PT  - JOURNAL ARTICLE
AU  - Ramon Viñas
AU  - Paul Scherer
AU  - Nikola Simidjievski
AU  - Mateja Jamnik
AU  - Pietro Liò
TI  - Spatio-relational inductive biases in spatial cell-type deconvolution
AID  - 10.1101/2023.05.19.541474
DP  - 2023 Jan 01
TA  - bioRxiv
PG  - 2023.05.19.541474
4099  - http://biorxiv.org/content/early/2023/05/22/2023.05.19.541474.short
4100  - http://biorxiv.org/content/early/2023/05/22/2023.05.19.541474.full
AB  - 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.Competing Interest StatementThe authors have declared no competing interest.