RT Journal Article
SR Electronic
T1 Spatio-relational inductive biases in spatial cell-type deconvolution
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2023.05.19.541474
DO 10.1101/2023.05.19.541474
A1 Ramon Viñas
A1 Paul Scherer
A1 Nikola Simidjievski
A1 Mateja Jamnik
A1 Pietro Liò
YR 2023
UL http://biorxiv.org/content/early/2023/05/22/2023.05.19.541474.abstract
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.