2024
Local Clustering for Functional Data
Chen Y, Zhang Q, Ma S. Local Clustering for Functional Data. Journal Of Computational And Graphical Statistics 2024, 1-37. DOI: 10.1080/10618600.2024.2431057.Peer-Reviewed Original ResearchThe spike‐and‐slab quantile LASSO for robust variable selection in cancer genomics studies
Liu Y, Ren J, Ma S, Wu C. The spike‐and‐slab quantile LASSO for robust variable selection in cancer genomics studies. Statistics In Medicine 2024, 43: 4928-4983. PMID: 39260448, DOI: 10.1002/sim.10196.Peer-Reviewed Original ResearchAsymmetric Laplace distributionSpike-and-slab LASSORobust variable selection methodHeavy-tailed errorsRobust variable selectionHeavy-tailed distributionsAnalysis of high-dimensional genomic dataHigh-dimensional genomic dataExpectation-maximizationComprehensive simulation studyVariable selection methodsLaplace distributionCoordinate descent frameworkPosterior modeCancer genomics studiesRobust likelihoodVariable selectionSparsity patternSimulation studyComputational advantagesQuantile regressionNonrobust oneSelf-adaptationLoss functionGenomic studiesHigh-Dimensional Gene–Environment Interaction Analysis
Wu M, Li Y, Ma S. High-Dimensional Gene–Environment Interaction Analysis. Annual Review Of Statistics And Its Application 2024 DOI: 10.1146/annurev-statistics-112723-034315.Peer-Reviewed Original ResearchFixed- and random-effects analysisG-E interaction analysisG-E interactionsVariable selectionFrequentist analysisGene-environmentRandom effects analysisGeneral frameworkStatistical propertiesProgression of complex diseasesDimension reductionHypothesis testingG-EComplex diseasesGenetic factorsInteraction analysisNonlinear effect analysisStatistical perspectiveDisease outcomeEnvironmental factorsPrediction-basedEstimation-basedIncorporating prior information in gene expression network-based cancer heterogeneity analysis
Li R, Xu S, Li Y, Tang Z, Feng D, Cai J, Ma S. Incorporating prior information in gene expression network-based cancer heterogeneity analysis. Biostatistics 2024, kxae028. PMID: 39074174, DOI: 10.1093/biostatistics/kxae028.Peer-Reviewed Original ResearchCTHRC1+ fibroblasts and SPP1+ macrophages synergistically contribute to pro-tumorigenic tumor microenvironment in pancreatic ductal adenocarcinoma
Li E, Cheung H, Ma S. CTHRC1+ fibroblasts and SPP1+ macrophages synergistically contribute to pro-tumorigenic tumor microenvironment in pancreatic ductal adenocarcinoma. Scientific Reports 2024, 14: 17412. PMID: 39075108, PMCID: PMC11286765, DOI: 10.1038/s41598-024-68109-z.Peer-Reviewed Original ResearchConceptsPancreatic ductal adenocarcinomaTumor-associated macrophagesTumor microenvironmentEpithelial mesenchymal transitionDuctal adenocarcinomaImmune-suppressive tumor microenvironmentPro-tumorigenic tumor microenvironmentPancreatic cancer casesHeterogeneous tumor microenvironmentCombination of single-cellCancer-associated myofibroblastsSurgical resectionMyeloid cellsCurrent therapiesCancer casesLethal cancersSurvival rateExtracellular matrixTreat cancerMesenchymal transitionTherapeutic targetAdenocarcinomaCellular populationsCancerIntercellular interactionsEditorial
Ma S. Editorial. Briefings In Bioinformatics 2024, 25: bbae453. PMID: 39288229, PMCID: PMC11407437, DOI: 10.1093/bib/bbae453.Peer-Reviewed Original ResearchHEARTSVG: 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 dataA penalized integrative deep neural network for variable selection among multiple omics datasets
