2020
Genetic analyses identify GSDMB associated with asthma severity, exacerbations, and antiviral pathways
Li X, Christenson SA, Modena B, Li H, Busse WW, Castro M, Denlinger LC, Erzurum SC, Fahy JV, Gaston B, Hastie AT, Israel E, Jarjour NN, Levy BD, Moore WC, Woodruff PG, Kaminski N, Wenzel SE, Bleecker ER, Meyers DA, Program N. Genetic analyses identify GSDMB associated with asthma severity, exacerbations, and antiviral pathways. Journal Of Allergy And Clinical Immunology 2020, 147: 894-909. PMID: 32795586, PMCID: PMC7876167, DOI: 10.1016/j.jaci.2020.07.030.Peer-Reviewed Original ResearchConceptsExpression quantitative trait loci (eQTL) analysisQuantitative trait locus (QTL) analysisSingle nucleotide polymorphismsGasdermin BMultiple single nucleotide polymorphismsFunctional genesExpression levelsLocus analysisAntiviral pathwaysGenes/single-nucleotide polymorphismsWhole genome sequencesGene expression dataEpithelial cellsImmune system pathwaysHigh expression levelsHuman bronchial epithelial cellsIFN regulatory factorGPI attachmentGSDMB expressionAsthma susceptibilityGenetic analysisGene expressionPathway analysisBronchial epithelial cellsRegulatory factors
2018
iDREM: Interactive visualization of dynamic regulatory networks
Ding J, Hagood JS, Ambalavanan N, Kaminski N, Bar-Joseph Z. iDREM: Interactive visualization of dynamic regulatory networks. PLOS Computational Biology 2018, 14: e1006019. PMID: 29538379, PMCID: PMC5868853, DOI: 10.1371/journal.pcbi.1006019.Peer-Reviewed Original ResearchConceptsDynamic regulatory networksRegulatory networksHigh-throughput time series dataInteraction dataProtein-DNA interaction dataSingle-cell RNA-seqTime series gene expression dataStatic datasetsInteractive visualizationGene expression dataData typesRNA-seqTime series dataBiological processesExpression dataMiRNA expressionNetworkSeries dataImportant challengeNew versionDevelopmental dataNovel hypothesisUnified modelMultiple labsRecent years
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 dataMicrobes Are Associated with Host Innate Immune Response in Idiopathic Pulmonary Fibrosis
Huang Y, Ma SF, Espindola MS, Vij R, Oldham JM, Huffnagle GB, Erb-Downward JR, Flaherty KR, Moore BB, White ES, Zhou T, Li J, Lussier YA, Han MK, Kaminski N, Garcia JG, Hogaboam CM, Martinez FJ, Noth I. Microbes Are Associated with Host Innate Immune Response in Idiopathic Pulmonary Fibrosis. American Journal Of Respiratory And Critical Care Medicine 2017, 196: 208-219. PMID: 28157391, PMCID: PMC5519968, DOI: 10.1164/rccm.201607-1525oc.Peer-Reviewed Original ResearchConceptsProgression-free survivalMicrobial diversityRegulated signaling pathwaysNOD-like receptor signalingRNA sequencing dataGene expression dataMicroarray gene expression dataImmune response pathwaysMicrobial interactionsMicrobial communitiesHost innate immune responseResponse pathwaysLung microbial communityLeukocyte phenotypeImmune responseSequencing dataNetwork analysisShannon indexSignaling pathwaysToll-like receptor 9 stimulationExpression associationsExpression dataIndividual generaIdiopathic pulmonary fibrosis progressionOligomerization domain
2015
Alterations in Gene Expression and DNA Methylation during Murine and Human Lung Alveolar Septation
Cuna A, Halloran B, Faye-Petersen O, Kelly D, Crossman DK, Cui X, Pandit K, Kaminski N, Bhattacharya S, Ahmad A, Mariani TJ, Ambalavanan N. Alterations in Gene Expression and DNA Methylation during Murine and Human Lung Alveolar Septation. American Journal Of Respiratory Cell And Molecular Biology 2015, 53: 60-73. PMID: 25387348, PMCID: PMC4566107, DOI: 10.1165/rcmb.2014-0160oc.Peer-Reviewed Original ResearchConceptsDNA methylationNormal septationGene expressionGenome-wide DNA methylation dataMajor epigenetic mechanismsLung developmentNumber of genesMouse lung developmentGene of interestDNA methylation dataGene expression dataMicroarray gene expression dataAlveolar septationCoordinated expressionEpigenetic mechanismsMethylated DNAMultiple genesMicroarray analysisMethylation dataExpression dataGenesMethylationExtracellular matrixAltered expressionAntioxidant defense
2004
Comparative analysis of algorithms for signal quantitation from oligonucleotide microarrays
Barash Y, Dehan E, Krupsky M, Franklin W, Geraci M, Friedman N, Kaminski N. Comparative analysis of algorithms for signal quantitation from oligonucleotide microarrays. Bioinformatics 2004, 20: 839-846. PMID: 14751998, DOI: 10.1093/bioinformatics/btg487.Peer-Reviewed Original Research
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