Taught by Paul Villoutreix

Spatial technologies such as advanced microscopy imaging techniques and spatial multi-omics (genomic, transcriptomic, proteomic, …) have revolutionized the resolution and richness of measurements of biological tissues. These technological developments lead to spatial multivariate data, for example individual cell features and their spatial relationships. In this class, after reviewing the main technologies and the type of spatial data they generate, we will address the concepts and methods underlying their analysis. In particular, we will focus on unsupervised data exploration strategies that exploit the spatial component, such as spatial statistics, network-based approaches or spatially-aware clustering methods. We will finally show how these methods can be relevant to extract spatial patterns of cell morphology or gene expression, and compare them across multiple conditions.

The hands-on session in the afternoon will provide an opportunity to work on spatial morphometrics and spatial transcriptomics data.

Prerequisites: some knowledge of machine learning, statistics, spatial biology, python (for the hands-on session)