GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data
Open accessAbstract
Most methods for inferring gene-gene interactions from expression data focus on intracellular interactions. The availability of high-throughput spatial expression data opens the door to methods that can infer such interactions both within and between cells. To achieve this, we developed Graph Convolutional Neural networks for Genes (GCNG). GCNG encodes the spatial information as a graph and combines it with expression data using supervised training. GCNG improves upon prior methods used to analyze spatial transcriptomics data and can propose novel pairs of extracellular interacting genes. The output of GCNG can also be used for downstream analysis including functional gene assignment. Supporting website with software and data: https://github.com/xiaoyeye/GCNG .
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Gene expression data can be turned into a colored graph that captures the shape of the data, for example with TDA, enabling downstream analysis with network science metrics and graph machine learning.
“Gene expression data can be transformed into a colored graph that captures the shape of the data (e.g., using TDA [148]; refer to SI Note 3), which then enables downstream analysis using network science metrics and graph machine learning.”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [148]
Methods that use only gene expression data typically transform the co-expression matrix into a more topologically meaningful form.
“Methods that rely solely on gene expression data typically transform the co-expression matrix into a more topologically meaningful form [146, 147, 148].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [148]
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References 49 · Cited by 194
Research works printed but not linked: 4
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- Authors
- Yuan, Y., & Bar-Joseph, Z.
- Affiliations
- Carnegie Mellon University (US)
- Venue
- Genome Biology
- Published
- 2020-12
- Volume
- 21
- Issue
- 1
- Pages
- 300-300
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- PMC
- PMC7726911
- OpenAlex
- W3113349968
- Cited by
- 194
- References
- 49
- Citations by year
- 2026: 15 · 2025: 48 · 2024: 48 · 2023: 28 · 2022: 33 · 2021: 19 · 2020: 3
- FWCI
- 6.73
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Genome biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute of General Medical Sciences (1R01GM122096)
- NIH Office of the Director (OT2OD026682)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- gene-gene interaction, spatial transcriptomics, graph convolutional networks, spatial gene expression, intercellular communication
- MeSH terms
- Animals, Epistasis, Genetic, Humans, Ligands, Software, Gene Expression, Neural Networks, Computer, Computational Biology, Mice, Transcriptome
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