Spatio-relational inductive biases in spatial cell-type deconvolution

Vinas, R.; Scherer, P.; Simidjievski, N.; Jamnik, M.; Lio, P.

doi:10.1101/2023.05.19.541474

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bioRxiv · 10.1101/2023.05.19.541474

Spatio-relational inductive biases in spatial cell-type deconvolution

Vinas, R.·Scherer, P.·Simidjievski, N.·Jamnik, M.·Lio, P.

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Abstract

Spatial transcriptomic technologies profile gene expression in-situ, facilitating the spatial characterisation of molecular phenomena within tissues, yet often at multi-cellular resolution. Computational approaches have been developed to infer fine-grained cell-type compositions across locations, but they frequently treat neighbouring spots independently of each other. Here we present GNN-C2L, a flexible deconvolution approach that leverages proximal inductive biases to propagate information along adjacent spots. In performance comparison on simulated and semisimulated datasets, GNN-C2L achieves increased deconvolution performance over spatial-agnostic variants. We believe that accounting for spatial inductive biases can yield improved characterisation of cell-type heterogeneity in tissues.

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Vinas, R., Scherer, P., Simidjievski, N., Jamnik, M., Lio, P.. 2023-05-22. Spatio-relational inductive biases in spatial cell-type deconvolution. https://doi.org/10.1101/2023.05.19.541474

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