Featured Publications
Computational and Statistical Methods for Single-Cell RNA Sequencing Data
Wang Z, Yan X. Computational and Statistical Methods for Single-Cell RNA Sequencing Data. Springer Handbooks Of Computational Statistics 2022, 3-35. DOI: 10.1007/978-3-662-65902-1_1.ChaptersSingle-cell RNA sequencing technologySingle-cell RNA sequencing dataRNA sequencing technologyPhenotype of interestRNA sequencing dataDifferential expression analysisScRNA-seq dataStatistical methodsSequencing technologiesExpression analysisDropout imputationSequencing dataSeq dataDroplet-based technologiesDropout eventsDisease pathogenesisPopulation composition changesData normalizationHigh noise levelsPhenotypeNoise levelTherapeuticsComposition changesG2S3: A gene graph-based imputation method for single-cell RNA sequencing data
Wu W, Liu Y, Dai Q, Yan X, Wang Z. G2S3: A gene graph-based imputation method for single-cell RNA sequencing data. PLOS Computational Biology 2021, 17: e1009029. PMID: 34003861, PMCID: PMC8189489, DOI: 10.1371/journal.pcbi.1009029.Peer-Reviewed Original ResearchConceptsSingle-cell transcriptomic datasetsTranscriptomic datasetsGene expressionSingle-cell RNA sequencing technologySingle-cell transcriptomic studiesSingle-cell RNA sequencing dataRNA sequencing technologyRNA sequencing dataSingle-cell resolutionGene expression profilesAdjacent genesTranscriptomic studiesSequencing technologiesSequencing dataExpression profilesGene graphDownstream analysisGenesCell trajectoriesDropout eventsCell subtypesExpressionHigh data sparsityCells
2023
Correction: iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects
Liu Y, Zhao J, Adams T, Wang N, Schupp J, Wu W, McDonough J, Chupp G, Kaminski N, Wang Z, Yan X. Correction: iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects. BMC Bioinformatics 2023, 24: 394. PMID: 37858060, PMCID: PMC10588114, DOI: 10.1186/s12859-023-05523-6.Peer-Reviewed Original Research