful graphical outputs
C. Case studies and applications
Several microbiome studies will be analysed using the methods presented above.
Day 3: bring your own data. Participants will be given the opportunity to analyse their own data under the guidance and the advice of the three instructors. Participants can also work in a team. Some data sets will also be provided for those unable to bring their own data.
The following statistical concepts will be introduced: covariance and correlation, multiple linear regression, classification and prediction, cross-validation, selection of microbial markers, penalised regressions. Each methodology will be illustrated on a case study (theory and application will alternate).
Target group The course is intended for microbiologists working in the fields of bioinformatics, computational biology and applied statistics with some statistical knowledge and a good working knowledge in R. It will be particularly useful to those interested in:
Exploring microbiome data sets.
Selecting microbial features with methods implementing LASSO-based penalisations.
Using graphical techniques to better visualise data.
Understanding and/or applying multivariate projection methodologies to large data sets.
Anticipated learning outcomes After completion of this workshop, participants will be able to
Understand fundamental principles of multivariate projection-based dimension reduction technique.
Perform statistical integration and feature selection using recently developed multivariate methodologies.
Apply those methods to high throughput microbiome studies, including their own studies.
Registration and registration fees: before 17 may 2019, using this form. Fees are: academic (500€), private (1000€), see more details below in the link provided.
Language: French or English depending on the attendees
Prerequisite and requirements We require from the trainees a good working knowledge in R programming (e.g. handling data frame, perform simple calculations and display simple graphical outputs) to fully benefit from the workshop. Participants are requested to bring their own laptop, having installed the software RStudio http://www.rstudio.com/and the R package mixOmics (instructions will be provided prior to the training).
![[open] Self-paced online course Feb 23 – April 11 2026](https://mixomics.org/wp-content/uploads/2025/08/mixomics-logo-dark-theme-round-v1-400x400-1.png){kind=logo}
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