Kong Chen

This study identifies FSTL-1 as a regulator of HSC homeostasis and recovery from bone marrow injury, showing its role in metabolic pathways and cell proliferation.

This study creates a spatiotemporal transcriptomic atlas of the ischemic penumbra in young and aged mice, revealing age-dependent molecular changes and potential therapeutic targets.

This study identifies broad immune remodeling and neutrophil subset dysfunction in IAPA using single-cell transcriptomics and functional assays.

This study shows IL-27 signaling protects against influenza-associated pulmonary aspergillosis by reducing type 2 immunity and enhancing antifungal responses in mice.

The paper identifies TWIST1 as a critical regulator of myofibroblast activity in IPF using single-nucleus ATAC-seq and scRNA-seq integration.

SECANT improves clustering and annotation of single-cell multi-omics data by integrating surface protein and RNA information with biology-guided semi-supervised learning.

This paper introduces TransformerST, a Transformer-based model for spatial transcriptomics analysis without single-cell references, achieving high-resolution tissue clustering and gene expression prediction.

This study integrates spatial transcriptome, single-cell RNA-seq, and single-cell ATAC-seq data from mouse lungs after immunization to identify cell types and their spatial dynamics.

The paper identifies a subset of fetal enteroendocrine cells expressing insulin and beta cell genes, suggesting an extra-pancreatic source of beta cells.

This paper introduces GMM-Demux, a method to identify and remove multiplets in scRNA-seq data, showing improved accuracy and error reduction.

CITE-sort improves cell type classification in CITE-seq by addressing artificial cell types caused by multiplets, achieving better clustering performance than existing methods.

BREM-SC is a Bayesian model that jointly clusters single-cell transcriptomic and proteomic data, improving accuracy and quantifying clustering uncertainty.

BAMM-SC clusters droplet-based scRNA-seq data from multiple individuals by accounting for batch effects and heterogeneity, improving accuracy over existing methods.