2024
SANTO: a coarse-to-fine alignment and stitching method for spatial omics
Li H, Lin Y, He W, Han W, Xu X, Xu C, Gao E, Zhao H, Gao X. SANTO: a coarse-to-fine alignment and stitching method for spatial omics. Nature Communications 2024, 15: 6048. PMID: 39025895, PMCID: PMC11258319, DOI: 10.1038/s41467-024-50308-x.Peer-Reviewed Original ResearchAntecedent Cardiac Arrest Status of Donation After Circulatory Determination of Death (DCDD) Kidney Donors and the Risk of Delayed Graft Function After Kidney Transplantation: A Cohort Study.
Philipoff A, Lin Y, Teixeira-Pinto A, Gately R, Craig J, Opdam H, Chapman J, Pleass H, Rogers N, Davies C, McDonald S, Yang J, Lopez P, Wong G, Lim W. Antecedent Cardiac Arrest Status of Donation After Circulatory Determination of Death (DCDD) Kidney Donors and the Risk of Delayed Graft Function After Kidney Transplantation: A Cohort Study. Transplantation 2024 PMID: 38685196, DOI: 10.1097/tp.0000000000005022.Peer-Reviewed Original ResearchRisk of delayed graft functionCardiac arrestAllograft lossGraft functionDuration of cardiac arrestAssociated with DGFDelayed graft functionKidney transplant recipientsRisk of DGFGlomerular filtration rateRecipients of kidneysNew Zealand DialysisCirculatory determination of deathDetermination of deathArrest statusAllograft outcomesTransplant recipientsCardiorespiratory supportKidney transplantationFiltration rateCohort studyPosttransplant eGFRCox regressionOdds ratioAllograftBIDCell: Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data
Fu X, Lin Y, Lin D, Mechtersheimer D, Wang C, Ameen F, Ghazanfar S, Patrick E, Kim J, Yang J. BIDCell: Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data. Nature Communications 2024, 15: 509. PMID: 38218939, PMCID: PMC10787788, DOI: 10.1038/s41467-023-44560-w.Peer-Reviewed Original ResearchConceptsGene expressionSingle-cell transcriptomic dataSpatial expression analysisMap of gene expressionSpatial mapping of gene expressionTranscriptome dataBiological discoveryExpression analysisTranscriptomic platformsOversized cellsPublic repositoriesCell morphologyState-of-the-art methodsSelf-supervised learningDeep learning-based frameworkState-of-the-artTissue typesLearning-based frameworkHigh-resolution spatial mappingCellsExpressionSignificant analytical challengeSegmentation performanceLoss functionRecent advances
2023
Data integration and inference of gene regulation using single-cell temporal multimodal data with scTIE
Lin Y, Wu T, Chen X, Wan S, Chao B, Xin J, Yang J, Wong W, Wang Y. Data integration and inference of gene regulation using single-cell temporal multimodal data with scTIE. Genome Research 2023, 34: gr.277960.123. PMID: 38190633, PMCID: PMC10903952, DOI: 10.1101/gr.277960.123.Peer-Reviewed Original ResearchThinking process templates for constructing data stories with SCDNEY
Cao Y, Tran A, Kim H, Robertson N, Lin Y, Torkel M, Yang P, Patrick E, Ghazanfar S, Yang J. Thinking process templates for constructing data stories with SCDNEY. F1000Research 2023, 12: 261. DOI: 10.12688/f1000research.130623.2.Peer-Reviewed Original ResearchPcdh19 mediates olfactory sensory neuron coalescence during postnatal stages and regeneration
Martinez A, Chung A, Huang S, Bisogni A, Lin Y, Cao Y, Williams E, Kim J, Yang J, Lin D. Pcdh19 mediates olfactory sensory neuron coalescence during postnatal stages and regeneration. IScience 2023, 26: 108220. PMID: 37965156, PMCID: PMC10641745, DOI: 10.1016/j.isci.2023.108220.Peer-Reviewed Original ResearchOlfactory sensory neuronsLateral glomeruliProjection of olfactory sensory neuronsMouse olfactory systemComplement of genesCell adhesion moleculesOlfactory bulbSensory neuronsCadherin superfamilySevere phenotypeSingle cell analysisPostnatal stagesAdhesion moleculesOlfactory systemGlomeruliOlfactory mapGenesOdorant receptorsProtocadherinMaleDifferential effectsCell analysisMOR28FemalesSuperfamilyAtlas-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 ResearchThinking process templates for constructing data stories with SCDNEY
