2023
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 Research
2022
A Markov random field model-based approach for differentially expressed gene detection from single-cell RNA-seq data
Zhu B, Li H, Zhang L, Chandra SS, Zhao H. A Markov random field model-based approach for differentially expressed gene detection from single-cell RNA-seq data. Briefings In Bioinformatics 2022, 23: bbac166. PMID: 35514182, PMCID: PMC9487630, DOI: 10.1093/bib/bbac166.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsGene Expression ProfilingHumansLipopolysaccharidesMiceRNARNA-SeqSequence Analysis, RNASingle-Cell AnalysisConceptsDE genesSeq dataSingle-cell RNA sequencing technologyDifferential expressionSingle-cell RNA-seq dataIdentification of genesRNA sequencing technologySpecific differential expressionSingle-cell resolutionRNA-seq dataMarkov random field modelMarkov random field model-based approachSimilar cell typesNovel statistical modelRandom field modelComplex biological systemsBiological pathwaysGene detectionGenesCell typesStatistical modelMouse datasetsField modelBiological systemsReal data