EMBL Courses and Conferences during the Coronavirus pandemic
With the onsite programme paused, many of our events are now being offered in virtual formats.
Registration is open as usual for many events, with back-up plans in place to move further courses and conferences online as necessary. Registration fees for any events affected by the COVID-19 disruption are fully refundable.
More information for participants of events at EMBL Heidelberg can be found here.
Registration is not yet open for this event. If you are interested in receiving more information please register your interest.
Experimental designs in basic biology research and biomedicine increasingly involve the use of multiple omics technologies to characterise a set of samples e.g. genomic variants, epigenetic marks, transcriptome, proteome, metabolome, functional assays, imaging. This is true for both bulk and single cell analyses. Therefore, data analysis techniques are required that are able to cope with such data in an efficient and generalisable manner. Thus, the proposed advanced course addresses one of the major data challenges scientists face today. Its core objective is to convey the conceptual and mathematical foundations that underpin integrative multimodal data analysis strategies and the practical trade-offs of different methods.
Course is aimed at PhD students and post-docs working in computational biology aiming to improve their skills in multi-omics data integration methodologies. Solid experience with R and/or Python is a prerequisite for this course.
- Probabilitic factor models for multi-omics integration (MOFA)
- Multi-table methods for multi-omics integration
- Data visualization and interactive exploration
- Pre-processing and data management
- Detection of hidden confounders and technical effects
- Hands-on exercises with bulk and single cell multi-omics data sets
The course will give students and postdocs and overview of the state-of-the-art methods, with a focus on omics data. In practical sessions using relevant real-world examples from single-cell and bulk multi-omics studies, participants will gain familiarity with software implementations, practical aspects and quality assessment of inputs and outputs and pitfalls, method performance assessment and tuning, and model fit interpretation.