Spage2vec: Unsupervised representation of localized spatial gene expression signatures
Open accessAbstract
Investigations of spatial cellular composition of tissue architectures revealed by multiplexed in situ RNA detection often rely on inaccurate cell segmentation or prior biological knowledge from complementary single‐cell sequencing experiments. Here, we present spage2vec, an unsupervised segmentation‐free approach for decrypting the spatial transcriptomic heterogeneity of complex tissues at subcellular resolution. Spage2vec represents the spatial transcriptomic landscape of tissue samples as a graph and leverages a powerful machine learning graph representation technique to create a lower dimensional representation of local spatial gene expression. We apply spage2vec to mouse brain data from three different in situ transcriptomic assays and to a spatial gene expression dataset consisting of hundreds of individual cells. We show that learned representations encode meaningful biological spatial information of re‐occurring localized gene expression signatures involved in cellular and subcellular processes. Database: Spatial gene expression data are available in Zenodo database at https://doi.org/10.5281/zenodo.3897401. Source code for reproducing analysis results and figures is available in Zenodo database at http://www.doi.org/10.5281/zenodo.4030404.
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Applying a joint GCN, VAE, and GAN framework to gene correlation networks initialized with a subset of gene expression matrices can generate disease networks with desired properties.
“Further, applying a joint GCN, VAE, and GAN framework (Section 2.3) to gene correlation networks—initialized with a subset of gene expression matrices—can generate disease networks with the desired properties [149].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [149]
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- Authors
- Partel, G., & Wählby, C.
- Affiliations
- Uppsala University (SE)
- Corresponding author
- Partel, Gabriele
- Wählby, Carolina
- Venue
- The FEBS Journal
- Publisher
- Wiley
- Published
- 2020-10-11
- Volume
- 288
- Issue
- 6
- Pages
- 1859-1870
- ISSN
- 1742-464X (ISSN-L)
- 1742-4658
- PMC
- PMC7983892
- OpenAlex
- W3087998689
- Cited by
- 50
- References
- 39
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 7 · 2023: 11 · 2022: 11 · 2021: 9 · 2020: 1
- FWCI
- 2.59
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- FEBS Journal (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- KTH Publication Database DiVA (KTH Royal Institute of Technology) (submittedVersion, other-oa)
- Funders
- H2020 European Research Council (682810)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- spatial transcriptomics, graph representation learning, unsupervised feature learning, graph neural networks, mouse brain, subcellular resolution
- MeSH terms
- Animals, Cell Line, Fibroblasts, Humans, Somatosensory Cortex, Cluster Analysis, Neural Networks, Computer, Computational Biology, Internet, Gene Expression Profiling, Mice, Gene Regulatory Networks, CA1 Region, Hippocampal, Transcriptome, Gene Ontology
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