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
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