2024
A 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 ResearchMeSH KeywordsAlgorithmsComputational BiologyGene Expression ProfilingHumansModels, StatisticalRNA-SeqSequence Analysis, RNASingle-Cell AnalysisConceptsCell 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 matrix
2021
NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data
He L, Davila-Velderrain J, Sumida TS, Hafler DA, Kellis M, Kulminski AM. NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data. Communications Biology 2021, 4: 629. PMID: 34040149, PMCID: PMC8155058, DOI: 10.1038/s42003-021-02146-6.Peer-Reviewed Original ResearchConceptsNegative binomial mixed modelsBinomial mixed modelsSingle-cell dataHigh-dimensional integralsLarge sample approximationLaplace approximationCell-level expressionMixed modelsApproximationNebulaSpeed gainData setsOrders of magnitudeMarker gene identificationIntegralsModelOverdispersionFalse positive errors