Featured Publications
G2S3: 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 sparsityCells
2021
Openness weighted association studies: leveraging personal genome information to prioritize non-coding variants
Song S, Shan N, Wang G, Yan X, Liu JS, Hou L. Openness weighted association studies: leveraging personal genome information to prioritize non-coding variants. Bioinformatics 2021, 37: 4737-4743. PMID: 34260700, PMCID: PMC8665759, DOI: 10.1093/bioinformatics/btab514.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesFunctional annotationGWAS signalsAssociation studiesComplex diseasesSpecific functional annotationsTissue-specific patternsNon-coding variationsPotential functional differencesDisease-relevant pathwaysPersonal genome informationGenome informationGenomic regionsComprehensive annotationHuman genomeChromosome accessibilityGenomic segmentsMore heritabilityNoncoding variantsPersonal genomesRelevant pathwaysNovel insightsFunctional differencesGenomeDisease mechanismsIntegrated 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 populations
2020
Approaches for integrating heterogeneous RNA-seq data reveal cross-talk between microbes and genes in asthmatic patients
Spakowicz D, Lou S, Barron B, Gomez JL, Li T, Liu Q, Grant N, Yan X, Hoyd R, Weinstock G, Chupp GL, Gerstein M. Approaches for integrating heterogeneous RNA-seq data reveal cross-talk between microbes and genes in asthmatic patients. Genome Biology 2020, 21: 150. PMID: 32571363, PMCID: PMC7310008, DOI: 10.1186/s13059-020-02033-z.Peer-Reviewed Original ResearchMeSH KeywordsAsthmaCase-Control StudiesComputational BiologyFemaleHumansMaleMicrobiotaMiddle AgedSequence Analysis, RNASputumUnsupervised Machine Learning