Chengxin Yu

This study integrates spatial proteomics and genomics in PDAC to identify tumour-immune-stroma interactions and their genomic associations, showing multi-omics models improve patient survival prediction.

The paper introduces ISOMERIC, a deep learning framework to separate copy number-dependent and independent variation in single-cell transcriptomes, showing distinct associations with clinical subtypes and tumour microenvironment phenotypes.

The paper infers multicellular ecosystems from nonspatial scRNA-seq data and shows they predict therapy response and are linked to somatic mutations.

This study evaluates how dataset imbalance affects single-cell RNA sequencing integration and introduces metrics to assess integration stability in imbalanced settings.