HEDeST: An Integrative Approach to Enhance Spatial Transcriptomic Deconvolution with Histology

Abstract Spatial organization of cells is essential for tissue function, yet sequencing-based spatial transcriptomics often lacks single-cell resolution. We present HEDeST, a weakly supervised framework that integrates histology-derived morphological features with deconvolution-derived spot-level proportions to assign cell types at single-cell resolution. HEDeST is robust to technical variability, adaptable to user-defined cell types, and compatible with any deconvolution method. Across simulated and semi-simulated datasets, HEDeST outperforms existing morphology-based approaches and reveals biologically meaningful microenvironments when applied to real cancer datasets, providing a scalable tool for high-resolution spatial tissue analysis. Competing Interest Statement The authors have declared no competing interest. Footnotes We repeated the experiments by setting the biological size of the images to 20 μm, thereby updating the Results and Methods sections. We also conducted new experiments with our fully simulated data, including the introduction of perturbation experiments.