James J. Zhu

This study introduces a deep learning model called spacer to analyze spatially resolved transcriptomics data, revealing novel insights into cellular recruitment mechanisms in tumors and heart tissue during myocarditis.

The paper introduces a Bayesian framework to map cell-cell interactions from spatial transcriptomics data, revealing new biological insights in cancer and immune responses.

This paper develops SClineger, a Bayesian model to correct genetic drop-outs in scRNA-Seq data for accurate lineage tracing across tissues and time scales.