Tao Wang, PhD
Assistant Professor Adjunct of BiostatisticsCards
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Research
Publications
2024
mbDriver: identifying driver microbes in microbial communities based on time-series microbiome data
Tan X, Xue F, Zhang C, Wang T. mbDriver: identifying driver microbes in microbial communities based on time-series microbiome data. Briefings In Bioinformatics 2024, 25: bbae580. PMID: 39526854, PMCID: PMC11551971, DOI: 10.1093/bib/bbae580.Peer-Reviewed Original ResearchmbDecoda: a debiased approach to compositional data analysis for microbiome surveys
Zong Y, Zhao H, Wang T. mbDecoda: a debiased approach to compositional data analysis for microbiome surveys. Briefings In Bioinformatics 2024, 25: bbae205. PMID: 38701410, PMCID: PMC11066923, DOI: 10.1093/bib/bbae205.Peer-Reviewed Original ResearchConceptsComposition of microbiomesCompositional biasMicrobiome datasetsMicrobiome studiesProbiotic microbesMicrobiome surveysMicrobiome compositionMicrobial loadMicrobiomeMaximum likelihood estimationAbundance analysisAbundance levelsAbsolute abundanceAbundance dataAbundanceClinical phenotypeFeature tableLikelihood estimation of model parametersEnvironmental factorsMaximum likelihood estimation of model parametersEstimation of model parametersMicrobesState-of-the-art methodsOver-dispersionState-of-the-art
2023
Data-driven slicing for dimension reduction in regressions: A likelihood-ratio approach
Xu P, Wang T, Zhu L. Data-driven slicing for dimension reduction in regressions: A likelihood-ratio approach. Science China Mathematics 2023, 67: 647-664. DOI: 10.1007/s11425-022-2088-x.Peer-Reviewed Original ResearchFactor Augmented Inverse Regression and its Application to Microbiome Data Analysis
Pang D, Zhao H, Wang T. Factor Augmented Inverse Regression and its Application to Microbiome Data Analysis. Journal Of The American Statistical Association 2023, 119: 1957-1967. DOI: 10.1080/01621459.2023.2231577.Peer-Reviewed Original ResearchInverse regressionCount vectorsLow-dimensional summariesAsymptotic propertiesVariational expectation-maximization algorithmExpectation-maximization algorithmGroup lassoVariational approximationApproximate inferenceSupplementary materialsCount dataModel selectionInformation criterionAbundance of featuresVectorPrediction of host phenotypeOverdispersionLatent factorsApproximationAuthor Correction: mbDenoise: microbiome data denoising using zero-inflated probabilistic principal components analysis
Zeng Y, Li J, Wei C, Zhao H, Wang T. Author Correction: mbDenoise: microbiome data denoising using zero-inflated probabilistic principal components analysis. Genome Biology 2023, 24: 84. PMID: 37085916, PMCID: PMC10120140, DOI: 10.1186/s13059-023-02940-x.Peer-Reviewed Original ResearchPGNneo: A Proteogenomics-Based Neoantigen Prediction Pipeline in Noncoding Regions
Tan X, Xu L, Jian X, Ouyang J, Hu B, Yang X, Wang T, Xie L. PGNneo: A Proteogenomics-Based Neoantigen Prediction Pipeline in Noncoding Regions. Cells 2023, 12: 782. PMID: 36899918, PMCID: PMC10000440, DOI: 10.3390/cells12050782.Peer-Reviewed Original Research
2022
phyloMDA: an R package for phylogeny-aware microbiome data analysis
Liu T, Zhou C, Wang H, Zhao H, Wang T. phyloMDA: an R package for phylogeny-aware microbiome data analysis. BMC Bioinformatics 2022, 23: 213. PMID: 35668363, PMCID: PMC9169257, DOI: 10.1186/s12859-022-04744-5.Peer-Reviewed Original ResearchConceptsHost-associated microbial communitiesShared evolutionary historyMicrobiome data analysisEvolutionary historyPhylogenetic informationPhylogenetic treeMicrobial communitiesR packageSequencing technologiesAbundance dataMicrobial compositionRelative abundanceMicrobiome dataSample sitesUser-friendly toolMultivariate abundance dataAbundanceUnique opportunityUnprecedented scaleDifferent patternsTreesmbDenoise: microbiome data denoising using zero-inflated probabilistic principal components analysis
Zeng Y, Li J, Wei C, Zhao H, Wang T. mbDenoise: microbiome data denoising using zero-inflated probabilistic principal components analysis. Genome Biology 2022, 23: 94. PMID: 35422001, PMCID: PMC9011970, DOI: 10.1186/s13059-022-02657-3.Peer-Reviewed Original ResearchA Zero-Inflated Logistic Normal Multinomial Model for Extracting Microbial Compositions
Zeng Y, Pang D, Zhao H, Wang T. A Zero-Inflated Logistic Normal Multinomial Model for Extracting Microbial Compositions. Journal Of The American Statistical Association 2022, 118: 2356-2369. DOI: 10.1080/01621459.2022.2044827.Peer-Reviewed Original ResearchMaximum likelihood estimationEfficient iterative algorithmProbabilistic PCA modelsEmpirical Bayes approachApproximation estimatorVariational approximationExcessive zerosM-estimationAsymptotic normalityIterative algorithmLikelihood estimationBayes approachCount dataHigh dimensionalityRaw count dataMultinomial modelExtensive simulationsZerosSupplementary materialMicrobiome dataCompositional natureEstimationPCA modelComposition estimationApproximationfastANCOM: a fast method for analysis of compositions of microbiomes
Zhou C, Wang H, Zhao H, Wang T. fastANCOM: a fast method for analysis of compositions of microbiomes. Bioinformatics 2022, 38: 2039-2041. PMID: 35134120, DOI: 10.1093/bioinformatics/btac060.Peer-Reviewed Original Research
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