RT Journal Article
SR Electronic
T1 Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.11.30.405118
DO 10.1101/2020.11.30.405118
A1 Jian Hu
A1 Xiangjie Li
A1 Kyle Coleman
A1 Amelia Schroeder
A1 David J. Irwin
A1 Edward B. Lee
A1 Russell T. Shinohara
A1 Mingyao Li
YR 2020
UL http://biorxiv.org/content/early/2020/12/02/2020.11.30.405118.abstract
AB Recent advances in spatial transcriptomics 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 spatial transcriptomics 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 analysis then detects genes with enriched expression patterns in the identified domains. Analyzing five spatially resolved transcriptomics datasets using SpaGCN, we show it can detect genes with much more enriched spatial expression patterns than existing 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, making it a desirable tool for spatial transcriptomics studies.Competing Interest StatementThe authors have declared no competing interest.