2024
SDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data
Liu Y, Li N, Qi J, Xu G, Zhao J, Wang N, Huang X, Jiang W, Wei H, Justet A, Adams T, Homer R, Amei A, Rosas I, Kaminski N, Wang Z, Yan X. SDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data. Genome Biology 2024, 25: 271. PMID: 39402626, PMCID: PMC11475911, DOI: 10.1186/s13059-024-03416-2.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsGene Expression ProfilingHumansMachine LearningRegression AnalysisRNA-SeqSequence Analysis, RNASingle-Cell AnalysisSoftwareTranscriptome
2023
iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects
Liu Y, Zhao J, Adams T, Wang N, Schupp J, Wu W, McDonough J, Chupp G, Kaminski N, Wang Z, Yan X. iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects. BMC Bioinformatics 2023, 24: 318. PMID: 37608264, PMCID: PMC10463720, DOI: 10.1186/s12859-023-05432-8.Peer-Reviewed Original ResearchA novel Bayesian framework for harmonizing information across tissues and studies to increase cell type deconvolution accuracy
Deng W, Li B, Wang J, Jiang W, Yan X, Li N, Vukmirovic M, Kaminski N, Wang J, Zhao H. A novel Bayesian framework for harmonizing information across tissues and studies to increase cell type deconvolution accuracy. Briefings In Bioinformatics 2023, 24: bbac616. PMID: 36631398, PMCID: PMC9851324, DOI: 10.1093/bib/bbac616.Peer-Reviewed Original Research
2022
CINS: 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 ResearchMeSH KeywordsAnimalsBayes TheoremCell CommunicationGene Expression ProfilingLigandsMiceSequence Analysis, RNASingle-Cell AnalysisConceptsCell 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
2021
A Markov random field model for network-based differential expression analysis of single-cell RNA-seq data
Li H, Zhu B, Xu Z, Adams T, Kaminski N, Zhao H. A Markov random field model for network-based differential expression analysis of single-cell RNA-seq data. BMC Bioinformatics 2021, 22: 524. PMID: 34702190, PMCID: PMC8549347, DOI: 10.1186/s12859-021-04412-0.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsGene Expression ProfilingGene Regulatory NetworksHumansRNA-SeqSequence Analysis, RNASingle-Cell AnalysisConceptsMarkov random field modelRandom field modelMean field-like approximationField modelSpecific DEGsExpectation maximizationSingle-cell sequencing technologiesProtein-coding genesRNA sequencing data setsSingle-cell RNA-seq dataCell-type levelCell typesGibbs samplerSingle-cell RNA sequencing data setsCell-cell networksDifferential expression analysisRNA-seq dataGene network informationStatistical powerType I error ratesDifferent expression levelsMRF modelI error rateModel parametersBiological networks
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
2019
BAL Cell Gene Expression in Severe Asthma Reveals Mechanisms of Severe Disease and Influences of Medications
Weathington N, O’Brien M, Radder J, Whisenant TC, Bleecker ER, Busse WW, Erzurum SC, Gaston B, Hastie A, Jarjour N, Meyers D, Milosevic J, Moore W, Tedrow J, Trudeau J, Wong H, Wu W, Kaminski N, Wenzel S, Modena B. BAL Cell Gene Expression in Severe Asthma Reveals Mechanisms of Severe Disease and Influences of Medications. American Journal Of Respiratory And Critical Care Medicine 2019, 200: 837-856. PMID: 31161938, PMCID: PMC6812436, DOI: 10.1164/rccm.201811-2221oc.Peer-Reviewed Original ResearchMeSH KeywordsAdrenergic beta-AgonistsAdultAsthmaBronchoalveolar Lavage FluidCase-Control StudiesCyclic AMPEosinophilsEpithelial CellsFemaleGene ExpressionHumansIn Vitro TechniquesLymphocytesMacrophages, AlveolarMaleNeutrophilsSequence Analysis, RNASeverity of Illness IndexSignal TransductionTHP-1 CellsConceptsCell gene expressionGene expressionAirway epithelial cell gene expressionEpithelial cell gene expressionGlobal gene expressionCellular gene expressionCell expression profilesAsthma susceptibility lociProtein levelsSystem-wide analysisExpression networksImportant disease mechanismCoexpression networkCellular milieuExpression changesExpression profilesSusceptibility lociCellular modelDisease mechanismsBiomolecular mechanismsNew targetsRobust upregulationSample traitsGenesExpression
2017
Transcriptome profiles in sarcoidosis and their potential role in disease prediction
