SuperSpot: coarse graining spatial transcriptomics data into metaspots

• DOI: 10.1093/bioinformatics/btae734
• Publication Date: 2024-12-11T00:50:56Z
• Authors: Matei Teleman; Aurélie A G Gabriel; Léonard Hérault; David Gfeller
• Keywords: 0301 basic medicine; Applications Note; 03 medical and health sciences

Abstract

Summary Spatial Transcriptomics is revolutionizing our ability to phenotypically characterize complex biological tissues and decipher cellular niches. With current technologies such as VisiumHD, thousands of genes can be detected across millions of spots (also called cells or bins depending on the technologies). Building upon the metacell concept, we present a workflow, called SuperSpot, to combine adjacent and transcriptomically similar spots into “metaspots”. The process involves representing spots as nodes in a graph with edges connecting spots in spatial proximity and edge weights representing transcriptomic similarity. Hierarchical clustering is used to aggregate spots into metaspots at a user-defined resolution. We demonstrate that metaspots reduce the size and sparsity of spatial transcriptomic data and facilitate the analysis of large datasets generated with the most recent technologies.

Availability and implementation SuperSpot is an R package available at https://github.com/GfellerLab/SuperSpot and archived on Zenodo (https://doi.org/10.5281/zenodo.14222088). The code to reproduce the figures is available at https://github.com/GfellerLab/SuperSpot/tree/main/figures (https://doi.org/10.5281/zenodo.14222088).