Xiting Yan, PhD
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Appointments
Pulmonary, Critical Care & Sleep Medicine
Primary
Biostatistics
Secondary
Titles
Director of Data Analysis and Bioinformatics Hub, The Center for Precision Pulmonary Medicine (P2MED); Assistant Professor, Biostatistics
Contact Info
Appointments
Pulmonary, Critical Care & Sleep Medicine
Primary
Biostatistics
Secondary
Titles
Director of Data Analysis and Bioinformatics Hub, The Center for Precision Pulmonary Medicine (P2MED); Assistant Professor, Biostatistics
Contact Info
Appointments
Pulmonary, Critical Care & Sleep Medicine
Primary
Biostatistics
Secondary
Titles
Director of Data Analysis and Bioinformatics Hub, The Center for Precision Pulmonary Medicine (P2MED); Assistant Professor, Biostatistics
Contact Info
About
Titles
Associate Professor of Medicine (Pulmonary, Critical Care and Sleep Medicine)
Director of Data Analysis and Bioinformatics Hub, The Center for Precision Pulmonary Medicine (P2MED); Assistant Professor, BiostatisticsBiography
Dr. Yan received doctoral degrees in both applied statistics and computational biology and bioinformatics. She is interested in genetics, genomics, computational biology, biostatistics, system biology and bioinformatics. Her current research topics include (1) understanding disease heterogeneity and pathogenesis using large-scale omics data at both bulk and single cell resolution and (2) developing novel statistical and computational methods for analyses of different types of omics data and the integration of them with drug perturbation data for potential personalized treatment design.
Appointments
Pulmonary, Critical Care & Sleep Medicine
Associate Professor on TermPrimaryBiostatistics
Assistant ProfessorSecondary
Other Departments & Organizations
- Biostatistics
- Center for Biomedical Data Science
- Internal Medicine
- Kaminski Lab
- Pulmonary, Critical Care & Sleep Medicine
- The Center for Precision Pulmonary Medicine (P2MED)
- Yale School of Public Health
Education & Training
- Postdoctoral Associate
- Yale School of Medicine (2010)
- PhD
- University of Southern California, Biological Science Department/Computational Biology and Bioinformatics (2009)
- PhD
- Peking University, Department of Probability and Statistics, School of Mathematical Sciences/Applied Statistics (2006)
- BS
- Peking University, Department of Probability and Statistics, School of Mathematical Sciences/Probability and Statistics (2001)
Research
Overview
Analysis of one time-point microarray and longitudinal bulk RNA sequencing data from asthma patients;
Analysis of longitudinal microbiome sequencing data from children with cystic fibrosis;
Bulk RNA sequencing of IPF, A1AT and SARC patients using Ion Torrent technology;
Single cell RNA sequencing data analysis;
Spatial single cell RNA sequencing data analysis;
Medical Subject Headings (MeSH)
Biostatistics; Computational Biology; Genetics; Genomics; Lung Diseases; Molecular Medicine; Respiratory Hypersensitivity
Research at a Glance
Yale Co-Authors
Frequent collaborators of Xiting Yan's published research.
Publications Timeline
A big-picture view of Xiting Yan's research output by year.
Research Interests
Research topics Xiting Yan is interested in exploring.
Naftali Kaminski, MD
Jonas Christian Schupp, MD
Taylor Adams
Geoffrey Chupp, MD
Farida Ahangari, MD
Maor Sauler, MD
77Publications
3,055Citations
Computational Biology
Genomics
Lung Diseases
Publications
Featured Publications
Computational and Statistical Methods for Single-Cell RNA Sequencing Data
Wang Z, Yan X. Computational and Statistical Methods for Single-Cell RNA Sequencing Data. Springer Handbooks Of Computational Statistics 2022, 3-35. DOI: 10.1007/978-3-662-65902-1_1.ChaptersConceptsSingle-cell RNA sequencing technologySingle-cell RNA sequencing dataRNA sequencing technologyPhenotype of interestRNA sequencing dataDifferential expression analysisScRNA-seq dataStatistical methodsSequencing technologiesExpression analysisDropout imputationSequencing dataSeq dataDroplet-based technologiesDropout eventsDisease pathogenesisPopulation composition changesData normalizationHigh noise levelsPhenotypeNoise levelTherapeuticsComposition changesTranscriptomics 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 ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsWeighted 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 ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsSingle-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 sparsityCellsA novel pathway-based distance score enhances assessment of disease heterogeneity in gene expression
Yan X, Liang A, Gomez J, Cohn L, Zhao H, Chupp GL. A novel pathway-based distance score enhances assessment of disease heterogeneity in gene expression. BMC Bioinformatics 2017, 18: 309. PMID: 28637421, PMCID: PMC5480187, DOI: 10.1186/s12859-017-1727-4.Peer-Reviewed Original ResearchCitationsAltmetricNoninvasive 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 ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsHistory 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 signatureIntubationDetecting functional rare variants by collapsing and incorporating functional annotation in Genetic Analysis Workshop 17 mini-exome data
Yan X, Li L, Lee JS, Zheng W, Ferguson J, Zhao H. Detecting functional rare variants by collapsing and incorporating functional annotation in Genetic Analysis Workshop 17 mini-exome data. BMC Proceedings 2011, 5: s27. PMID: 22373324, PMCID: PMC3287862, DOI: 10.1186/1753-6561-5-s9-s27.Peer-Reviewed Original ResearchCitationsConceptsFunctional annotationCommon variantsDisease-associated common variantsGenetic Analysis Workshop 17 mini-exome dataGenetic Analysis Workshop 17 dataRare variantsFunctional rare variantsGenomic blocksSequencing technologiesAssociation studiesSynonymous variantsTag SNPsDifferent rare variantsAnnotationGenetic factorsRecent studiesVariantsVariant assumptionsHeritabilitySNPsCommon diseaseDisease riskRare exceptionsTesting 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 ResearchCitationsMeSH Keywords and ConceptsDetecting 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 ResearchCitations
2024
Single Cell Analysis Reveals Novel Immune Perturbations in Fibrotic Hypersensitivity Pneumonitis.
