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bioRxiv · 10.64898/2026.01.06.697922

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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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.

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Gortana, L., Chadoutaud, L., Bourgade, R., Barillot, E., Walter, T.. 2026-01-07. HEDeST: An Integrative Approach to Enhance Spatial Transcriptomic Deconvolution with Histology. https://doi.org/10.64898/2026.01.06.697922

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