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bioRxiv · 10.1101/2025.03.20.644258

SpatialRNA: a Python package for easy application of Graph Neural Network models on single-molecule spatial transcriptomicsdataset

Lyu, R.·Vannan, A.·Kropski, J. A.·Banovich, N. E.·McCarthy, D. J.
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Abstract

Image-based spatial transcriptomics deliver gene expression measurements of RNA transcripts in tissue slices with single-molecule resolution and spatial context preserved. Modern Graph Neural Network (GNN) models are promising methods for capturing the complex molecular and cellular phenotypes in tissues at single-transcript and single-cell levels. A key application of GNNs is the detection of spatial domains or niches, that is, groups of molecules and/or cells that collaboratively work together to produce complex phenotypes. Due to the vast number of detected transcripts in image-based spatial transcriptomics, applying GNNs on RNA molecule graphs is not trivial. We present a Python package, SpatialRNA, for easy (sub)graph generation from tissue samples and provide comprehensive tutorials for convenient and efficient application of Graph Neural Network models under the PyG framework. Availability and implementation: The SpatialRNA package is freely accessible from GitLab https://gitlab.svi.edu.au/biocellgen-public/spatialrna and can be installed via pip. Python notebooks and scripts used in case studies are freely accessible at https://gitlab.svi.edu.au/biocellgen-public/case_study_ipf and https://gitlab.svi.edu.au/biocellgen-public/case_study_xenium_5k_panel.

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Lyu, R., Vannan, A., Kropski, J. A., Banovich, N. E., McCarthy, D. J.. 2025-03-23. SpatialRNA: a Python package for easy application of Graph Neural Network models on single-molecule spatial transcriptomicsdataset. https://doi.org/10.1101/2025.03.20.644258

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