2022
Characterization of the COPD alveolar niche using single-cell RNA sequencing
Sauler M, McDonough JE, Adams TS, Kothapalli N, Barnthaler T, Werder RB, Schupp JC, Nouws J, Robertson MJ, Coarfa C, Yang T, Chioccioli M, Omote N, Cosme C, Poli S, Ayaub EA, Chu SG, Jensen KH, Gomez JL, Britto CJ, Raredon MSB, Niklason LE, Wilson AA, Timshel PN, Kaminski N, Rosas IO. Characterization of the COPD alveolar niche using single-cell RNA sequencing. Nature Communications 2022, 13: 494. PMID: 35078977, PMCID: PMC8789871, DOI: 10.1038/s41467-022-28062-9.Peer-Reviewed Original ResearchConceptsSingle-cell RNA sequencingRNA sequencingCell-specific mechanismsChronic obstructive pulmonary diseaseAdvanced chronic obstructive pulmonary diseaseTranscriptomic network analysisSingle-cell RNA sequencing profilesCellular stress toleranceAberrant cellular metabolismStress toleranceRNA sequencing profilesTranscriptional evidenceCellular metabolismAlveolar nicheSequencing profilesHuman alveolar epithelial cellsChemokine signalingAlveolar epithelial type II cellsObstructive pulmonary diseaseSitu hybridizationType II cellsEpithelial type II cellsSequencingCOPD pathobiologyHuman lung tissue samples
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
2015
Integrative phenotyping framework (iPF): integrative clustering of multiple omics data identifies novel lung disease subphenotypes
Kim S, Herazo-Maya JD, Kang DD, Juan-Guardela BM, Tedrow J, Martinez FJ, Sciurba FC, Tseng GC, Kaminski N. Integrative phenotyping framework (iPF): integrative clustering of multiple omics data identifies novel lung disease subphenotypes. BMC Genomics 2015, 16: 924. PMID: 26560100, PMCID: PMC4642618, DOI: 10.1186/s12864-015-2170-4.Peer-Reviewed Original Research
2014
T-RECS: STABLE SELECTION OF DYNAMICALLY FORMED GROUPS OF FEATURES WITH APPLICATION TO PREDICTION OF CLINICAL OUTCOMES
Altman R, Dunker A, Hunter L, Ritchie M, Murray T, Klein T, HUANG G, TSAMARDINOS I, RAGHU V, KAMINSKI N, BENOS P. T-RECS: STABLE SELECTION OF DYNAMICALLY FORMED GROUPS OF FEATURES WITH APPLICATION TO PREDICTION OF CLINICAL OUTCOMES. Biocomputing 2014, 20: 431-42. PMID: 25592602, PMCID: PMC4299881, DOI: 10.1142/9789814644730_0041.Peer-Reviewed Original ResearchConceptsTraditional feature selection methodsFeature selection methodCohort of patientsPersonalized medicine strategiesReal expression dataFeature selectionClassification accuracyCluster selectionBiological datasetsClinical outcomesCluster featuresLung diseaseBreast cancerSelection methodPatient classificationStructured natureMedicine strategiesSurvival dataTarget variablesEfficient selectionCohortStable selectionImportant featuresMissing value imputation in high-dimensional phenomic data: imputable or not, and how?
