Qunlun Shen

DeSPA uncovers genes with cross-state spatial pattern remodeling and coordinated spatial programs across multi-slice spatial transcriptomics atlases

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

STAVelo: deciphering spatiotemporal cellular dynamics via spatial RNA velocity inference from spatial transcriptomics data

STAVelo infers RNA velocity from spatial transcriptomics data to capture spatiotemporal cellular dynamics across multiple species and tissues.

SpatioCAD: Context-aware graph diffusion model for pinpointing spatially variable genes in heterogeneous tissues

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

High-precision cell-type mapping and annotation of single-cell spatial transcriptomics with STAMapper

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.

Highly accurate reference and method selection for universal cross-dataset cell type annotation with CAMUS

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

Uncovering directionally and temporally variable genes with STAVAG

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.

Spatial Transcriptomics-Aided Localization for Single-Cell Transcriptomics with STALocator

STALocator localizes single cells to spatial transcriptomics data, improving spatial reconstruction and gene expression analysis in multiple datasets.

Cross-species cell-type assignment of single-cell RNA-seq by a heterogeneous graph neural network

This paper introduces CAME, a heterogeneous graph neural network for cross-species cell-type assignment in scRNA-seq data, showing improved performance and revealing evolutionary patterns.