A certain number of places will be attributed in priority to StarOmics members.

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 statistical approaches unsupervised and supervised, from simple PCA/PLS to more advanced multi-omics dimension reduction methods (Common Component and Specific Weights Analysis, Multiblock Partial Least Squares). For each method, we will cover both its principle and practical aspects.

Audience

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

Learning outcomes

At the end of the course, the participants are expected to:
* run and interpret a Principal Component Analysis or compute a Partial Least Square model on one data table
* compute and interpret an unsupervised or a supervised integrative model with the R MBAnalysis package (CCSWA/MB-PLS)

Prerequisites

Knowledge / competencies

Before the course, you should meet the learning outcomes of, or have taken:
First Steps with R in Life Sciences
and Introduction to Statistics with R

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

Evaluate your R skills with the following self-assesment.

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: PCA/PLS theory and exercise

Day 2: general introduction to multiblock analyses, focus on an unsupervised model (Common Component and Specific Weights Analysis) and a supervised model (Multiblock Partial Least Squares)

Application

A certain number of places will be attributed in priority to StarOmics members. If you are a StarOmics student, please indicate this in your 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 as soon as the places will be filled up. Deadline for free-of-charge cancellation is set to 27/02/2025. 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 days.

Venue and Time

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.

We will recommend 0.5 ECTS credits for this course (given a passed exam at the end of the course).

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.