2017
A Dirichlet process mixture model for clustering longitudinal gene expression data
Sun J, Herazo‐Maya J, Kaminski N, Zhao H, Warren JL. A Dirichlet process mixture model for clustering longitudinal gene expression data. Statistics In Medicine 2017, 36: 3495-3506. PMID: 28620908, PMCID: PMC5583037, DOI: 10.1002/sim.7374.Peer-Reviewed Original ResearchConceptsLongitudinal gene expression profilesDirichlet process prior distributionRegression coefficientsExtensive simulation studyLongitudinal gene expression dataBayesian settingPrior distributionClustering methodFactor analysis modelDimensionality challengeStatistical methodsSimulation studyNovel clustering methodHigh dimensionality challengeSubgroup identificationImportant problemGene expression dataInteresting subgroupsClusteringCoefficientAnalysis modelModelExpression data
2002
Practical Approaches to Analyzing Results of Microarray Experiments
Kaminski N, Friedman N. Practical Approaches to Analyzing Results of Microarray Experiments. American Journal Of Respiratory Cell And Molecular Biology 2002, 27: 125-132. PMID: 12151303, DOI: 10.1165/ajrcmb.27.2.f247.Peer-Reviewed Original ResearchConceptsCommon clustering methodsStatistical meaningLarge-scale gene expression dataMicroarray experimentsClustering methodGene expression dataProblemAdvanced toolsValid directionsMain challengesExpression dataApproachEfficient useTechnologySolutionPractical focusPractical approachSuccessful implementation