Musu Yuan

UTOPIA quantifies confidence in virtual spatial transcriptomics predictions across scales, improving reliability and preventing false conclusions.

HistoSweep improves tissue quality control in gigapixel images by removing artifacts and preserving biologically relevant structures, enhancing analysis accuracy in digital pathology and spatial omics.

S2Omics automatically selects regions of interest from histology images to maximize molecular information content in spatial omics experiments, improving reproducibility and downstream biological discovery.

This paper introduces scCTClust, a deep learning method for clustering CITE-seq data that integrates RNA and protein information, achieving better clustering results than existing methods.