Schupp JC, Vukmirovic M, Kaminski N, Prasse A. Transcriptome profiles in sarcoidosis and their potential role in disease prediction. Current Opinion In Pulmonary Medicine 2017, 23: 487-492. PMID: 28590292, PMCID: PMC5637542, DOI: 10.1097/mcp.0000000000000403.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsMeSH KeywordsGene Expression ProfilingGenome-Wide Association StudyHumansInterferon-gammaSarcoidosisSequence Analysis, RNASignal TransductionTh1 CellsTranscriptomeConceptsGenome-wide expression studiesWide expression studiesTranscriptome profilesTranscriptomic dataRNA sequencingExpression studiesGene expressionMolecular mechanismsLarge prospective followTh1 immune responseTranscriptomeNonnecrotizing granulomasProspective followSystemic diseaseDisease progressionTreatment outcomesImmune responseSarcoidosisPotential roleControl tissuesProgressive sarcoidosisKey roleDiseaseTranscriptomicsGranulomasIdentification and validation of differentially expressed transcripts by RNA-sequencing of formalin-fixed, paraffin-embedded (FFPE) lung tissue from patients with Idiopathic Pulmonary Fibrosis
Vukmirovic M, Herazo-Maya JD, Blackmon J, Skodric-Trifunovic V, Jovanovic D, Pavlovic S, Stojsic J, Zeljkovic V, Yan X, Homer R, Stefanovic B, Kaminski N. Identification and validation of differentially expressed transcripts by RNA-sequencing of formalin-fixed, paraffin-embedded (FFPE) lung tissue from patients with Idiopathic Pulmonary Fibrosis. BMC Pulmonary Medicine 2017, 17: 15. PMID: 28081703, PMCID: PMC5228096, DOI: 10.1186/s12890-016-0356-4.Peer-Reviewed Original ResearchConceptsPaired-end sequencingTranscript profilingHuman genomeRNA sequencingTranscriptomic profilingFFPE lung tissuesSequencing readsLung tissueTotal RNABackgroundIdiopathic pulmonary fibrosisLethal lung diseaseSequencingReadsProfilingPulmonary fibrosisLung diseaseUnknown etiologyIPF tissueGenomeHiSeqTissueTopHat2GenesIPFRNA
2016
Genome-wide imputation study identifies novel HLA locus for pulmonary fibrosis and potential role for auto-immunity in fibrotic idiopathic interstitial pneumonia
Fingerlin TE, Zhang W, Yang IV, Ainsworth HC, Russell PH, Blumhagen RZ, Schwarz MI, Brown KK, Steele MP, Loyd JE, Cosgrove GP, Lynch DA, Groshong S, Collard HR, Wolters PJ, Bradford WZ, Kossen K, Seiwert SD, du Bois RM, Garcia CK, Devine MS, Gudmundsson G, Isaksson HJ, Kaminski N, Zhang Y, Gibson KF, Lancaster LH, Maher TM, Molyneaux PL, Wells AU, Moffatt MF, Selman M, Pardo A, Kim DS, Crapo JD, Make BJ, Regan EA, Walek DS, Daniel JJ, Kamatani Y, Zelenika D, Murphy E, Smith K, McKean D, Pedersen BS, Talbert J, Powers J, Markin CR, Beckman KB, Lathrop M, Freed B, Langefeld CD, Schwartz DA. Genome-wide imputation study identifies novel HLA locus for pulmonary fibrosis and potential role for auto-immunity in fibrotic idiopathic interstitial pneumonia. BMC Genomic Data 2016, 17: 74. PMID: 27266705, PMCID: PMC4895966, DOI: 10.1186/s12863-016-0377-2.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedChromosomes, Human, Pair 6FemaleGene Expression ProfilingGene Expression RegulationGenetic LociGenetic Predisposition to DiseaseGenome-Wide Association StudyHLA-DQ beta-ChainsHLA-DRB1 ChainsHumansIdiopathic Pulmonary FibrosisLinkage DisequilibriumMaleMiddle AgedPulmonary FibrosisSequence Analysis, RNAConceptsRisk lociGenome-wide single nucleotide polymorphism (SNP) dataGenome-wide significant associationSingle nucleotide polymorphism dataGenome-wide genotypesRNA sequencing studiesNucleotide polymorphism dataTargeted gene expressionIdiopathic interstitial pneumoniaHigh linkage disequilibriumLung tissueGene regulationHLA allelesRNA sequencingPolymorphism dataRisk allelesGene expressionChromosome 6Protein structureInterstitial pneumoniaHLA regionSequencing studiesGenetic risk allelesAssociation analysisReplication genotyping
2014
Assessment of microRNA differential expression and detection in multiplexed small RNA sequencing data
Campbell JD, Liu G, Luo L, Xiao J, Gerrein J, Juan-Guardela B, Tedrow J, Alekseyev YO, Yang IV, Correll M, Geraci M, Quackenbush J, Sciurba F, Schwartz DA, Kaminski N, Johnson WE, Monti S, Spira A, Beane J, Lenburg ME. Assessment of microRNA differential expression and detection in multiplexed small RNA sequencing data. RNA 2014, 21: 164-171. PMID: 25519487, PMCID: PMC4338344, DOI: 10.1261/rna.046060.114.Peer-Reviewed Original Research