Featured Publications
scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles
Zhu B, Wang Y, Ku L, van Dijk D, Zhang L, Hafler D, Zhao H. scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles. Genome Biology 2023, 24: 292. PMID: 38111007, PMCID: PMC10726524, DOI: 10.1186/s13059-023-03129-y.Peer-Reviewed Original ResearchSCADIE: simultaneous estimation of cell type proportions and cell type-specific gene expressions using SCAD-based iterative estimating procedure
Tang D, Park S, Zhao H. SCADIE: simultaneous estimation of cell type proportions and cell type-specific gene expressions using SCAD-based iterative estimating procedure. Genome Biology 2022, 23: 129. PMID: 35706040, PMCID: PMC9199219, DOI: 10.1186/s13059-022-02688-w.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsGene ExpressionGene Expression ProfilingSequence Analysis, RNASingle-Cell AnalysisConceptsCell type-specific gene expressionType-specific gene expressionCell type proportionsDifferential expression analysisCell type-specific gene expression profilesExpression analysisGene expressionSingle-cell RNA-seq dataRNA-seq dataGene differential expression analysisGene expression profilesType proportionsExpression profilesExpressionGenesCells
2023
Cell-type-specific co-expression inference from single cell RNA-sequencing data
Su C, Xu Z, Shan X, Cai B, Zhao H, Zhang J. Cell-type-specific co-expression inference from single cell RNA-sequencing data. Nature Communications 2023, 14: 4846. PMID: 37563115, PMCID: PMC10415381, DOI: 10.1038/s41467-023-40503-7.Peer-Reviewed Original ResearchCluster AnalysisCOVID-19Gene Expression ProfilingHumansRNASequence Analysis, RNASingle-Cell AnalysisA novel Bayesian framework for harmonizing information across tissues and studies to increase cell type deconvolution accuracy
Deng W, Li B, Wang J, Jiang W, Yan X, Li N, Vukmirovic M, Kaminski N, Wang J, Zhao H. A novel Bayesian framework for harmonizing information across tissues and studies to increase cell type deconvolution accuracy. Briefings In Bioinformatics 2023, 24: bbac616. PMID: 36631398, PMCID: PMC9851324, DOI: 10.1093/bib/bbac616.Peer-Reviewed Original Research
2019
NITUMID: Nonnegative matrix factorization-based Immune-TUmor MIcroenvironment Deconvolution
Tang D, Park S, Zhao H. NITUMID: Nonnegative matrix factorization-based Immune-TUmor MIcroenvironment Deconvolution. Bioinformatics 2019, 36: 1344-1350. PMID: 31593244, PMCID: PMC8215918, DOI: 10.1093/bioinformatics/btz748.Peer-Reviewed Original Research