2023
Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2
Lin Y, Cao Y, Willie E, Patrick E, Yang J. Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2. Nature Communications 2023, 14: 4272. PMID: 37460600, PMCID: PMC10352351, DOI: 10.1038/s41467-023-39923-2.Peer-Reviewed Original Research
2022
scJoint integrates atlas-scale single-cell RNA-seq and ATAC-seq data with transfer learning
Lin Y, Wu T, Wan S, Yang J, Wong W, Wang Y. scJoint integrates atlas-scale single-cell RNA-seq and ATAC-seq data with transfer learning. Nature Biotechnology 2022, 40: 703-710. PMID: 35058621, PMCID: PMC9186323, DOI: 10.1038/s41587-021-01161-6.Peer-Reviewed Original ResearchMeSH KeywordsChromatin Immunoprecipitation SequencingExome SequencingMachine LearningRNA-SeqSequence Analysis, RNASingle-Cell AnalysisConceptsData modalitiesTransfer learning methodDifferent data modalitiesSingle-cell multiomics dataTransfer learningUnlabeled dataMultimodal datasetLeverage informationNeural networkLearning methodsData compositionLabel transferLabel accuracyJoint visualizationHeterogeneous collectionPromising resultsUnprecedented paceVisualizationFrameworkMultiomics dataScRNA-seq dataDatasetNetworkScATAC-seq dataCell atlases
2020
Investigating higher-order interactions in single-cell data with scHOT
Ghazanfar S, Lin Y, Su X, Lin D, Patrick E, Han Z, Marioni J, Yang J. Investigating higher-order interactions in single-cell data with scHOT. Nature Methods 2020, 17: 799-806. PMID: 32661426, PMCID: PMC7610653, DOI: 10.1038/s41592-020-0885-x.Peer-Reviewed Original ResearchConceptsSingle-cell dataCell fate choiceSingle-cell genomicsDifferential expression testingGene-gene correlationsFate choiceHigher-order interactionsKey genesTranscriptomic dataEmbryonic developmentCoordinated changesExpression testingGenesSubtle changesMouse liverMouse olfactory bulbCellsGenomicsSchotPseudotimeInteractionOlfactory bulbHigher-order measurementsCovariationVariabilityCiteFuse enables multi-modal analysis of CITE-seq data
Kim H, Lin Y, Geddes T, Yang J, Yang P. CiteFuse enables multi-modal analysis of CITE-seq data. Bioinformatics 2020, 36: 4137-4143. PMID: 32353146, DOI: 10.1093/bioinformatics/btaa282.Peer-Reviewed Original ResearchMeSH KeywordsEpitopesGene Expression ProfilingRNASequence Analysis, RNASingle-Cell AnalysisSoftwareTranscriptomeConceptsCITE-seq dataLigand-receptor interaction analysisCell surface proteinsMulti-modal profilingProtein expression analysisLigand-receptor interactionsCell hashingDifferential RNATranscriptome dataDistinct speciesCellular indexingExpression analysisDoublet detectionIntegrative analysisMolecular biologyStreamlined packageTranscriptomeSingle cellsInteractive web-based visualizationSupplementary dataRNAR packageProfilingEpitope profilesSuite of tools
2019
Evaluating stably expressed genes in single cells
Lin Y, Ghazanfar S, Strbenac D, Wang A, Patrick E, Lin D, Speed T, Yang J, Yang P. Evaluating stably expressed genes in single cells. GigaScience 2019, 8: giz106. PMID: 31531674, PMCID: PMC6748759, DOI: 10.1093/gigascience/giz106.Peer-Reviewed Original ResearchConceptsSingle-cell levelScRNA-seq datasetsHousekeeping genesExpression stabilitySingle-cell RNA-seq profilingSingle cellsSingle-cell transcriptomesRNA-seq profilingSubset of genesDiverse biological systemsBioconductor R packageCell population levelEssential functionsStable expressionGenesIndividual cellsData normalizationTissue typesCell populationsDifferent cellsPopulation levelR packageBiological systemsCellsPotential rolescMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets
Lin Y, Ghazanfar S, Wang K, Gagnon-Bartsch J, Lo K, Su X, Han Z, Ormerod J, Speed T, Yang P, Yang J. scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets. Proceedings Of The National Academy Of Sciences Of The United States Of America 2019, 116: 9775-9784. PMID: 31028141, PMCID: PMC6525515, DOI: 10.1073/pnas.1820006116.Peer-Reviewed Original ResearchConceptsMultiple single-cell RNA-seq datasetsSingle-cell RNA-seq datasetsRNA-seq datasetsSingle-cell RNA sequencing dataRNA sequencing dataFurther biological insightsBiological discoveryBiological insightsSequencing dataStable expressionConcerted examinationRobust data integrationLarge collectionIndividual datasetsGenesMultiple collectionsPseudoreplicatesExpression