2024
A mediation analysis framework based on variance component to remove genetic confounding effect
Dong Z, Zhao H, DeWan A. A mediation analysis framework based on variance component to remove genetic confounding effect. Journal Of Human Genetics 2024, 69: 301-309. PMID: 38528049, DOI: 10.1038/s10038-024-01232-x.Peer-Reviewed Original ResearchMediation analysis frameworkSingle nucleotide polymorphismsMediation analysisPleiotropic single nucleotide polymorphismsUK Biobank dataConfounding effectsTrait pairsBiobank dataIndividual-levelEpidemiological studiesCausal effectsGenetic signalsEstimated effectsLinear regressionNucleotide polymorphismsStandard errorData analysisGenetic correlationsPhenotypeIndirect effectsPleiotropyVariance componentsOutcomesRegression
2022
An unbiased kinship estimation method for genetic data analysis
Jiang W, Zhang X, Li S, Song S, Zhao H. An unbiased kinship estimation method for genetic data analysis. BMC Bioinformatics 2022, 23: 525. PMID: 36474154, PMCID: PMC9727941, DOI: 10.1186/s12859-022-05082-2.Peer-Reviewed Original ResearchConceptsRigorous mathematical proofGenetic data analysisReal data analysisUnbiased estimation methodEstimation methodIndividual-level genotype dataSample correlation coefficientMathematical proofMathematical derivationMean square errorCoefficient estimationMatrix methodEstimation accuracyEstimation biasHeritability estimationRoot mean square errorData analysisSquare errorAccurate estimatesEstimationUKINVariances of genotypesSpurious associationsKinship coefficientsEstimatesA Manifold Proximal Linear Method for Sparse Spectral Clustering with Application to Single-Cell RNA Sequencing Data Analysis
Wang Z, Liu B, Chen S, Ma S, Xue L, Zhao H. A Manifold Proximal Linear Method for Sparse Spectral Clustering with Application to Single-Cell RNA Sequencing Data Analysis. INFORMS Journal On Optimization 2022, 4: 200-214. DOI: 10.1287/ijoo.2021.0064.Peer-Reviewed Original ResearchSparse spectral clusteringOptimization problemSpectral clusteringLinear methodsIteration complexity resultsNonconvex objectiveNonsmooth objectiveConvex relaxationStiefel manifoldSingle-cell RNA sequencing data setsSSC problemComplexity resultsSmoothing techniquesRNA sequencing data analysisData setsOriginal formulationUnsupervised learning methodData analysisNonsmoothProblemAlgorithmFormulationManifoldClusteringConvergence
2017
Structured subcomposition selection in regression and its application to microbiome data analysis
Wang T, Zhao H. Structured subcomposition selection in regression and its application to microbiome data analysis. The Annals Of Applied Statistics 2017, 11: 771-791. DOI: 10.1214/16-aoas1017.Peer-Reviewed Original ResearchRegularization methodLinear log contrast modelGeneralized lasso problemLog-contrast modelNovel penalty functionMicrobiome data analysisCompositional covariatesOptimization problemLasso problemHigher dimensionsStatistical challengesPenalty functionPractical problemsSymmetric versionTree structure informationSubtree levelProblemPrior knowledgeTree structureSubcompositionsCompositional dataSuch dataStructure informationData analysisNodes
2011
Bayesian hierarchical modeling for signaling pathway inference from single cell interventional data
Luo R, Zhao H. Bayesian hierarchical modeling for signaling pathway inference from single cell interventional data. The Annals Of Applied Statistics 2011, 5: 725-745. PMID: 22162986, PMCID: PMC3233205, DOI: 10.1214/10-aoas425.Peer-Reviewed Original ResearchMarkov chain Monte CarloReal data analysisBayesian hierarchical modeling frameworkHierarchical modelingHierarchical modeling frameworkBayesian hierarchical modelingStatistical inferencePosterior distributionMonte CarloNetwork sparsitySimulation studyIntrinsic noiseModeling frameworkMeasurement errorInterventional dataInferenceMultiple protein activitiesPathway inferenceCarloModelingInterventional experimentsSuch dataSparsityData analysisNoise
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