Day 2: From hypothesis to validation: Systems-based tools for omics

Author

Wen Zhong

Published

October 6, 2025

Learning objectives

Gain an understanding of cutting-edge omics technologies and their potential for exploring biological complexity. By the end of this workshop, participants will be able to

  • Understand the applications of single-cell techniques, spatial omics and multi-omics data.
  • Be able to analyse single-cell data.
  • Learn the main strategies to analyse multi-omics.

Lectures

[9:00-10:30] Single-cell sequencing methodology

Speaker: Antonio Lentini

Single-cell and spatial omics are among the latest advances in omics technologies. Single-cell omics uncover the diversity of cell types and states, while spatial omics add information on their position and interactions. Combined, they provide a powerful framework to map tissues and understand health and disease at unprecedented resolution.

[10:30-10:45] Break

[10:45-12:00] Exploratory analysis of multi-omics data

Speaker: Alberto Zenere

Analyzing paired omics generated from the same biological samples promises to give a more holistic view of the biological processes. However, there remain several practical questions on how to integrate multi-omics effectively. In this module, we will present what strategies are currently used by researchers for this purpose.

Workshops

[13:00-14:00] Single-cell sequencing analysis

Instructor: Antonio Lentini

Standard pipelines for the analysis of single-cell data.

[14:00-15:00] General approaches for multi-omics data

Instructor: Alberto Zenere

Standard techniques to perform exploratory analyses of multi-omics data.

[15:00-16:00] Multi-omics factor analysis

Instructor: Xiangfu Zhong

Multi-omics factor analysis (MOFA) is an unsupervised method to integrate multiple omics datasets. It works by inferring an interpretable low-dimensional representation in terms of a few latent factors.