2022
scFeatures: 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 ResearchHumansSoftware
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: msb199389. 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
scMerge 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