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
2020
Disease state prediction from single-cell data using graph attention networks
Ravindra N, Sehanobish A, Pappalardo J, Hafler D, van Dijk D. Disease state prediction from single-cell data using graph attention networks. 2020, 121-130. DOI: 10.1145/3368555.3384449.Peer-Reviewed Original Research
2014
Joint Modeling and Registration of Cell Populations in Cohorts of High-Dimensional Flow Cytometric Data
Pyne S, Lee SX, Wang K, Irish J, Tamayo P, Nazaire MD, Duong T, Ng SK, Hafler D, Levy R, Nolan GP, Mesirov J, McLachlan GJ. Joint Modeling and Registration of Cell Populations in Cohorts of High-Dimensional Flow Cytometric Data. PLOS ONE 2014, 9: e100334. PMID: 24983991, PMCID: PMC4077578, DOI: 10.1371/journal.pone.0100334.Peer-Reviewed Original ResearchConceptsMultivariate probability distributionProbability distributionMultivariate responseJCM modelMultiple experimental conditionsJoint modelingJoint clusteringSimultaneous modelingComputational methodsRegistration of populationTypical experimentModelingNew samplesModelJCMFlow cytometric dataMultiparametric cytometrySystem-level variationApplications
2013
Models of Somatic Hypermutation Targeting and Substitution Based on Synonymous Mutations from High-Throughput Immunoglobulin Sequencing Data
Yaari G, Vander Heiden J, Uduman M, Gadala-Maria D, Gupta N, Stern JN, O’Connor K, Hafler DA, Laserson U, Vigneault F, Kleinstein SH. Models of Somatic Hypermutation Targeting and Substitution Based on Synonymous Mutations from High-Throughput Immunoglobulin Sequencing Data. Frontiers In Immunology 2013, 4: 358. PMID: 24298272, PMCID: PMC3828525, DOI: 10.3389/fimmu.2013.00358.Peer-Reviewed Original ResearchAccurate background modelSynonymous mutationsNon-coding regionsParticular codon usageNon-functional sequencesComputational analysis methodsObserved mutation patternExisting modelsBackground modelInfluence of selectionCodon usageSHM targetingBase compositionImproved modelSequencing dataNucleotide substitutionsAnalysis methodStatistical analysisFunctional sequencesMutation targetingB-cell cancersModelSomatic hypermutation patternsMutationsHypermutation patterns