scGPT-spatial incorporates a coordinate-based sampling and training strategy to further facilitate spatially-aware learning. This continual pretraining regimen enables the model to recognize and interpret complex spatial patterns from transcriptomic measurements.

scGPT-spatial highlights two technical enhancements specifically designed to model spatial transcriptomic data. Firstly, scGPT-spatial leverages the Mixture-of-Experts (MoE) architecture in its decoders to capture expression profiles from diverse sequencing protocols.

The continual pretraining of the scGPT-spatial model aims to harmonize various spatial technologies, providing a robust prior for fine-tuning on specific downstream tasks.