Cao Y, Tran A, Kim H, Robertson N, Lin Y, Torkel M, Yang P, Patrick E, Ghazanfar S, Yang J. Thinking process templates for constructing data stories with SCDNEY. F1000Research 2023, 12: 261. DOI: 10.12688/f1000research.130623.1.Peer-Reviewed Original Research
2022
Scalable workflow for characterization of cell-cell communication in COVID-19 patients
Lin Y, Loo L, Tran A, Lin D, Moreno C, Hesselson D, Neely G, Yang J. Scalable workflow for characterization of cell-cell communication in COVID-19 patients. PLOS Computational Biology 2022, 18: e1010495. PMID: 36197936, PMCID: PMC9534414, DOI: 10.1371/journal.pcbi.1010495.Peer-Reviewed Original ResearchConceptsCOVID-19 patientsSevere patientsDisease severityDysfunctional immune responseDistinct disease outcomesHigher mortality riskSARS-CoV-2Different disease statesImmune cellsLung tissueDisease outcomeImmune responseMortality riskPatientsCell-cell interactionsPathogenic outcomesCritical symptomsCell-cell interaction patternsDisease statesSeverityOutcomesCellsLungSymptomsscFeatures: multi-view representations of single-cell and spatial data for disease outcome prediction
Cao Y, Lin Y, Patrick E, Yang P, Yang J. scFeatures: multi-view representations of single-cell and spatial data for disease outcome prediction. Bioinformatics 2022, 38: 4745-4753. PMID: 36040148, PMCID: PMC9563679, DOI: 10.1093/bioinformatics/btac590.Peer-Reviewed Original Research3D reconstruction of spatial expression
Lin Y, Yang J. 3D reconstruction of spatial expression. Nature Methods 2022, 19: 526-527. PMID: 35577956, DOI: 10.1038/s41592-022-01476-5.Commentaries, Editorials and LettersscJoint 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 ResearchConceptsData 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
2021
Uncovering cell identity through differential stability with Cepo
Kim H, Wang K, Chen C, Lin Y, Tam P, Lin D, Yang J, Yang P. Uncovering cell identity through differential stability with Cepo. Nature Computational Science 2021, 1: 784-790. PMID: 38217190, DOI: 10.1038/s43588-021-00172-2.Peer-Reviewed Original Research
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 measurementsCovariationVariabilityscClassify: sample size estimation and multiscale classification of cells using single and multiple reference
Lin Y, Cao Y, Kim H, Salim A, Speed T, Lin D, Yang P, Yang J. scClassify: sample size estimation and multiscale classification of cells using single and multiple reference. Molecular Systems Biology 2020, 16: e9389. PMID: 32567229, PMCID: PMC7306901, DOI: 10.15252/msb.20199389.Peer-Reviewed Original ResearchConceptsType hierarchyKey computational challengeType identificationMultiple referencesType classification methodMultiscale classificationEnsemble learningCell type hierarchyClassification frameworkClassification methodPairs of referenceJoint classificationComputational challengesAccurate classificationLarge collectionTesting dataArt methodologiesDatasetLevel of complexityExperimental datasetsCell type identificationClassificationSingle-cell atlasesNovel applicationScalabilityCiteFuse 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 ResearchConceptsCITE-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