Li Y, Ren X, Yu H, Sun T, Ma S. A penalized integrative deep neural network for variable selection among multiple omics datasets. Quantitative Biology 2024, 12: 313-323. DOI: 10.1002/qub2.51.Peer-Reviewed Original ResearchOmics data analysisAvailability of omics dataMultiple omics datasetsGene expression datasetsAggregate multiple datasetsDeep neural networksOmics dataIntegrated deep neural networkOmics datasetsExpression datasetsMultiple datasetsDeep learningDiverse originsNeural networkOmicsAbstract Deep learningVariable selection resultsSample sizeVariable selectionIntegrated analysis frameworkCognitive statusOvarian cancer patientsModel interpretationExtensive simulation studyDatasetPartial Hepatectomy and Ablation for Survival of Early-Stage Hepatocellular Carcinoma Patients: A Bayesian Emulation Analysis
Wang J, Im Y, Wang R, Ma S. Partial Hepatectomy and Ablation for Survival of Early-Stage Hepatocellular Carcinoma Patients: A Bayesian Emulation Analysis. Life 2024, 14: 661. PMID: 38929645, PMCID: PMC11204969, DOI: 10.3390/life14060661.Peer-Reviewed Original ResearchOverall survivalHepatocellular carcinomaPartial hepatectomyTumor sizeAssociated with inferior overall survivalEarly-stage hepatocellular carcinomaEarly-stage HCC patientsInferior overall survivalHepatocellular carcinoma patientsAblation armCarcinoma patientsAblation therapyNo significant differenceTreatment regimensHCC patientsEmulated target trialSurgical proceduresEffect of ablationHepatectomyPatientsCompare treatment effectsClinical treatmentSignificant differencePropensity scoreLogistic regressionPrediction Consistency Regularization for Learning with Noise Labels Based on Contrastive Clustering
Sun X, Zhang S, Ma S. Prediction Consistency Regularization for Learning with Noise Labels Based on Contrastive Clustering. Entropy 2024, 26: 308. PMID: 38667864, PMCID: PMC11049179, DOI: 10.3390/e26040308.Peer-Reviewed Original ResearchLabel noiseContrastive clusteringConsistency regularizationRegularization termPrediction consistencyClassification accuracyImpact of label noiseEffects of label noiseClassification taskClustering problemComprehensive experimentsNoise labelsLabel informationNeural networkClustering resultsSample recognitionNoise rateMitigate noiseNoiseClassificationModel performanceRegularizationPrototypeAccuracyLabelingInformation‐incorporated sparse hierarchical cancer heterogeneity analysis
Han W, Zhang S, Ma S, Ren M. Information‐incorporated sparse hierarchical cancer heterogeneity analysis. Statistics In Medicine 2024, 43: 2280-2297. PMID: 38553996, DOI: 10.1002/sim.10071.Peer-Reviewed Original ResearchOrganochlorine pesticides and risk of papillary thyroid cancer in U.S. military personnel: a nested case-control study
Rusiecki J, McAdam J, Denic-Roberts H, Sjodin A, Davis M, Jones R, Hoang T, Ward M, Ma S, Zhang Y. Organochlorine pesticides and risk of papillary thyroid cancer in U.S. military personnel: a nested case-control study. Environmental Health 2024, 23: 28. PMID: 38504322, PMCID: PMC10949709, DOI: 10.1186/s12940-024-01068-0.Peer-Reviewed Original ResearchConceptsPapillary thyroid cancer riskNested case-control studyPapillary thyroid cancerCase-control studyOdds ratioBody mass index categoriesOrganochlorine pesticidesConfidence intervalsFindings of increased riskRisk of papillary thyroid cancerConditional logistic regressionPre-diagnostic serum samplesIndividually-matched controlsThyroid cancerFollicular variant papillary thyroid cancerSerum concentrationsVariant papillary thyroid cancerEffect modificationU.S. military personnelBirth cohortPapillary thyroid cancer casesBirth yearIndex categoriesDecreased riskEffects of organochlorine pesticidesA quantitative linguistic analysis of a cancer online health community with a smooth latent space model
Liu M, Fan X, Ma S. A quantitative linguistic analysis of a cancer online health community with a smooth latent space model. The Annals Of Applied Statistics 2024, 18 DOI: 10.1214/23-aoas1783.Peer-Reviewed Original ResearchEndocrine disrupting chemical mixture exposure and risk of papillary thyroid cancer in U.S. military personnel: A nested case-control study
