Related Experiment Video
Updated: Jun 28, 2026
Mining Spatial Transcriptomics Datasets using DeepSpaceDB
Published on: September 5, 2025
ResSAT: enhancing spatial transcriptomics prediction from H&E-stained histology images with an interactive spot
Anqi Liu1, Yue Zhao1, Woong-Ki Kim2,3
1Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, USA.
None:
Spatial transcriptomics has revolutionized RNA quantification with spatial resolution. Hematoxylin and eosin (H&E) images, the gold standard in medical diagnosis, offer insights into tissue structure, correlating with gene expression patterns. We introduce ResSAT (Residual networks with Spatial encoding-self-Attention Transformer), a framework for predicting spatially resolved transcriptomic profiles from H&E images by integrating image features, spatial locations, and self-attention transformer-based spot interactions. Benchmarking on 10 × Visium datasets, ResSAT outperforms existing methods and preserved biologically meaningful spatial patterns, promising reduced spatial transcriptomics profiling costs and rapid acquisition of numerous profiles.
