Featured Publications
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 AnalysisSoftwareTranscriptomeG2S3: A gene graph-based imputation method for single-cell RNA sequencing data
Wu W, Liu Y, Dai Q, Yan X, Wang Z. G2S3: A gene graph-based imputation method for single-cell RNA sequencing data. PLOS Computational Biology 2021, 17: e1009029. PMID: 34003861, PMCID: PMC8189489, DOI: 10.1371/journal.pcbi.1009029.Peer-Reviewed Original ResearchMeSH KeywordsComputational BiologyDatasets as TopicGene Expression ProfilingHumansSequence Analysis, RNASingle-Cell AnalysisConceptsSingle-cell transcriptomic datasetsTranscriptomic datasetsGene expressionSingle-cell RNA sequencing technologySingle-cell transcriptomic studiesSingle-cell RNA sequencing dataRNA sequencing technologyRNA sequencing dataSingle-cell resolutionGene expression profilesAdjacent genesTranscriptomic studiesSequencing technologiesSequencing dataExpression profilesGene graphDownstream analysisGenesCell trajectoriesDropout eventsCell subtypesExpressionHigh data sparsityCellsNoninvasive Analysis of the Sputum Transcriptome Discriminates Clinical Phenotypes of Asthma
Yan X, Chu JH, Gomez J, Koenigs M, Holm C, He X, Perez MF, Zhao H, Mane S, Martinez FD, Ober C, Nicolae DL, Barnes KC, London SJ, Gilliland F, Weiss ST, Raby BA, Cohn L, Chupp GL. Noninvasive Analysis of the Sputum Transcriptome Discriminates Clinical Phenotypes of Asthma. American Journal Of Respiratory And Critical Care Medicine 2015, 191: 1116-1125. PMID: 25763605, PMCID: PMC4451618, DOI: 10.1164/rccm.201408-1440oc.Peer-Reviewed Original ResearchConceptsHistory of intubationNitric oxide levelsOxide levelsClinical phenotypeMost subjectsHigher bronchodilator responseNormal lung functionBlood of patientsCohort of childrenLogistic regression analysisSputum gene expressionBlood of childrenAirway transcriptomeMilder asthmaPathophysiologic heterogeneityPrebronchodilator FEV1Steroid requirementsLung functionBronchodilator responseGene expressionPhenotype of diseaseAsthmaBlood samplesGene signatureIntubationTesting gene set enrichment for subset of genes: Sub-GSE
Yan X, Sun F. Testing gene set enrichment for subset of genes: Sub-GSE. BMC Bioinformatics 2008, 9: 362. PMID: 18764941, PMCID: PMC2543030, DOI: 10.1186/1471-2105-9-362.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsDatabases, GeneticDatabases, ProteinGene Expression ProfilingInformation Storage and RetrievalProteomeSignal TransductionSoftwareDetecting differentially expressed genes by relative entropy
Yan X, Deng M, Fung K, Qian M. Detecting differentially expressed genes by relative entropy. Journal Of Theoretical Biology 2005, 234: 395-402. PMID: 15784273, DOI: 10.1016/j.jtbi.2004.11.039.Peer-Reviewed Original Research
2023
A 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
Single-cell multi-omics reveals dyssynchrony of the innate and adaptive immune system in progressive COVID-19
Unterman A, Sumida TS, Nouri N, Yan X, Zhao AY, Gasque V, Schupp JC, Asashima H, Liu Y, Cosme C, Deng W, Chen M, Raredon MSB, Hoehn KB, Wang G, Wang Z, DeIuliis G, Ravindra NG, Li N, Castaldi C, Wong P, Fournier J, Bermejo S, Sharma L, Casanovas-Massana A, Vogels CBF, Wyllie AL, Grubaugh ND, Melillo A, Meng H, Stein Y, Minasyan M, Mohanty S, Ruff WE, Cohen I, Raddassi K, Niklason L, Ko A, Montgomery R, Farhadian S, Iwasaki A, Shaw A, van Dijk D, Zhao H, Kleinstein S, Hafler D, Kaminski N, Dela Cruz C. Single-cell multi-omics reveals dyssynchrony of the innate and adaptive immune system in progressive COVID-19. Nature Communications 2022, 13: 440. PMID: 35064122, PMCID: PMC8782894, DOI: 10.1038/s41467-021-27716-4.Peer-Reviewed Original ResearchMeSH KeywordsAdaptive ImmunityAgedAntibodies, Monoclonal, HumanizedCD4-Positive T-LymphocytesCD8-Positive T-LymphocytesCells, CulturedCOVID-19COVID-19 Drug TreatmentFemaleGene Expression ProfilingGene Expression RegulationHumansImmunity, InnateMaleReceptors, Antigen, B-CellReceptors, Antigen, T-CellRNA-SeqSARS-CoV-2Single-Cell AnalysisConceptsProgressive COVID-19B cell clonesSingle-cell analysisT cellsImmune responseMulti-omics single-cell analysisCOVID-19Cell clonesAdaptive immune interactionsSevere COVID-19Dynamic immune responsesGene expressionSARS-CoV-2 virusAdaptive immune systemSomatic hypermutation frequenciesCellular effectsProtein markersEffector CD8Immune signaturesProgressive diseaseHypermutation frequencyProgressive courseClassical monocytesClonesImmune interactionsLung Microenvironments and Disease Progression in Fibrotic Hypersensitivity Pneumonitis.
