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
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