Abstract
Here we present stFormer, a foundation model which incorporates ligand genes within spatial niches into Transformer encoders of single-cell transcriptomics. We demonstrate that despite a moderate pretraining data size, the spatially informed gene representations generated by stFormer more consistently cluster the cells, more accurately encode hierarchy and membership within receptor-dependent gene networks, remarkably boost identification of ligand-receptor interaction pairs, and could simulate perturbation effects of ligand-receptor interactions on downstream targets.
Competing Interest Statement
The authors have declared no competing interest.
Copyright
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Posted September 29, 2024.
