Spage2vec: Unsupervised representation of localized spatial gene expression signatures.
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Cited by: 10


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 Source code for reproducing analysis results and figures is available in Zenodo database at


Spatial Transcriptomics
RNA profiling
gene expression
graph representation learning
spatial transcriptomics
tissue analysis

MeSH terms

CA1 Region, Hippocampal
Cell Line
Cluster Analysis
Computational Biology
Gene Expression Profiling
Gene Ontology
Gene Regulatory Networks
Neural Networks, Computer
Somatosensory Cortex


Partel, Gabriele
Wählby, Carolina

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