De Sadeleer LJ, McDonough JE, Schupp JC, Yan X, Vanstapel A, Van Herck A, Everaerts S, Geudens V, Sacreas A, Goos T, Aelbrecht C, Nawrot TS, Martens DS, Schols D, Claes S, Verschakelen JA, Verbeken EK, Ackermann M, Decottignies A, Mahieu M, Hackett TL, Hogg JC, Vanaudenaerde BM, Verleden SE, Kaminski N, Wuyts WA. Lung Microenvironments and Disease Progression in Fibrotic Hypersensitivity Pneumonitis. American Journal Of Respiratory And Critical Care Medicine 2022, 205: 60-74. PMID: 34724391, PMCID: PMC8865586, DOI: 10.1164/rccm.202103-0569oc.Peer-Reviewed Original ResearchConceptsFibrotic hypersensitivity pneumonitisIdiopathic pulmonary fibrosisHypersensitivity pneumonitisLung zonesMolecular traitsUnused donor lungsInterstitial lung diseaseLocal disease extentProgression of fibrosisSevere fibrosis groupGene co-expression network analysisCo-expression network analysisExplant lungsDonor lungsLung involvementEndothelial functionLung findingsDisease extentPulmonary fibrosisLung diseaseFibrosis groupLung microenvironmentClinical behaviorDisease progressionBAL samples
2021
Integrated Single-Cell Atlas of Endothelial Cells of the Human Lung
Schupp JC, Adams TS, Cosme C, Raredon MSB, Yuan Y, Omote N, Poli S, Chioccioli M, Rose KA, Manning EP, Sauler M, DeIuliis G, Ahangari F, Neumark N, Habermann AC, Gutierrez AJ, Bui LT, Lafyatis R, Pierce RW, Meyer KB, Nawijn MC, Teichmann SA, Banovich NE, Kropski JA, Niklason LE, Pe’er D, Yan X, Homer RJ, Rosas IO, Kaminski N. Integrated Single-Cell Atlas of Endothelial Cells of the Human Lung. Circulation 2021, 144: 286-302. PMID: 34030460, PMCID: PMC8300155, DOI: 10.1161/circulationaha.120.052318.Peer-Reviewed Original ResearchConceptsDifferential expression analysisPrimary lung endothelial cellsLung endothelial cellsCell typesMarker genesExpression analysisSingle-cell RNA sequencing dataCross-species analysisVenous endothelial cellsEndothelial marker genesSingle-cell atlasMarker gene setsRNA sequencing dataEndothelial cellsSubsequent differential expression analysisDifferent lung cell typesResident cell typesLung cell typesCellular diversityEndothelial cell typesCapillary endothelial cellsHuman lung endothelial cellsPhenotypic diversityEndothelial diversityIndistinguishable populationsSingle-cell characterization of a model of poly I:C-stimulated peripheral blood mononuclear cells in severe asthma
Chen A, Diaz-Soto MP, Sanmamed MF, Adams T, Schupp JC, Gupta A, Britto C, Sauler M, Yan X, Liu Q, Nino G, Cruz CSD, Chupp GL, Gomez JL. Single-cell characterization of a model of poly I:C-stimulated peripheral blood mononuclear cells in severe asthma. Respiratory Research 2021, 22: 122. PMID: 33902571, PMCID: PMC8074196, DOI: 10.1186/s12931-021-01709-9.Peer-Reviewed Original ResearchConceptsPeripheral blood mononuclear cellsSevere asthmaEffector T cellsBlood mononuclear cellsT cellsHealthy controlsPoly IDendritic cellsMononuclear cellsUnstimulated peripheral blood mononuclear cellsInterferon responseTLR3 agonist poly IImpaired interferon responseMain cell subsetsNatural killer cellsPro-inflammatory profilePro-inflammatory pathwaysC stimulationCyTOF profilingHigh CD8Cell typesEffector cellsKiller cellsCell subsetsMain cell types
2020
Gene coexpression networks reveal novel molecular endotypes in alpha-1 antitrypsin deficiency
