Dongshunyi Li

Glimmer improves spatial representation learning by adaptively learning graphs that balance molecular similarity and spatial proximity across scales, outperforming existing methods in clustering and spatial conservation.

SCS improves cell segmentation in sub-cellular spatial transcriptomics by combining imaging and sequencing data, achieving higher accuracy and better cell size estimation.

UNIFAN improves cell type annotation in scRNA-Seq by simultaneously clustering and annotating cells using gene sets, outperforming prior methods.

The paper introduces TraSig, a method that uses pseudotime ordering from scRNA-Seq data to improve the inference of cell-cell interactions by identifying ligand-receptor pairs with similar trajectories.

MESSI identifies active signaling genes in spatial single-cell data by using a mixture of experts approach, improving prediction accuracy and providing biological insights.