2021
Immunophenotyping assessment in a COVID-19 cohort (IMPACC): A prospective longitudinal study
, , Rouphael N, Maecker H, Montgomery R, Diray-Arce J, Kleinstein S, Altman M, Bosinger S, Eckalbar W, Guan L, Hough C, Krammer F, Langelier C, Levy O, McEnaney K, Peters B, Rahman A, Rajan J, Sigelman S, Steen H, van Bakel H, Ward A, Wilson M, Woodruff P, Zamecnik C, Augustine A, Ozonoff A, Reed E, Becker P, Higuita N, Altman M, Atkinson M, Baden L, Becker P, Bime C, Brakenridge S, Calfee C, Cairns C, Corry D, Davis M, Augustine A, Ehrlich L, Haddad E, Erle D, Fernandez-Sesma A, Hafler D, Hough C, Kheradmand F, Kleinstein S, Kraft M, Levy O, McComsey G, Melamed E, Messer W, Metcalf J, Montgomery R, Nadeau K, Ozonoff A, Peters B, Pulendran B, Reed E, Rouphael N, Sarwal M, Schaenman J, Sekaly R, Shaw A, Simon V. Immunophenotyping assessment in a COVID-19 cohort (IMPACC): A prospective longitudinal study. Science Immunology 2021, 6: eabf3733. PMID: 34376480, PMCID: PMC8713959, DOI: 10.1126/sciimmunol.abf3733.Peer-Reviewed Original ResearchConceptsCOVID-19 cohortProspective longitudinal studyHost immune responseLongitudinal studyCOVID-19Identification of biomarkersHospitalized patientsRespiratory secretionsClinical criteriaDisease progressionImmune responseRadiographic dataImmunologic assaysEffective therapeuticsOptimal timingStudy designBiologic samplingSuch interventionsCohortSeveritySample collectionAssay protocolsPatientsNEBULA 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
2016
Solving Immunology?
Vodovotz Y, Xia A, Read EL, Bassaganya-Riera J, Hafler DA, Sontag E, Wang J, Tsang JS, Day JD, Kleinstein SH, Butte AJ, Altman MC, Hammond R, Sealfon SC. Solving Immunology? Trends In Immunology 2016, 38: 116-127. PMID: 27986392, PMCID: PMC5695553, DOI: 10.1016/j.it.2016.11.006.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 ResearchMeSH KeywordsAlgorithmsCluster AnalysisComputational BiologyComputer SimulationFlow CytometryHumansSoftwareConceptsMultivariate probability distributionProbability distributionMultivariate responseJCM modelMultiple experimental conditionsJoint modelingJoint clusteringSimultaneous modelingComputational methodsRegistration of populationTypical experimentModelingNew samplesModelJCMFlow cytometric dataMultiparametric cytometrySystem-level variationApplications