Featured Publications
Characterizing Spatiotemporal Transcriptome of the Human Brain Via Low-Rank Tensor Decomposition
Liu T, Yuan M, Zhao H. Characterizing Spatiotemporal Transcriptome of the Human Brain Via Low-Rank Tensor Decomposition. Statistics In Biosciences 2022, 14: 485-513. DOI: 10.1007/s12561-021-09331-5.Peer-Reviewed Original ResearchLow-rank tensor decompositionTensor decompositionPower iterationClassical principal component analysisStatistical performanceNumerical experimentsTensor unfoldingStatistical methodsGene expression dataEfficient algorithmData matrixExpression dataTensor principal componentsBrain expression dataPrincipal component analysisIterationDecompositionSpatiotemporal transcriptomeImplicit assumptionAlgorithmDynamicsTrajectoriesGuaranteesAssumptionSpatial patterns
2024
Estimating Cell-Type-Specific Gene Co-Expression Networks from Bulk Gene Expression Data with an Application to Alzheimer’s Disease
Su C, Zhang J, Zhao H. Estimating Cell-Type-Specific Gene Co-Expression Networks from Bulk Gene Expression Data with an Application to Alzheimer’s Disease. Journal Of The American Statistical Association 2024, 119: 811-824. PMID: 39280354, PMCID: PMC11394578, DOI: 10.1080/01621459.2023.2297467.Peer-Reviewed Original Research
2019
A statistical framework for cross-tissue transcriptome-wide association analysis
Hu Y, Li M, Lu Q, Weng H, Wang J, Zekavat SM, Yu Z, Li B, Gu J, Muchnik S, Shi Y, Kunkle BW, Mukherjee S, Natarajan P, Naj A, Kuzma A, Zhao Y, Crane PK, Lu H, Zhao H. A statistical framework for cross-tissue transcriptome-wide association analysis. Nature Genetics 2019, 51: 568-576. PMID: 30804563, PMCID: PMC6788740, DOI: 10.1038/s41588-019-0345-7.Peer-Reviewed Original ResearchConceptsTranscriptome-wide association analysisAssociation analysisGene-trait associationsGene expression dataGene expression levelsGenetic architectureComplex traitsMore genesGene expressionSingle tissueExpression dataAssociation resultsExpression levelsPowerful approachImputation modelHuman tissuesImputation accuracyGenotypesStatistical frameworkTissueGenesKey componentTraitsPowerful metricExpression
2006
A Misclassification Model for Inferring Transcriptional Regulatory Networks
Vannucci M, Sun N, Zhao H. A Misclassification Model for Inferring Transcriptional Regulatory Networks. 2006, 347-365. DOI: 10.1017/cbo9780511584589.019.Peer-Reviewed Original ResearchTranscriptional regulatory networksGene expression dataRegulatory networksExpression dataUnderlying transcriptional regulatory networksProtein-DNA binding dataNetwork reconstructionSet of proteinsYeast cell cycleMutual regulatory interactionsRegulatory network reconstructionGene regulationRegulatory interactionsSpecific genesCell cycleGenesBiological researchExpression levelsProteinTRNBinding dataHigh connectivityTransient stimulationRecent advancesStatistical framework
2000
Assessing reliability of gene clusters from gene expression data
Zhang K, Zhao H. Assessing reliability of gene clusters from gene expression data. Functional & Integrative Genomics 2000, 1: 156-173. PMID: 11793234, DOI: 10.1007/s101420000019.Peer-Reviewed Original ResearchConceptsStatistical resampling methodsHierarchical clustering methodCluster identification methodNumerical algorithmGene expression dataClustering methodClustering treesResampling methodHierarchical clustering algorithmExpression dataExperiment designClustering algorithmAlgorithmChallenging problemData setsMeasured gene expression levelsEffect of variationData analysisClustersUncertaintyProblemReliability