HEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics
Yuan X, Ma Y, Gao R, Cui S, Wang Y, Fa B, Ma S, Wei T, Ma S, Yu Z. HEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics. Nature Communications 2024, 15: 5700. PMID: 38972896, PMCID: PMC11228050, DOI: 10.1038/s41467-024-49846-1.Peer-Reviewed Original ResearchConceptsSpatially variable genesVariable genesSpatial expression patternsSpatial transcriptomics technologiesSpatial transcriptomics researchTranscriptome researchTranscriptomic technologiesBiological functionsExpression patternsSpatial transcriptomicsGenesState-of-the-art methodsColorectal cancer dataSingle-cell biclustering for cell-specific transcriptomic perturbation detection in AD progression
Gong Y, Xu J, Wu M, Gao R, Sun J, Yu Z, Zhang Y. Single-cell biclustering for cell-specific transcriptomic perturbation detection in AD progression. Cell Reports Methods 2024, 4: 100742. PMID: 38554701, PMCID: PMC11045878, DOI: 10.1016/j.crmeth.2024.100742.Peer-Reviewed Original ResearchConceptsSnRNA-seq dataGene modulesAD progressionPathogenesis of Alzheimer's diseaseBiologically interpretable resultsSingle-cell data analysisGene regulatory changesFunctional gene modulesGene coexpression patternsAlzheimer's diseaseSingle-cell levelSnRNA-seqBiclustering methodsPolygenic diseaseBatch effectsDropout eventsCoexpression patternsNetwork biomarkersCell typesBiclusteringCellsGenesScRNABiologyComparative analysisPredicting long-term progression of Alzheimer’s disease using a multimodal deep learning model incorporating interaction effects
Wang Y, Gao R, Wei T, Johnston L, Yuan X, Zhang Y, Yu Z. Predicting long-term progression of Alzheimer’s disease using a multimodal deep learning model incorporating interaction effects. Journal Of Translational Medicine 2024, 22: 265. PMID: 38468358, PMCID: PMC10926590, DOI: 10.1186/s12967-024-05025-w.Peer-Reviewed Original ResearchConceptsAlzheimer's diseaseGenetic polymorphism dataProgression of Alzheimer's diseaseMild cognitive impairmentPolymorphism dataAlzheimer's Disease Neuroimaging InitiativeAD progressionArea under the receiver operating characteristic curvePrediction of AD progressionDeep learning modelsADNI-1AlzheimerPatient careLearning modelsMCI to ADInteraction effectsADNI-3Increase prediction accuracyMild cognitive impairment to ADEarly interventionCognitive impairmentClinical assessmentA generalized calibrated Bayesian hierarchical modeling approach to basket trials with multiple endpoints
Chi X, Yuan Y, Yu Z, Lin R. A generalized calibrated Bayesian hierarchical modeling approach to basket trials with multiple endpoints. Biometrical Journal 2024, 66: e2300122. PMID: 38368277, PMCID: PMC11323483, DOI: 10.1002/bimj.202300122.Peer-Reviewed Original ResearchConceptsBayesian hierarchical modeling approachShrinkage parameterTheoretical propertiesPhase II basket trialBasket trialsSimulation studyHierarchical modelHierarchical modeling approachFunctional formGeneral hierarchical modelEfficacy endpointLatent variable approachApproach yieldsRisk-benefit profileVariable approachImmunotherapy agentsTumor responseGeneralizationTargeted therapyCancer subtypesMultiple cancer subtypesTreatment effectsMonitoring procedures