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 tumor-microenvironment interaction. To do so, one can group cells of the same type and genes that exhibit similar expression patterns.
This talk presents some recently developed statistical methods for the analysis of spatial transcriptomic data, designed to address three main goals: (i) identifying genes that are co-regulated in their expression levels across the tissue in a spatially structured manner; (ii) learning the spatial organisation of the tissue by partitioning cells into regions based on the gene expressions; and (iii) identifying genes that are specifically active in particular spatial regions. All these objectives reflect a common underlying theme, namely the idea of gathering observations into groups. For this reason, the methods presented in this talk are primarily designed to perform clustering, or co-clustering when the analysis is applied to genes and cells simultaneously.
The talk will discuss both the advantages and limitations of these approaches, with examples from multiple spatial transcriptomic datasets.