Denic-Roberts H, McAdam J, Sjodin A, Davis M, Jones R, Ward M, Hoang T, Ma S, Zhang Y, Rusiecki J. Endocrine disrupting chemical mixture exposure and risk of papillary thyroid cancer in U.S. military personnel: A nested case-control study. The Science Of The Total Environment 2024, 922: 171342. PMID: 38428594, PMCID: PMC11034764, DOI: 10.1016/j.scitotenv.2024.171342.Peer-Reviewed Original ResearchConceptsPapillary thyroid cancer riskEndocrine disrupting chemicalsRisk of papillary thyroid cancerNested case-control studyPolychlorinated biphenylsOne-quartile increaseCase-control studyPapillary thyroid cancerOdds ratioWhite raceEvaluate associationsThyroid cancer riskBrominated flame retardantsEDC mixturesHistological subtypesChemical mixture exposureNon-statistically significant increaseBody mass indexQuantile g-computationOrganochlorine pesticidesSerum sampling timesCancer riskThyroid cancerU.S. military personnelStandard errorEstimation of multiple networks with common structures in heterogeneous subgroups
Qin X, Hu J, Ma S, Wu M. Estimation of multiple networks with common structures in heterogeneous subgroups. Journal Of Multivariate Analysis 2024, 202: 105298. PMID: 38433779, PMCID: PMC10907012, DOI: 10.1016/j.jmva.2024.105298.Peer-Reviewed Original ResearchGaussian graphical modelsMultiple networksGraphical modelsSparse regression problemHigh-dimensional data analysisNetwork estimationMinimax concave penaltyLarge-scale dataRegularized likelihoodJoint estimation approachRegression problemSelection consistency propertyConcave penaltyComplex dependence structureNetworkBreast cancer dataConsistency propertiesConvexity propertiesTCGA breast cancer dataNetwork identificationReparameterization techniqueEstimation approachDistributional assumptionsDependence structureBiological networks
2023
Hierarchical false discovery rate control for high-dimensional survival analysis with interactions
Liang W, Zhang Q, Ma S. Hierarchical false discovery rate control for high-dimensional survival analysis with interactions. Computational Statistics & Data Analysis 2023, 192: 107906. PMID: 38098875, PMCID: PMC10718515, DOI: 10.1016/j.csda.2023.107906.Peer-Reviewed Original ResearchFunctanSNP: an R package for functional analysis of dense SNP data (with interactions)
Ren R, Fang K, Zhang Q, Ma S. FunctanSNP: an R package for functional analysis of dense SNP data (with interactions). Bioinformatics 2023, 39: btad741. PMID: 38060266, PMCID: PMC10723032, DOI: 10.1093/bioinformatics/btad741.Peer-Reviewed Original ResearchEditorial
Ma S. Editorial. Briefings In Bioinformatics 2023, 24: bbad258. PMID: 37406189, DOI: 10.1093/bib/bbad258.Peer-Reviewed Original ResearchIdentifying Sex-Specific Cancer Metabolites and Associations to Prognosis
Shen X, Ma S, Khan S, Johnson C. Identifying Sex-Specific Cancer Metabolites and Associations to Prognosis. Learning Materials In Biosciences 2023, 271-299. DOI: 10.1007/978-3-031-44256-8_11.Peer-Reviewed Original ResearchMetabolomics dataSurvival analysisCancer metabolitesColorectal cancerCox proportional hazards regression analysisProportional hazards regression analysisHazards regression analysisSex-specific associationsClinical dataCancer patientsCancer prognosisCancerMetabolomicsMultiple comparisonsPrognosisAnalytical methodSurvivalOncology studiesPatientsAlternative analytical approachRegression analysisSex-specificMetabolitesSex interactionAssociation
2022
Heterogeneous Graphical Model for Non-Negative and Non-Gaussian PM2.5 data
Zhang J, Fan X, Li Y, Ma S. Heterogeneous Graphical Model for Non-Negative and Non-Gaussian PM2.5 data. Journal Of The Royal Statistical Society Series C (Applied Statistics) 2022, 71: 1303-1329. DOI: 10.1111/rssc.12575.Peer-Reviewed Original Research