Yuzhou Chang

The paper introduces two methods to profile functional glycan-lectin interactions in tissues using single-cell and spatial multiomic approaches, revealing stimulus-specific glycan remodeling and spatial glycan programs in immune cells and follicular lymphoma.

This paper introduces IN-DEPTH and SGCC for spatial multi-omics analysis, revealing EBV-related immunomodulatory differences in DLBCL.

The paper shows that higher TREM2 expression in gliomas correlates with poor prognosis, but TREM2 deficiency does not improve outcomes in pre-clinical models, as it impairs myeloid cell phagocytosis and MHCII-dependent CD4+ T cell responses.

This paper introduces BSP, a method for identifying spatially variable genes from 2D or 3D spatial transcriptomics data, showing improved accuracy and efficiency in simulations and biological validations.

This study uses spatial transcriptomics to identify gene signatures in the human temporal cortex linked to Alzheimer's pathology.

The paper introduces BANYAN, a Bayesian model for analyzing community connectivity in spatial transcriptomics data, revealing cell interactions in tumor microenvironments and adipose tissue.

RESEPT uses deep learning to visualize and characterize tissue architecture from spatial transcriptomics data, revealing pathological structures in Alzheimer's and glioblastoma samples.

DeepMAPS infers biological networks from single-cell multi-omics data using a heterogeneous graph transformer, outperforming existing tools in cell clustering and pathway prediction.

scGNN introduces a graph neural network framework for scRNA-Seq analyses, improving gene imputation and cell clustering on benchmark datasets and revealing disease-related neural development in Alzheimer's disease.

IRIS-FGM is an R package for analyzing scRNA-Seq data to identify functional gene modules, cluster cells, and perform enrichment analysis, with support for Seurat objects.