HEDeST: An Integrative Approach to Enhance Spatial Transcriptomic Deconvolution with Histology
Gortana, L.; Chadoutaud, L.; Bourgade, R.; Barillot, E.; Walter, T.
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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.
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