A composite scaling network of EfficientNet for improving spatial domain identification performance.

Spatial Transcriptomics leverages gene expression profiling while preserving spatial location and histological images. However, processing the vast and noisy image data in spatial transcriptomics (ST) for precise recognition of spatial domains remains a challenge. In this study, we propose a method of EfNST for recognizing spatial domains, which employs an efficient composite scaling network of EfficientNet to learn multi-scale image features. Compared with other relevant algorithms on six data sets from three sequencing platforms, EfNST exhibits higher accuracy in discerning fine tissue structures, highlighting its strong scalability to data and operational efficiency. Under limited computing resources, the testing results on multiple data sets show that the EfNST algorithm runs faster while maintaining accuracy. The ablation studies of EfNST model demonstrate the significant effectiveness of the EfficientNet. Within the annotated data sets, EfNST showcases the ability to finely identify subregions within tissue structure and discover corresponding marker genes. In the unannotated data sets, EfNST successfully identifies minute regions within complex tissues and elucidated their spatial expression patterns in biological processes. In summary, EfNST presents a novel approach to inferring cellular spatial organization from discrete data spots with significant implications for the exploration of tissue structure and function.