2022
DeepVelo: Single-cell transcriptomic deep velocity field learning with neural ordinary differential equations
Chen Z, King W, Hwang A, Gerstein M, Zhang J. DeepVelo: Single-cell transcriptomic deep velocity field learning with neural ordinary differential equations. Science Advances 2022, 8: eabq3745. PMID: 36449617, PMCID: PMC9710871, DOI: 10.1126/sciadv.abq3745.Peer-Reviewed Original ResearchOrdinary differential equationsDifferential equationsTranscriptome dynamicsSingle-cell gene expression measurementsNeural ordinary differential equationsSingle-cell sequencing technologiesVelocity fieldIndividual cellsGene expression changesGene expression profilesDynamical systemsGene expression measurementsTranscriptional dynamicsRNA velocityDifferent sequencing platformsChaotic propertiesSequencing technologiesCell statesPerturbation analysisRegulatory relationshipsData-Driven DiscoveryExpression changesExpression profilesSequencing platformsDriver genes
2021
A Markov random field model for network-based differential expression analysis of single-cell RNA-seq data
Li H, Zhu B, Xu Z, Adams T, Kaminski N, Zhao H. A Markov random field model for network-based differential expression analysis of single-cell RNA-seq data. BMC Bioinformatics 2021, 22: 524. PMID: 34702190, PMCID: PMC8549347, DOI: 10.1186/s12859-021-04412-0.Peer-Reviewed Original ResearchConceptsMarkov random field modelRandom field modelMean field-like approximationField modelSpecific DEGsExpectation maximizationSingle-cell sequencing technologiesProtein-coding genesRNA sequencing data setsSingle-cell RNA-seq dataCell-type levelCell typesGibbs samplerSingle-cell RNA sequencing data setsCell-cell networksDifferential expression analysisRNA-seq dataGene network informationStatistical powerType I error ratesDifferent expression levelsMRF modelI error rateModel parametersBiological networks
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