Area of research
Molecular Biology
Research interest
Research interests include Epigenetics and DNA Methylation, RNA modifications and cancer, Single-cell and spatial transcriptomics, and Gene expression and cancer classification.
CEMUSA: a graph-based integrative metric for evaluating clusters in spatial transcriptomics.
Brain 5-hydroxymethylcytosine alterations are associated with Alzheimer’s disease neuropathology
LEGEND: Identifying Co-expressed Genes in Multimodal Transcriptomic Sequencing Data
LEGEND: Identifying Co-expressed Genes in Multimodal Transcriptomic Sequencing Data.
Occurrence of vanA/vanM-positive vancomycin-resistant Enterococcus faecium in hospital wastewater associated with clinical infections in Guangzhou, China: A genomic epidemiological study
scDETECT: a novel statistical model accounting for cell type correlation in single-cell RNA-seq differential expression analysis.
Detecting anomalous anatomic regions in spatial transcriptomics with STANDS.
Subpopulation commensalism promotes Rac1-dependent invasion of single cells via laminin-332.
scCTS: identifying the cell type-specific marker genes from population-level single-cell RNA-seq.
SCIntRuler: guiding the integration of multiple single-cell RNA-seq datasets with a novel statistical metric.
LEGEND: Identifying Co-expressed Genes in Multimodal Transcriptomic Sequencing Data
MIXER: Identifying Co-expressed Genes in Multimodal Transcriptomic Sequencing Data
Spatiotemporal attention boosts calling of complicated variations from long reads' alignment data
Detecting and Subtyping Anomalous Single Cells with M2ASDA
Cellcano: supervised cell type identification for single cell ATAC-seq data
Cellcano: supervised cell type identification for single cell ATAC-seq data.
A cofunctional grouping-based approach for non-redundant feature gene selection in unannotated single-cell RNA-seq analysis
A cofunctional grouping-based approach for non-redundant feature gene selection in unannotated single-cell RNA-seq analysis.
CeDAR: incorporating cell type hierarchy improves cell type-specific differential analyses in bulk omics data.
Molecular landscapes of human hippocampal immature neurons across lifespan
A comprehensive comparison of supervised and unsupervised methods for cell type identification in single-cell RNA-seq.
Differential RNA methylation analysis for MeRIP-seq data under general experimental design
Altered hydroxymethylome in the substantia nigra of Parkinson’s disease
Differential RNA methylation analysis for MeRIP-seq data under general experimental design.
EDClust: an EM-MM hybrid method for cell clustering in multiple-subject single-cell RNA sequencing.
Cellcano: supervised cell type identification for single cell ATAC-seq data
N6-methyladenosine dynamics in neurodevelopment and aging, and its potential role in Alzheimer’s disease
N6-methyladenosine dynamics in neurodevelopment and aging, and its potential role in Alzheimer's disease.
A human forebrain organoid model of fragile X syndrome exhibits altered neurogenesis and highlights new treatment strategies
Accurate feature selection improves single-cell RNA-seq cell clustering