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
2019
Sparse principal component analysis with missing observations
Park S, Zhao H. Sparse principal component analysis with missing observations. The Annals Of Applied Statistics 2019, 13: 1016-1042. DOI: 10.1214/18-aoas1220.Peer-Reviewed Original ResearchHigh-dimensional settingsPrincipal subspaceStep estimation procedureRate of convergenceSparse principal component analysisDimensional settingSimulated examplesMissing observationsStatistical methodsEstimation procedureSparse PCA methodsSingle-cell dataSubspacePCA methodSingle-cell RNA-sequencing dataNumber of featuresCompetitive performancePrincipal component analysisConvergenceSample sizeEstimationWide rangeComponent analysis
2013
Application of Bayesian Sparse Factor Analysis Models in Bioinformatics
Ma H, Zhao H. Application of Bayesian Sparse Factor Analysis Models in Bioinformatics. 2013, 350-365. DOI: 10.1017/cbo9781139226448.018.Peer-Reviewed Original ResearchFactor analysis modelClassical factor analysis modelLatent variable modelStatistical methodsInferential methodsVariable modelComputational biologyLarge data setsGeometrical procedureObserved variablesCorrelated variablesAnalysis modelGeneral approachLatent variablesFactor modelingLatent factorsStrong prior beliefsUnderlying structureData setsPrincipal component analysisModelVariablesRegulatory networksLarge numberPrior beliefs