2024
Single-Cell Analysis in Cerebrovascular Research: Primed for Breakthroughs and Clinical Impact
Albertson A, Winkler E, Yang A, Buckwalter M, Dingman A, Fan H, Herson P, McCullough L, Perez-Pinzon M, Sansing L, Sun D, Alkayed N. Single-Cell Analysis in Cerebrovascular Research: Primed for Breakthroughs and Clinical Impact. Stroke 2024, 56: 1082-1091. PMID: 39772596, PMCID: PMC11932790, DOI: 10.1161/strokeaha.124.049001.Peer-Reviewed Original ResearchA scalable approach to topic modelling in single-cell data by approximate pseudobulk projection
Subedi S, Sumida T, Park Y. A scalable approach to topic modelling in single-cell data by approximate pseudobulk projection. Life Science Alliance 2024, 7: e202402713. PMID: 39107066, PMCID: PMC11303850, DOI: 10.26508/lsa.202402713.Peer-Reviewed Original ResearchConceptsCell type-specific marker genesSingle-cell RNA-seq data analysisRNA-seq data analysisSingle-cell data analysisTopic modelsSingle-cell dataProbabilistic topic modelPathway annotationScalable approximation methodsLow memory consumptionComputation timeCellular statesMarker genesDictionary matrixLatent representationSingle-cellMemory consumptionTopic assignmentsComputing unitsFrequency vectorSelection stepCellsData analysisScalable approachData matrixSingle-cell biclustering for cell-specific transcriptomic perturbation detection in AD progression
Gong Y, Xu J, Wu M, Gao R, Sun J, Yu Z, Zhang Y. Single-cell biclustering for cell-specific transcriptomic perturbation detection in AD progression. Cell Reports Methods 2024, 4: 100742. PMID: 38554701, PMCID: PMC11045878, DOI: 10.1016/j.crmeth.2024.100742.Peer-Reviewed Original ResearchConceptsSnRNA-seq dataGene modulesAD progressionPathogenesis of Alzheimer's diseaseBiologically interpretable resultsSingle-cell data analysisGene regulatory changesFunctional gene modulesGene coexpression patternsAlzheimer's diseaseSingle-cell levelSnRNA-seqBiclustering methodsPolygenic diseaseBatch effectsDropout eventsCoexpression patternsNetwork biomarkersCell typesBiclusteringCellsGenesScRNABiologyComparative analysis
2021
OpenAnnotate: a web server to annotate the chromatin accessibility of genomic regions
Chen S, Liu Q, Cui X, Feng Z, Li C, Wang X, Zhang X, Wang Y, Jiang R. OpenAnnotate: a web server to annotate the chromatin accessibility of genomic regions. Nucleic Acids Research 2021, 49: w483-w490. PMID: 33999180, PMCID: PMC8262705, DOI: 10.1093/nar/gkab337.Peer-Reviewed Original ResearchConceptsGenomic regionsChromatin accessibilityRegulatory elementsAnnotation of genomic regionsActive DNA regulatory elementsFunction of transcription factorsRegulatory mechanismsGene functional relationshipsDNA regulatory elementsSingle-cell data analysisChromatin accessibility profilesCell type-specificHigh-throughput methodChromatin contactsTranscription factorsAccessibility profilesType-specificBiosample typesAnnotation resourcesPublic repositoriesChromatinUser-friendly functionsReal-time browsingWeb serverComprehensive profile
2019
Geometric Sketching Compactly Summarizes the Single-Cell Transcriptomic Landscape
Hie B, Cho H, DeMeo B, Bryson B, Berger B. Geometric Sketching Compactly Summarizes the Single-Cell Transcriptomic Landscape. Cell Systems 2019, 8: 483-493.e7. PMID: 31176620, PMCID: PMC6597305, DOI: 10.1016/j.cels.2019.05.003.Peer-Reviewed Original ResearchConceptsSingle-cell transcriptomic landscapeSingle-cell RNA sequencing studiesSingle-cell omicsCell typesSeq data integrationSingle-cell data analysisRare cell typesRNA sequencing studiesScRNA-seq dataTranscriptional diversityTranscriptomic landscapeBiological cell typesTranscriptomic heterogeneitySequencing studiesRare subpopulationAnalysis pipelineCellsUmbilical cord bloodEssential stepInflammatory macrophagesOmicsComprehensive visualizationDiversityGeometric sketchHundreds of thousands
2017
Scalable multi-sample single-cell data analysis by Partition-Assisted Clustering and Multiple Alignments of Networks
Li YH, Li D, Samusik N, Wang X, Guan L, Nolan GP, Wong H. Scalable multi-sample single-cell data analysis by Partition-Assisted Clustering and Multiple Alignments of Networks. PLOS Computational Biology 2017, 13: e1005875. PMID: 29281633, PMCID: PMC5760091, DOI: 10.1371/journal.pcbi.1005875.Peer-Reviewed Original Research
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