Loïc Chadoutaud

HEDeST improves spatial transcriptomic deconvolution by integrating histology features, achieving single-cell resolution and outperforming existing methods in simulated and real cancer datasets.

sCellST predicts single-cell gene expression from H&E images using spatial transcriptomics data, outperforming traditional methods in pancreatic cancer and detecting morphological differences in ovarian cancer.

transmorph provides a flexible framework for single-cell data integration, enabling complex pipelines and demonstrating its utility in various integration tasks.