SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network
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 i...
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| Published in | Nature methods Vol. 18; no. 11; pp. 1342 - 1351 |
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| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Nature Publishing Group US
01.11.2021
Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1548-7091 1548-7105 1548-7105 |
| DOI | 10.1038/s41592-021-01255-8 |
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| Abstract | 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.
SpaGCN is a spatially resolved transcriptomics data analysis tool for identifying spatial domains and spatially variable genes using graph convolutional networks. |
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| AbstractList | 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.
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.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. 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. 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. |
| Audience | Academic |
| Author | Ma, Nan Shinohara, Russell T. Hu, Jian Coleman, Kyle Lee, Edward B. Schroeder, Amelia Li, Mingyao Li, Xiangjie Irwin, David J. |
| Author_xml | – sequence: 1 givenname: Jian orcidid: 0000-0003-2852-7675 surname: Hu fullname: Hu, Jian email: jianhu@pennmedicine.upenn.edu organization: Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania – sequence: 2 givenname: Xiangjie surname: Li fullname: Li, Xiangjie organization: School of Statistics and Data Science, Nankai University – sequence: 3 givenname: Kyle orcidid: 0000-0002-9726-2335 surname: Coleman fullname: Coleman, Kyle organization: Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania – sequence: 4 givenname: Amelia surname: Schroeder fullname: Schroeder, Amelia organization: Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania – sequence: 5 givenname: Nan orcidid: 0000-0001-8553-7808 surname: Ma fullname: Ma, Nan organization: Weitzman School of Design, University of Pennsylvania – sequence: 6 givenname: David J. orcidid: 0000-0002-5599-5098 surname: Irwin fullname: Irwin, David J. organization: Department of Neurology, Perelman School of Medicine, University of Pennsylvania – sequence: 7 givenname: Edward B. orcidid: 0000-0002-4589-1180 surname: Lee fullname: Lee, Edward B. organization: Translational Neuropathology Research Laboratory, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania – sequence: 8 givenname: Russell T. surname: Shinohara fullname: Shinohara, Russell T. organization: Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania – sequence: 9 givenname: Mingyao orcidid: 0000-0003-2422-9494 surname: Li fullname: Li, Mingyao email: mingyao@pennmedicine.upenn.edu organization: Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34711970$$D View this record in MEDLINE/PubMed |
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| Title | SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network |
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