Shuqin Zhang

SORT identifies multilevel gene programs and their spatial activities across diverse spatial transcriptomic datasets, revealing both shared and sample-specific patterns.

DeSPA identifies genes with spatial pattern remodeling across biological states and reveals coordinated spatial programs in multi-slice spatial transcriptomics data.

This paper introduces st-Xprop, a graph learning method for spatial domain detection in multi-modal spatial transcriptomics, improving clustering accuracy and biological relevance.

This paper introduces STACAME, a framework for analyzing cross-species spatial transcriptomics data to identify shared and divergent tissue architectures across species.

This study shows that chronic unpredictable mild stress induces age-dependent ovarian aging and POI in young and middle-aged female mice, with distinct endocrine and transcriptomic responses.

SpatioCAD detects spatially variable genes by accounting for cell density variations, improving accuracy in heterogeneous tissues like tumors.

STAMapper maps and annotates cell types in single-cell spatial transcriptomics data using a graph neural network, achieving high accuracy and enabling unknown cell-type detection.

CAMUS improves cross-dataset cell type annotation by selecting optimal references and methods, achieving accuracy gains across multiple single-cell datasets.

This paper introduces STAVAG, a framework to identify directionally and temporally variable genes in spatial and temporal data, revealing biologically relevant gene modules and diagnostic biomarkers.

SEPAR identifies spatial metagenes and their patterns in spatial transcriptomics and multi-omics data, improving gene expression analysis and spatial domain detection.

CoupleVAE predicts post-perturbation single-cell RNA-seq data by using coupled variational autoencoders, achieving better accuracy than existing models in three real datasets.

This paper introduces XVGAE, a cross-view graph neural network that integrates gene expression, spatial coordinates, and histological images to identify spatial domains, achieving better clustering accuracy and biologically meaningful results.