Multiomics Data Analysis and Integration

This course is now full with a long waiting list. If you do not want to miss your chance to be part of the next session and remain informed about all training activities at SIB, we highly recommend you to keep an eye on our list of upcoming events and subscribe to our courses mailing list here (if you haven't done so already).

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 days workshop, 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:

• be able to run and interpret a Principal Component Analysis or compute a Partial Least Square model on one data table.
• be able to compute and interpret an unsupervised or a supervised integrative model with the R MBAnalysis package (CCSWA/MB-PLS).

Prerequisites

Knowledge / competencies

This course is designed for beginner users with the following pre-requisites:

• having performed analyses with at least one type of data (RNAseq, metabolomics…).
• basic R
• basic statistics

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

The course is now full with a long waiting list. We therefore can't take any other application. There will be other sessions in the future. Thank you for your understanding.

The registration fees for academics are 120 CHF and 600 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. Applications close on 04/03/2024. Deadline for free-of-charge cancellation is set to 04/03/2024. 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.