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