Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.04.17.537189v1?rss=1
Authors: Cai, P., Robinson, M. D., Tiberi, S.
Abstract:
Spatially resolved transcriptomics (SRT) enables scientists to investigate spatial context of mRNA abundance. Here, we introduce DESpace, a novel approach to discover spatially variable genes (SVGs), i.e., genes whose expression varies across the tissue. Our framework inputs all types of SRT data, summarizes spatial information via spatial clusters, and identifies spatially variable genes by performing differential gene expression testing between clusters. Although several methods have been proposed to identify SVGs, our approach adds some unique features; in particular: it allows identifying (and testing) the specific areas of the tissue affected by spatial variability, and it enables joint modelling of multiple samples (i.e., biological replicates). Furthermore, in our benchmarks, DESpace displays a higher true positive rate than competitors, controls for false positive and false discovery rates, and is among the most computationally efficient SVG tools. DESpace is distributed as a Bioconductor R package.
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