Chu JH, Zang W, Vukmirovic M, Yan X, Adams T, DeIuliis G, Hu B, Mihaljinec A, Schupp JC, Becich MJ, Hochheiser H, Gibson KF, Chen ES, Morris A, Leader JK, Wisniewski SR, Zhang Y, Sciurba FC, Collman RG, Sandhaus R, Herzog EL, Patterson KC, Sauler M, Strange C, Kaminski N. Gene coexpression networks reveal novel molecular endotypes in alpha-1 antitrypsin deficiency. Thorax 2020, 76: 134-143. PMID: 33303696, PMCID: PMC10794043, DOI: 10.1136/thoraxjnl-2019-214301.Peer-Reviewed Original ResearchConceptsWeighted gene co-expression network analysisAlpha-1 antitrypsin deficiencyGene modulesGene co-expression network analysisDifferential gene expression analysisCo-expression network analysisPeripheral blood mononuclear cellsGene expression patternsPBMC gene expression patternsGene coexpression networksAATD individualsGene expression profilesGene expression analysisBronchoalveolar lavageAugmentation therapyClinical variablesAntitrypsin deficiencyGene expression assaysRNA-seqCoexpression networkGene validationExpression analysisExpression assaysWGCNA modulesExpression patternsLatent-space embedding of expression data identifies gene signatures from sputum samples of asthmatic patients
Lou S, Li T, Spakowicz D, Yan X, Chupp GL, Gerstein M. Latent-space embedding of expression data identifies gene signatures from sputum samples of asthmatic patients. BMC Bioinformatics 2020, 21: 457. PMID: 33059594, PMCID: PMC7560063, DOI: 10.1186/s12859-020-03785-y.Peer-Reviewed Original Research
2019
Integrating multiomics longitudinal data to reconstruct networks underlying lung development
Ding J, Ahangari F, Espinoza CR, Chhabra D, Nicola T, Yan X, Lal CV, Hagood JS, Kaminski N, Bar-Joseph Z, Ambalavanan N. Integrating multiomics longitudinal data to reconstruct networks underlying lung development. American Journal Of Physiology - Lung Cellular And Molecular Physiology 2019, 317: l556-l568. PMID: 31432713, PMCID: PMC6879899, DOI: 10.1152/ajplung.00554.2018.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsAnimals, NewbornChildChild, PreschoolDNA MethylationEpigenesis, GeneticFemaleGene Expression ProfilingGene Expression Regulation, DevelopmentalGene Regulatory NetworksHigh-Throughput Nucleotide SequencingHumansImmunity, InnateInfantInfant, NewbornLungMaleMiceMice, Inbred C57BLMicroRNAsOrganogenesisProteomicsPulmonary AlveoliRNA, MessengerSingle-Cell AnalysisTranscriptomeConceptsSingle-cell RNA-seq dataLung developmentDynamic regulatory networksOmics data setsRNA-seq dataIndividual cell typesHuman lung developmentRegulatory networksDNA methylationLaser capture microdissectionEpigenetic changesExpression trajectoriesKey pathwaysCell typesActive pathwaysCapture microdissectionRegulatorKey eventsInnate immunityNew insightsSpecific key eventsPathwayComprehensive understandingProteomicsMethylation
2017
Characterisation of asthma subgroups associated with circulating YKL-40 levels
Gomez JL, Yan X, Holm CT, Grant N, Liu Q, Cohn L, Nezgovorova V, Meyers DA, Bleecker ER, Crisafi GM, Jarjour NN, Rogers L, Reibman J, Chupp GL. Characterisation of asthma subgroups associated with circulating YKL-40 levels. European Respiratory Journal 2017, 50: 1700800. PMID: 29025889, PMCID: PMC5967238, DOI: 10.1183/13993003.00800-2017.