Yinuo Jin

LYNX is a deep generative model that integrates multimodal spatial data to model spatial dynamics and cell-cell interactions across tissues.

This study uses spatiotemporal single-cell analysis to track T cell clonal dynamics and phenotypic plasticity in GVHD, revealing links between TCR features, clonal expansion, and disease severity.

The paper analyzes single-cell data from bone marrow samples to identify immune cell interactions that predict response to adoptive cellular therapy in myeloid leukemia.

CRISPRmap enables multi-omic optical pooled screens to map gene function in cells and tissue without sequencing, identifying pathogenic mutations and analyzing spatial phenotypes.

CellStitch improves 3D cell segmentation in anisotropic images using optimal transport, outperforming existing methods on anisotropic datasets.

Starfysh integrates spatial transcriptomics and histology to analyze tumor microenvironments, identifying disease-specific spatial hubs and immune interactions in breast cancers.

The paper introduces a transfer learning framework for cell segmentation that incorporates geometric features to improve performance on in situ tissue images using in vitro data.