SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network

SpaGCN is a spatially resolved transcriptomics data analysis tool for identifying spatial domains and spatially variable genes using graph convolutional networks. Recent advances in spatially resolved transcriptomics (SRT) technologies have enabled comprehensive characterization of gene expression patterns in the context of tissue microenvironment. To elucidate spatial gene expression variation, we present SpaGCN, a graph convolutional network approach that integrates gene expression, spatial location and histology in SRT data analysis. Through graph convolution, SpaGCN aggregates gene expression of each spot from its neighboring spots, which enables the identification of spatial domains with coherent expression and histology. The subsequent domain guided differential expression (DE) analysis then detects genes with enriched expression patterns in the identified domains. Analyzing seven SRT datasets using SpaGCN, we show it can detect genes with much more enriched spatial expression patterns than competing methods. Furthermore, genes detected by SpaGCN are transferrable and can be utilized to study spatial variation of gene expression in other datasets. SpaGCN is computationally fast, platform independent, making it a desirable tool for diverse SRT studies.

Automated summary

SpaGCN is a spatially resolved transcriptomics data analysis tool that uses graph convolutional networks to identify spatial domains and spatially variable genes. By integrating gene expression, spatial location, and histology, SpaGCN can detect genes with enriched spatial expression patterns and transferable to other datasets for studying spatial gene expression variation. SpaGCN is computationally fast, platform independent, and ideal for diverse SRT studies.