B0430
Title: Sparse Bayesian clustering of gene expression profiles in spatial transcriptomic experiments
Authors: Andrea Sottosanti - University of Padova (Italy) [presenting]
Davide Risso - University of Padua (Italy)
Abstract: Spatial transcriptomics is a groundbreaking technology that allows the measurement of the activity of thousands of genes in a tissue sample and maps where the activity occurs. This technology has enabled the study of the spatial variation of the genes across the tissue. Comprehending gene functions and interactions in different areas of the tissue is of great scientific interest, as it might lead to a deeper understanding of several key biological mechanisms, such as cell-cell communication or tumour-microenvironment interaction. To do so, one can group cells of the same type and genes that exhibit similar expression patterns. A new flexible model is introduced that exploits recent developments in sparse modelling of spatial data to analyse the spatial expression profiles of the genes, estimates their spatial covariance nonparametrically with a novel Bayesian methodology, and groups the genes into clusters. The method is computationally attractive for analyzing the expression patterns of thousands of genes measured across thousands of different spots where the RNA is collected, and its usefulness in responding to specific biological questions is illustrated with a series of simulation experiments and with an application to a tissue sample processed with the 10X-Visium protocol.
