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.
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.
Workshop details
- Dates: 2 – 27 March 2026 (4 weeks)
- Format: Weekly focus topic with online resources, live Q&A sessions, and dedicated Slack support. Topics will include batch-effect management, multi-study integration, and longitudinal/time-course designs.
- Capacity: Limited places to ensure personalised support from our team
- Fees:
• AUD $795 (~ €438, US$515) – Students
• AUD $1,350 (~ €755, US$875) – Academic / Non-profit staff
• AUD $2,250 (~ €1,260, US$1458) – Industry participants
Payment available via invoice or credit card - Certificate: All participants will receive a certificate of attendance
- Platform: You will be among the first to use our new mixOmics PRO platform !
We aim to open the registrations at the start of December, and can notify you if you register to our EOI.
Learning outcomes
By the end of the course, learners will be able to:
- Frame study design and data preparation (Module 1).
Define experimental design, anticipate batch risks, and prepare datasets for analysis. - Model and cluster time trajectories to extract structure (Module 2).
Apply modelling to capture temporal patterns and dimension reduction approaches to summarise time-varying behaviour across features and omics datasets. - Detect and mitigate batch effects (Module 3).
Diagnose unwanted variation and implement appropriate corrections, showing improvement in core diagnostics. - Extend to advanced longitudinal methods when warranted (Module 4).
Trial network or tensor approaches and judge when they add value beyond baseline clustering. - Report robust, reproducible, and interpretable findings (all modules).
Validate results, communicate limitations, and deliver a clean, reproducible analysis with a concise biological narrative.