Weizhou Qian

CausalODE infers gene regulatory networks from single-cell perturbation data by optimizing causal reach, capturing transient dynamics, and modeling latent protein levels, outperforming existing methods on real datasets.

CytoSignal detects and quantifies ligand-receptor signaling interactions at cellular resolution from spatial transcriptomic data, enabling spatial gradient analysis and temporal dynamics prediction.

PerturbNet predicts cell state changes from unseen chemical and genetic perturbations using deep learning and perturbation response data.

This study uses single-cell RNA-seq and mitochondrial variants to trace lineage differences between alpha and beta cells in human pancreatic development, revealing distinct developmental trajectories not seen in mice.