2022
Integrative analyses for the identification of idiopathic pulmonary fibrosis-associated genes and shared loci with other diseases
Chen M, Zhang Y, Adams T, Ji D, Jiang W, Wain LV, Cho M, Kaminski N, Zhao H. Integrative analyses for the identification of idiopathic pulmonary fibrosis-associated genes and shared loci with other diseases. Thorax 2022, 78: 792-798. PMID: 36216496, PMCID: PMC10083187, DOI: 10.1136/thorax-2021-217703.Peer-Reviewed Original ResearchConceptsTranscriptome-wide association analysisLocal genetic correlationsSingle-cell expression dataCandidate genesTranscription factorsIntegrative analysisGenomic regionsGenetic correlationsExpression dataTF target genesComplex genetic architectureTF binding sitesWide association studyPower of GWASSpecific DEGsGenetic architectureNew genesNovel genesCausal genesTarget genesGenetic basisEnrichment analysisAssociation studiesRegulatory roleAssociation analysisCINS: Cell Interaction Network inference from Single cell expression data
Yuan Y, Cosme C, Adams TS, Schupp J, Sakamoto K, Xylourgidis N, Ruffalo M, Li J, Kaminski N, Bar-Joseph Z. CINS: Cell Interaction Network inference from Single cell expression data. PLOS Computational Biology 2022, 18: e1010468. PMID: 36095011, PMCID: PMC9499239, DOI: 10.1371/journal.pcbi.1010468.Peer-Reviewed Original ResearchConceptsCell type interactionsSingle-cell expression dataSingle-cell RNA-seq dataRNA-seq dataScRNA-seq experimentsCell-cell interactionsExpression dataCell typesMouse datasetsNetwork inferenceCell interactionsInteraction predictionNetwork analysisInference pipelineGenesCINSProteinInteractionBayesian network analysis
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 yearsReconstructing differentiation networks and their regulation from time series single-cell expression data
Ding J, Aronow BJ, Kaminski N, Kitzmiller J, Whitsett JA, Bar-Joseph Z. Reconstructing differentiation networks and their regulation from time series single-cell expression data. Genome Research 2018, 28: 383-395. PMID: 29317474, PMCID: PMC5848617, DOI: 10.1101/gr.225979.117.Peer-Reviewed Original ResearchTranscription factorsSingle-cell expression dataSingle-cell RNA-seq dataRNA-seq dataDiverse cell populationsGene expression levelsDifferent cell typesStages of organogenesisCell fateDescendant cellsDifferentiation networkExpression similarityKey regulatorRegulatory informationExpression dataCell typesProgenitor cellsCell trajectoriesExpression levelsCell populationsDevelopmental dataCellsLineagesOrganogenesisRegulator
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 domainSelecting the most appropriate time points to profile in high-throughput studies
Kleyman M, Sefer E, Nicola T, Espinoza C, Chhabra D, Hagood JS, Kaminski N, Ambalavanan N, Bar-Joseph Z. Selecting the most appropriate time points to profile in high-throughput studies. ELife 2017, 6: e18541. PMID: 28124972, PMCID: PMC5319842, DOI: 10.7554/elife.18541.Peer-Reviewed Original ResearchConceptsMolecular dataMouse lung developmentHigh-throughput profilingHigh-throughput studiesDNA methylationGene expressionThroughput profilingExpression dataTime series experimentsExpression valuesLung developmentSeries experimentsBiological systemsGenesMethylationMiRNAProteinProfilingExpressionTime pointsKey design strategiesLarge setAppropriate time points
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
2013
Reconstructing dynamic microRNA-regulated interaction networks
Schulz MH, Pandit KV, Cardenas C, Ambalavanan N, Kaminski N, Bar-Joseph Z. Reconstructing dynamic microRNA-regulated interaction networks. Proceedings Of The National Academy Of Sciences Of The United States Of America 2013, 110: 15686-15691. PMID: 23986498, PMCID: PMC3785769, DOI: 10.1073/pnas.1303236110.Peer-Reviewed Original ResearchConceptsTranscription factorsGene expressionDynamic Regulatory Events MinerTemporal gene expressionDynamic regulatory networksSpecific developmental phasesMRNA expression dataLung developmentRegulatory networksMiRNA targetsInteraction networksImportant miRNAsExpression dataMiRNAsAdditional miRNAsLung differentiationDevelopmental phasesMiRNAPostnatal lung developmentProgression pathwaysProliferation assaysExpressionRegulationMRNA expressionMicroRNAs
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