Multiomics Data Analysis and Integration

Overview

Researchers often have access or generate multiple omics data (RNAseq, metabolomics, lipidomics, proteomics…) within a single study. Although each omics data has been traditionally analysed in isolation, combining possibly complementary data can yield a better understanding of the mechanisms involved in the biological processes. Several integrative approaches are now available to combine such data, which can be regarded as extensions of the standard Principal Component Analysis (PCA).

In this 2-day course, we will provide an overview of omics data structures, and present different unsupervised and supervised statistical approaches, from simple PCA/PLS to more advanced multiomics dimension reduction methods (Common Component and Specific Weights Analysis, Consensus Orthogonal Partial Least Squares). For each method, we will cover both its principles and practical aspects.

Audience

This course is addressed to life scientists who have worked with at least one type of omics data.

Learning outcomes

At the end of the course, the participants are expected to:

• Perform and interpret a PCA
• Build a PLS model on a single data table
• Compute and interpret an unsupervised R MBAnalysis package (CCSWA) and a supervised integrative model with the ConsensusOPLS R package

Prerequisites

Knowledge / competencies

This course is part of the Omics Data Analysis learning path. To get the most out of this course, you should meet the learning outcomes of the
First Steps with R in Life Sciences and Introduction to Statistics and Data Visualisation with R courses. Evaluate your R skills with the following self-assessment.

You should also have performed analyses with at least one type of omics data (RNAseq, metabolomics…).

Technical

You are required to bring your own laptop and have the following installed:

• R and RStudio
• R packages will be announced to the participants

Schedule

Day 1: Introduction to dimensionality reduction theory and exercises (PCA/PLS)

Day 2: Introduction to multiblock analyses, focusing on an unsupervised model (Common Component and Specific Weights Analysis) and a supervised model (Consensus Orthogonal Partial Least Squares)

Application

The registration fees for academics are 200 CHF and 1000 CHF for for-profit companies.

While participants are registered on a first come, first served basis, exceptions may be made to ensure diversity and equity, which may increase the time before your registration is confirmed.

Applications will close on 22/02/2027 or as soon as the places will be filled up. Deadline for free-of-charge cancellation is set to 22/02/2027. Cancellation after this date will not be reimbursed. Please note that participation in SIB courses is subject to our general conditions.

You will be informed by email of your registration confirmation. Upon reception of the confirmation email, participants will be asked to confirm attendance by paying the fees within 5 working days.

Venue and Time

The course will take place at the University of Geneva, Centre Médical Universitaire (CMU).

The course will start at 9:00 and end around 17:00.

Precise information will be provided to the participants in due time.

Additional information

Coordination: Monique Zahn, SIB Training group.

A Certificate of Attendance will be sent provided you were present at the course, whereas a Certificate of Achievement recommending 0.5 ECTS will be sent provided you passed the exam.

You are welcome to register to the SIB courses mailing list to be informed of all future courses and workshops, as well as all important deadlines using the form here.

Please note that participation in SIB courses is subject to our general conditions.

SIB abides by the ELIXIR Code of Conduct. Participants of SIB courses are also required to abide by the same code.

For more information, please contact training@sib.swiss.