Liao SG, Lin Y, Kang DD, Chandra D, Bon J, Kaminski N, Sciurba FC, Tseng GC. Missing value imputation in high-dimensional phenomic data: imputable or not, and how? BMC Bioinformatics 2014, 15: 346. PMID: 25371041, PMCID: PMC4228077, DOI: 10.1186/s12859-014-0346-6.Peer-Reviewed Original ResearchConceptsImputation methodsSTS schemeReal data analysisData imputationMissing valuesDifferent imputation methodsBest imputation methodOrdinal data typeComplete data matrixValue imputation methodsMultivariate imputationWeighted hybridData matrixR packageValue imputationContinuous intensityImputation errorPhenomic dataSelection schemeReal datasetsSchemeMost methodsImputationSimulationsMicroarray experiments
2013
Peripheral Blood Mononuclear Cell Gene Expression Profiles Predict Poor Outcome in Idiopathic Pulmonary Fibrosis
Herazo-Maya JD, Noth I, Duncan SR, Kim S, Ma SF, Tseng GC, Feingold E, Juan-Guardela BM, Richards TJ, Lussier Y, Huang Y, Vij R, Lindell KO, Xue J, Gibson KF, Shapiro SD, Garcia JG, Kaminski N. Peripheral Blood Mononuclear Cell Gene Expression Profiles Predict Poor Outcome in Idiopathic Pulmonary Fibrosis. Science Translational Medicine 2013, 5: 205ra136. PMID: 24089408, PMCID: PMC4175518, DOI: 10.1126/scitranslmed.3005964.Peer-Reviewed Original ResearchMeSH KeywordsBiomarkersCD28 AntigensCD4 AntigensCluster AnalysisCohort StudiesGene Expression ProfilingHumansIdiopathic Pulmonary FibrosisLeukocytes, MononuclearOligonucleotide Array Sequence AnalysisReproducibility of ResultsReverse Transcriptase Polymerase Chain ReactionSignal TransductionTreatment OutcomeConceptsTransplant-free survivalIdiopathic pulmonary fibrosisPeripheral blood mononuclear cell gene expression profilesReplication cohortCell gene expression profilesPoor outcomePulmonary fibrosisQuantitative reverse transcription polymerase chain reactionReverse transcription-polymerase chain reactionProportional hazards modelTranscription-polymerase chain reactionGene expression profilesPotential cellular sourcesT cell activationIPF patientsLung transplantationMicroarray cohortPatient ageOutcome biomarkerPatient groupVital capacityPolymerase chain reactionT cellsDiscovery cohortITK expression
2010
Module-based prediction approach for robust inter-study predictions in microarray data
Mi Z, Shen K, Song N, Cheng C, Song C, Kaminski N, Tseng GC. Module-based prediction approach for robust inter-study predictions in microarray data. Bioinformatics 2010, 26: 2586-2593. PMID: 20719761, PMCID: PMC2951088, DOI: 10.1093/bioinformatics/btq472.Peer-Reviewed Original Research
2007
Effects of exercise training on quadriceps muscle gene expression in chronic obstructive pulmonary disease
Radom-Aizik S, Kaminski N, Hayek S, Halkin H, Cooper DM, Ben-Dov I. Effects of exercise training on quadriceps muscle gene expression in chronic obstructive pulmonary disease. Journal Of Applied Physiology 2007, 102: 1976-1984. PMID: 17483440, DOI: 10.1152/japplphysiol.00577.2006.Peer-Reviewed Original ResearchMeSH KeywordsAgedCase-Control StudiesCluster AnalysisEnergy MetabolismExerciseGene ExpressionGene Expression ProfilingHumansMaleOligonucleotide Array Sequence AnalysisOxidative StressOxygen ConsumptionProteasome Endopeptidase ComplexPulmonary Disease, Chronic ObstructiveQuadriceps MuscleReproducibility of ResultsReverse Transcriptase Polymerase Chain ReactionRNA, MessengerUbiquitinConceptsChronic obstructive pulmonary diseaseObstructive pulmonary diseaseCOPD patientsPulmonary diseaseExercise trainingAge-matched healthy menMuscle gene expressionHigh expressionSkeletal muscle functionExercise capacityGene expressionWalk testHealthy menControl subjectsNeedle biopsyMuscle functionVastus lateralisPatientsOxidative stressTraining responseFunctional parametersDiseaseTissue stressExpressionGene pathways
