Hanyun Zhang

This paper introduces wSIR, a scalable supervised dimensionality reduction method for spatial transcriptomics data, which retains predictive power of spatial coordinates and is demonstrated on breast cancer and embryonic development datasets.

The paper identifies stromal cell subsets in breast cancer that influence T-cell infiltration and patient outcomes through spatial and clinical analysis.

This study uses single-cell RNA-sequencing and spatial transcriptomics to analyze epithelial and fibroblast states in hormone therapy-naive prostate cancer, identifying novel phenotypes and molecular perturbations.

This study uses topological data analysis to quantify ECM architecture and spatial patterns in lung adenocarcinoma, revealing progressive changes linked to immune exclusion and metabolic shifts.