2024
BIDCell: 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
Thinking 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 statesSeverityOutcomesCellsLungSymptoms
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 measurementsCovariationVariability
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 role