计算生物学
基因表达
基因
图形
基因表达谱
背景(考古学)
转录组
计算机科学
生物
遗传学
理论计算机科学
古生物学
作者
Jian Hu,Xiangjie Li,Kyle Coleman,Amelia Schroeder,Nan Ma,David J. Irwin,Edward B. Lee,Russell T. Shinohara,Mingyao Li
出处
期刊:Nature Methods
[Springer Nature]
日期:2021-10-28
卷期号:18 (11): 1342-1351
被引量:482
标识
DOI:10.1038/s41592-021-01255-8
摘要
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.
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