2006
Gene expression profiling of target genes in ventilator-induced lung injury
Dolinay T, Kaminski N, Felgendreher M, Kim HP, Reynolds P, Watkins SC, Karp D, Uhlig S, Choi AM. Gene expression profiling of target genes in ventilator-induced lung injury. Physiological Genomics 2006, 26: 68-75. PMID: 16569776, DOI: 10.1152/physiolgenomics.00110.2005.Peer-Reviewed Original ResearchMeSH KeywordsA Kinase Anchor ProteinsAmphiregulinAnimalsCell Cycle ProteinsCluster AnalysisCysteine-Rich Protein 61DNA-Binding ProteinsEGF Family of ProteinsGene Expression ProfilingGene Expression RegulationGlycoproteinsImmediate-Early ProteinsImmunohistochemistryIntercellular Signaling Peptides and ProteinsInterleukin-11LipopolysaccharidesLungLung InjuryMaleMiceMice, Inbred BALB CNuclear Receptor Subfamily 4, Group A, Member 1Oligonucleotide Array Sequence AnalysisReceptors, Cytoplasmic and NuclearReceptors, SteroidReproducibility of ResultsRespiration, ArtificialRNA, MessengerTranscription FactorsConceptsVentilator-induced lung injuryLung injuryAcute respiratory distress syndromeHigh-pressure mechanical ventilationRespiratory distress syndromeHigh-pressure ventilationLow-pressure ventilationClassical inflammatory pathwaysGrowth factor-related genesDistress syndromeMechanical ventilationInflammatory pathwaysLPS treatmentInflammatory responseReal-time PCRMouse lungGene expression profilingProtein expressionImmunoblotting assaysMRNA expression patternsVentilationOverventilationLungNovel candidate genesInjury
2005
Analysis of Microarray Experiments for Pulmonary Fibrosis
Davé NB, Kaminski N. Analysis of Microarray Experiments for Pulmonary Fibrosis. Methods In Molecular Medicine 2005, 117: 333-358. PMID: 16118461, DOI: 10.1385/1-59259-940-0:333.Peer-Reviewed Original Research
2001
DNA microarrays identification of primary and secondary target genes regulated by p53
Kannan K, Amariglio N, Rechavi G, Jakob-Hirsch J, Kela I, Kaminski N, Getz G, Domany E, Givol D. DNA microarrays identification of primary and secondary target genes regulated by p53. Oncogene 2001, 20: 2225-2234. PMID: 11402317, DOI: 10.1038/sj.onc.1204319.Peer-Reviewed Original ResearchConceptsSecondary target genesTarget genesCell linesTumor suppressor p53Primary targetP53 activatesPresence of cycloheximideSuch genesTranscriptional programsTranscriptional changesAdditional genesDNA repairAbsence of cycloheximideMurine p53Primary genesOligonucleotide microarraysCell cycleSuppressor p53GenesProtein synthesisCell adhesionLung cancer cell linesCancer cell linesCell phenotypeHuman lung cancer cell linesThe use of microarrays in medicine.
Cojocaru GS, Rechavi G, Kaminski N. The use of microarrays in medicine. Israel Medical Association Journal 2001, 3: 292-6. PMID: 11344848.Peer-Reviewed Original Research
2000
Global analysis of gene expression in pulmonary fibrosis reveals distinct programs regulating lung inflammation and fibrosis
Kaminski N, Allard J, Pittet J, Zuo F, Griffiths M, Morris D, Huang X, Sheppard D, Heller R. Global analysis of gene expression in pulmonary fibrosis reveals distinct programs regulating lung inflammation and fibrosis. Proceedings Of The National Academy Of Sciences Of The United States Of America 2000, 97: 1778-1783. PMID: 10677534, PMCID: PMC26512, DOI: 10.1073/pnas.97.4.1778.Peer-Reviewed Original ResearchConceptsPulmonary fibrosisLung inflammationBleomycin administrationSusceptible miceMultiple time pointsFibrotic responseFibrosisFibrotic diseasesInflammationMore effective strategiesGene expressionTime pointsMiceBeta6 subunitMolecular mechanismsSequential inductionGene expression patternsExpression patternsNull mutationResponseEffective strategyLungExpressionBleomycinGene expression programs