Co-clustering of Spatially Resolved Transcriptomic Data

TL;DR

This paper introduces SpaRTaCo, a co-clustering statistical model for spatial transcriptomic data that simultaneously clusters genes and tissue regions, enhancing understanding of spatial gene expression patterns.

Contribution

The paper presents a novel co-clustering method specifically designed for spatial transcriptomics, addressing the lack of statistical tools that leverage spatial information.

Findings

Validated with simulations demonstrating effectiveness.

Applied to human brain tissue, revealing meaningful spatial gene clusters.

Shows potential for advancing biological insights from spatial data.

Abstract

Spatial transcriptomics is a modern sequencing technology that allows the measurement of the activity of thousands of genes in a tissue sample and map where the activity is occurring. This technology has enabled the study of the so-called spatially expressed genes, i.e., genes which exhibit spatial variation across the tissue. Comprehending their functions and their 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. However, adequate statistical tools that exploit the newly spatial mapping information to reach more specific conclusions are still lacking. In this work, we introduce SpaRTaCo, a new statistical model that clusters the spatial expression profiles of the genes according to the areas of the tissue. This is accomplished by performing a co-clustering, i.e., inferring the latent block structure of the data and inducing two types of clustering: of the genes, using their expression across the tissue, and of the image areas, using the gene expression in the spots where the RNA is collected. Our proposed methodology is validated with a series of simulation experiments and its usefulness in responding to specific biological questions is illustrated with an application to a human brain tissue sample processed with the 10X-Visium protocol.