Graph representation learning for single-cell biology
Open accessAbstract
Single-cell RNA sequencing measures gene expression at an unprecedented resolution and scale and allows the analysis of cellular phenotypes which was not possible before. In this context, graphs occur as a natural representation of the system —both as gene-centric and cell-centric. However, many advances in machine learning on graphs are not yet harnessed in models on single-cell data. Taking the inference of cell types or gene interactions as examples, graph representation learning has a wide applicability to both cell and gene graphs. Recent advances in spatial molecular profiling additionally put graph learning in the focus of attention because of the innate resemblance of spatial information to spatial graphs. We argue that graph embedding techniques have great potential for various applications across single-cell biology. Here, we discuss how graph representation learning maps to current models and concepts used in single-cell biology and formalise overlaps to developments in graph-based deep learning.
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Spatial transcriptomics data can be used to construct cell graphs, spatial gene expression graphs, gene co-expression networks, or molecular similarity graphs.
“As a result, spatial transcriptomics data can be used to construct cell graphs [161], spatial gene expression graphs [162], gene co-expression networks, or molecular similarity graphs [35].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [35]
Gene co-expression and molecular similarity graphs require a threshold applied to gene expression data.
“Creating cell neighborhood and spatial gene expression graphs require a distance metric, as edges are determined based on spatial proximity, while gene co-expression and molecular similarity graphs need a threshold applied on the gene expression data [35].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [35]
Cell neighborhood and spatial gene expression graphs require a distance metric because edges are determined by spatial proximity.
“Creating cell neighborhood and spatial gene expression graphs require a distance metric, as edges are determined based on spatial proximity, while gene co-expression and molecular similarity graphs need a threshold applied on the gene expression data [35].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [35]
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- Authors
- Hetzel, L., Fischer, D. S., Günnemann, S., & Theis, F. J.
- Affiliations
- Helmholtz Munich (DE)
- Technical University of Munich (DE)
- Corresponding author
- Theis, Fabian J.
- Venue
- Current Opinion in Systems Biology
- Publisher
- Elsevier BV
- Published
- 2021-12
- Volume
- 28
- Pages
- 100347
- ISSN
- 2452-3100 (ISSN-L)
- OpenAlex
- W3164766680
- Cited by
- 55
- References
- 66
- Citations by year
- 2026: 7 · 2025: 14 · 2024: 14 · 2023: 10 · 2022: 8 · 2021: 2
- FWCI
- 0.89
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Current Opinion in Systems Biology (publishedVersion, cc-by)
- Site cant be reached (submittedVersion, cc-by)
- Funders
- Bundesministerium für Bildung und Forschung (01IS18036B, 01IS18053A)
- BMBF Berlin
- Helmholtz Association of German Research Centres (ZT-I-007, ZT-I-PF-5-01)
- Helmholtz Artificial Intelligence Cooperation Unit
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- graph representation learning, single-cell RNA sequencing, graph embedding, spatial graphs, graph-based deep learning
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