Zhao A, Unterman A, Abu Hussein N, Sharma P, Nekola F, Flint J, Yan X, Adams T, Justet A, Sumida T, Zhao J, Schupp J, Raredon M, Ahangari F, Deluliis G, Zhang Y, Buendia-Roldan I, Adegunsoye A, Sperling A, Prasse A, Ryu C, Herzog E, Selman M, Pardo A, Kaminski N. Single Cell Analysis Reveals Novel Immune Perturbations in Fibrotic Hypersensitivity Pneumonitis. American Journal Of Respiratory And Critical Care Medicine 2024 PMID: 38924775, DOI: 10.1164/rccm.202401-0078oc.Peer-Reviewed Original ResearchAltmetricConceptsFibrotic hypersensitivity pneumonitisIdiopathic pulmonary fibrosisPeripheral blood mononuclear cellsBronchoalveolar lavage cellsBlood mononuclear cellsClassical monocytesHypersensitivity pneumonitisPulmonary fibrosisT cellsImmune perturbationsLavage cellsMononuclear cellsCD8+ T cellsCytotoxic T cellsInterstitial lung diseaseHypersensitivity pneumonitis patientsCytotoxic CD4Immune aberrationsPneumonic patientsPneumonitisLung diseaseHealthy controlsImmune mechanismsPatient cellsSingle-cell transcriptomicssnRNAseq of IPF Stromal Cells Reveals Dysfunctional Gene Co-expression Profile
Sharma P, Taylor S A, Anderson S, Nekola F, Giuseppe D, Yan X, Schupp J, Balayev A, Justet A, Wuyts W, Vanaudenaerde B, Kaminski N. snRNAseq of IPF Stromal Cells Reveals Dysfunctional Gene Co-expression Profile. 2024, a4903-a4903. DOI: 10.1164/ajrccm-conference.2024.209.1_meetingabstracts.a4903.Peer-Reviewed Original Research
Academic Achievements and Community Involvement
activity Reviewer
Journal ServiceThe Journal of Allergy and Clinical ImmunologyDetails2016 - Presentactivity Reviewer
Journal ServiceBMC BioinformaticsDetails2009 - Presentactivity Reviewer
Journal ServiceBioinformaticsDetails2009 - Presentactivity A Hybrid Machine Learning and regression Method for Cell Type Deconvolution of Spatial Barcoding-based Transcriptomic data
Oral PresentationJSM 2023Details08/05/2023 - 08/10/2023Toronto, ON, CanadaSponsored by American Statistical AssociationCollaboratorsactivity Spatial Deconvolution Method Considering Platform Effect Removal, Sparsity and Spatial Information
Oral PresentationSpring 2023 Seminar Series of Department of Biomedical Informatics at Ohio State UniversityDetails04/07/2023 - 04/07/2023It's a presentation on zoom.Sponsored by Department of Biomedical Informatics, College of Medicine, The Ohio State University,
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B). Heatmap showing the clustering results by KEGG pathways using MCLUST. The color represents the clustering assignment of each sample by the KEGG pathways. C). Pathway based distance matrix among the clusters. The color of entry represents the pathway based distance between the corresponding two samples. Red represents a small distance (samples are strongly related) and white represents longer distance showing the strength of the clusters (samples are weakly related). Samples within TEA cluster 3 are the most strongly related and most homogeneous, followed by cluster 1 and 2, respectively.
News
- March 21, 2023
Department of Internal Medicine Promotions and Reappointments
- February 08, 2022
Scientists Apply High-resolution, Single-cell Profiling to Understand Immune Response in Severe COVID-19
- June 23, 2021
Despite the challenges of COVID-19, Yale-PCCSM section members continued their work on scientific papers
- September 17, 2020
Discoveries & Impact (September 2020)
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