Kangning Dong

scMagnifier improves fine-grained cell subtype resolution in scRNA-seq by using GRN-informed perturbations and consensus clustering, enhancing accuracy in both single and multi-batch data.

This paper introduces STAMO, a spatially aware integration method for unpaired spatial multi-omics data, showing improved alignment and domain detection compared to existing methods.

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.

TADGATE identifies TADs from sparse Hi-C maps, enhances their structure, and reveals functional elements and cell-type-specific regulatory mechanisms.

This paper develops STAligner, a graph attention neural network for integrating spatial transcriptomics data across different conditions, technologies, and developmental stages, identifying shared and specific spatial domains.

STAMarker identifies spatial domain-specific variable genes using saliency maps in deep learning, outperforming existing methods on sparse spatial transcriptomic data.

SmartGate improves spatial segmentation and biomarker identification in IMS data through iterative peak picking and graph attention auto-encoder modeling.

This paper introduces STAGATE, a graph attention auto-encoder for spatial domain detection in spatial transcriptomics, improving accuracy and reducing batch effects across sections.

The paper introduces scAND, a network diffusion method for embedding single-cell ATAC-seq data, improving clustering accuracy and handling sparsity and missingness.

This paper jointly reconstructs cis-regulatory interaction networks across multiple tissues using single-cell ATAC-seq data, revealing common and tissue-specific regulatory mechanisms.