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 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 applicationScalability
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