Yanhong Wu

uniLIVER creates a human liver cell atlas using sc/snRNA-seq data and develops LiverCT to map abnormal datasets to the reference atlas, revealing changes in cell type composition and states in liver cancer.

This paper introduces Descartes, a model for identifying spatially variable chromatin accessibility peaks using inter-cellular correlations, showing improved performance and efficiency over existing methods.

scCancer2 provides a supervised framework for annotating tumor microenvironment cells across datasets and evaluates subtype relationships using scRNA-seq and bulk data.

TIST integrates spatial transcriptomics data with histopathological images to improve spatial cluster identification and gene expression analysis.

STEM maps single-cell and spatial transcriptomics data using transfer learning to create spatially aware embeddings and predict pseudo-spatial adjacencies.