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 ResearchTranscriptomics of bronchoalveolar lavage cells identifies new molecular endotypes of sarcoidosis
Vukmirovic M, Yan X, Gibson KF, Gulati M, Schupp JC, DeIuliis G, Adams TS, Hu B, Mihaljinec A, Woolard TN, Lynn H, Emeagwali N, Herzog EL, Chen ES, Morris A, Leader JK, Zhang Y, Garcia JGN, Maier LA, Collman RG, Drake WP, Becich MJ, Hochheiser H, Wisniewski SR, Benos PV, Moller DR, Prasse A, Koth LL, Kaminski N. Transcriptomics of bronchoalveolar lavage cells identifies new molecular endotypes of sarcoidosis. European Respiratory Journal 2021, 58: 2002950. PMID: 34083402, PMCID: PMC9759791, DOI: 10.1183/13993003.02950-2020.Peer-Reviewed Original ResearchConceptsWeighted gene co-expression network analysisGene co-expression network analysisCo-expression network analysisGene expression programsGene expression patternsDistinct transcriptional programsImmune response pathwaysIon Torrent ProtonMicroarray expression datasetsExpression programsTranscriptional programsPhenotypic traitsGene modulesResponse pathwaysRNA sequencingMolecular endotypesExpression patternsGene expressionHilar lymphadenopathyOrgan involvementGenomic researchMechanistic targetExpression datasetsT helper type 1T cell immune responsesG2S3: 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 ResearchConceptsSingle-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 signatureIntubation
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 interactions
2008
Genomic Androgen Receptor-Occupied Regions with Different Functions, Defined by Histone Acetylation, Coregulators and Transcriptional Capacity
Jia L, Berman BP, Jariwala U, Yan X, Cogan JP, Walters A, Chen T, Buchanan G, Frenkel B, Coetzee GA. Genomic Androgen Receptor-Occupied Regions with Different Functions, Defined by Histone Acetylation, Coregulators and Transcriptional Capacity. PLOS ONE 2008, 3: e3645. PMID: 18997859, PMCID: PMC2577007, DOI: 10.1371/journal.pone.0003645.Peer-Reviewed Original ResearchConceptsTranscription factorsChromatin immunoprecipitation-microarray analysisTarget gene expression levelsSame transcription factorHuman genomic DNAGene expression levelsChromatin structureSuch genesKnockout experimentsHistone acetylationTarget genesEnhancer activityTranscriptional capacityDNA locationsGenomic androgen receptorsGene expressionGenomic DNAMicroarray analysisProstate cancer cellsDifferential regulationGenesLuciferase reporterDiverse mechanismsHigh-throughput elucidationContinuous stretch
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