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAge of OnsetAirway ObstructionAsthmaChitinase-3-Like Protein 1Cluster AnalysisCross-Sectional StudiesDisease ProgressionFemaleGene Expression ProfilingHumansInflammationMaleMiddle AgedReproducibility of ResultsRespiratory SystemSeverity of Illness IndexSputumStatistics as TopicSymptom Flare UpConceptsSerum YKL-40 levelsYKL-40 levelsHigher serum YKL-40 levelsAirflow obstructionAsthma phenotypesElevated serum YKL-40 levelsSevere Asthma Research ProgramClinical asthma phenotypesExacerbation-prone asthmaIrreversible airway obstructionSevere airflow obstructionFrequent exacerbationsSevere exacerbationsAirway inflammationAirway obstructionAsthma exacerbationsAirway diseaseAsthma patientsAsthma severitySerum levelsInflammatory pathwaysAsthma subgroupsAdult onsetIdentification of individualsUnsupervised cluster analysisValidation of a 52-gene risk profile for outcome prediction in patients with idiopathic pulmonary fibrosis: an international, multicentre, cohort study
Herazo-Maya JD, Sun J, Molyneaux PL, Li Q, Villalba JA, Tzouvelekis A, Lynn H, Juan-Guardela BM, Risquez C, Osorio JC, Yan X, Michel G, Aurelien N, Lindell KO, Klesen MJ, Moffatt MF, Cookson WO, Zhang Y, Garcia JGN, Noth I, Prasse A, Bar-Joseph Z, Gibson KF, Zhao H, Herzog EL, Rosas IO, Maher TM, Kaminski N. Validation of a 52-gene risk profile for outcome prediction in patients with idiopathic pulmonary fibrosis: an international, multicentre, cohort study. The Lancet Respiratory Medicine 2017, 5: 857-868. PMID: 28942086, PMCID: PMC5677538, DOI: 10.1016/s2213-2600(17)30349-1.Peer-Reviewed Original ResearchMeSH KeywordsAgedCohort StudiesFemaleGene Expression ProfilingGenetic MarkersGenetic TestingHumansIdiopathic Pulmonary FibrosisLeukocytes, MononuclearLinear ModelsMaleMiddle AgedOligonucleotide Array Sequence AnalysisPrognosisProportional Hazards ModelsRisk AssessmentRisk FactorsTime FactorsVital CapacityConceptsIdiopathic pulmonary fibrosisTransplant-free survivalRisk profilePulmonary fibrosisAntifibrotic drugsPeripheral blood mononuclear cellsCox proportional hazards modelClinical prediction toolGroup of patientsBlood mononuclear cellsHigh-risk groupProportional hazards modelPulmonary Fibrosis FoundationPittsburgh cohortUntreated patientsCohort studyClinical courseIPF diagnosisBlood InstituteProspective studyVital capacityMononuclear cellsPeripheral bloodUS National InstitutesNational HeartIdentification 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
2007
Inferring activity changes of transcription factors by binding association with sorted expression profiles
Cheng C, Yan X, Sun F, Li LM. Inferring activity changes of transcription factors by binding association with sorted expression profiles. BMC Bioinformatics 2007, 8: 452. PMID: 18021409, PMCID: PMC2194743, DOI: 10.1186/1471-2105-8-452.Peer-Reviewed Original ResearchConceptsTranscription factorsExpression profilesMicroarray dataTarget gene selectionPost-transcriptional modificationsChIP-chip dataMicroarray expression profilesExpression differentiationLow expression levelsProfile of expressionTarget genesRegulatory mechanismsGene expressionBiological processesMicroarray studiesAffinity dataGene selectionSame machineryExpression levelsGenesActivity changesSignificance cutoffDifferentiationMeaningful hypothesesAffinity scores
2006
MARD: a new method to detect differential gene expression in treatment-control time courses
Cheng C, Ma X, Yan X, Sun F, Li LM. MARD: a new method to detect differential gene expression in treatment-control time courses. Bioinformatics 2006, 22: 2650-2657. PMID: 16928738, DOI: 10.1093/bioinformatics/btl451.Peer-Reviewed Original Research