![[open] 3-day mixOmics workshop in person, 22 – 24 Sept 2025, Lund University](https://mixomics.org/wp-content/uploads/2025/08/mixomics-logo-dark-theme-round-v1-400x400-1.png){kind=logo}
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.