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 ResearchG2S3: 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 sparsityCells
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 Research
2017
Identification 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
2011
Collapsing-based and kernel-based single-gene analyses applied to Genetic Analysis Workshop 17 mini-exome data
Li L, Zheng W, Lee JS, Zhang X, Ferguson J, Yan X, Zhao H. Collapsing-based and kernel-based single-gene analyses applied to Genetic Analysis Workshop 17 mini-exome data. BMC Proceedings 2011, 5: s117. PMID: 22373309, PMCID: PMC3287841, DOI: 10.1186/1753-6561-5-s9-s117.Peer-Reviewed Original ResearchMinor allele frequencyCausal variantsGenetic Analysis Workshop 17 mini-exome dataSingle gene analysisLow minor allele frequencyGAW17 data setRare variantsFalse positive genesFunctional annotationPhenotypic varianceTop genesGenotypic dataNoncausal variantsAssociation dataGenetic effectsAssociation TestUnrelated individualsAllele frequenciesGenesSame genotype distributionBayesian mixed-effects modelsVariantsSimilar genotype distributionGAW17Traits
2008
Nonlinear cooperation of p53-ING1-induced bax expression and protein S-nitrosylation in GSNO-induced thymocyte apoptosis: a quantitative approach with cross-platform validation
Duan S, Wan L, Fu WJ, Pan H, Ding Q, Chen C, Han P, Zhu X, Du L, Liu H, Chen Y, Liu X, Yan X, Deng M, Qian M. Nonlinear cooperation of p53-ING1-induced bax expression and protein S-nitrosylation in GSNO-induced thymocyte apoptosis: a quantitative approach with cross-platform validation. Apoptosis 2008, 14: 236. PMID: 19082896, DOI: 10.1007/s10495-008-0288-4.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAnimalsApoptosisBcl-2-Associated X ProteinDexamethasoneGene DosageGene Expression RegulationInhibitor of Growth Protein 1Intracellular Signaling Peptides and ProteinsMiceModels, BiologicalNeural Networks, ComputerNonlinear DynamicsNuclear ProteinsOligonucleotide Array Sequence AnalysisProtein BindingReproducibility of ResultsReverse Transcriptase Polymerase Chain ReactionS-NitrosoglutathioneThymus GlandTumor Suppressor Protein p53Tumor Suppressor ProteinsGenomic 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