Research

Journal Articles

• Zhang, S., Shen, Y., Chen, I. A. and Lee, J. (2023+) Bayesian Modeling of Interaction between Features in Sparse Multivariate Count Data with Application to Microbiome Study. The Annals of Applied Statistics, in press.

Keywords: Covariance Matrix, Differential Abundance, Factor Model, Joint Sparsity, Multivariate Count Data, Rounded Kernel Model, Zero Inflation.

• Lee, J., Thall, P. F. and Msaouel, P. (2023) Bayesian Treatment Screening and Selection Using Subgroup-Specific Utilities of Response and Toxicity. Biometrics, 79 (3), 2458-2473

KEYWORDS: Bayesian design, clustering, patient prognostic subgroups, treatment screening design, utility function

• Lee, J., Thall, P. F., Lim, B. and Msaouel, P. (2022) Utility Based Bayesian Personalized Treatment Selection for Advanced Breast Cancer. Journal of the Royal Statistical Society: Series C (Applied Statistics), 71 (5), 1605–1622

KEYWORDS: Bayesian nonparametrics, dependent Dirichlet process, multivariate probit regression, precision medicine, statistical decision making, utility function

• Msaouel, P., Lee, J., Karam, J. A., Thall, P. F. (2022) A Causal Framework for Making Individualized Treatment Decisions in Oncology. Cancers, 14(16), p.3923

Keywords: adjuvant therapy; causal diagrams; individualized inferences; patient-specific decision-making; precision medicine; prognostic biomarkers; predictive biomarkers

• Lee, J., Thall, P. F. and Msaouel, P. (2021) Precision Bayesian Phase I-II Dose-Finding Based on Utilities Tailored to Prognostic Subgroups. Statistics in Medicine, 40 (24), 5199-5217

KEYWORDS: adaptive randomization, Bayesian phase I-II clinical trial design, clustering, dose finding, patient prognostic subgroups

• Msaouel, P., Lee, J., and Thall, P. F. (2021) Making Patient-Specific Treatment Decisions Using Prognostic Variables. Cancers, 13 (11): 2741

Keywords: individualized inferences; patient-specific decision-making; precision medicine; prognostic biomarkers; utilities

• Dentro, S.C et al (2021) Characterizing Genetic Intra-tumor Heterogeneity across 2,658 Human Cancer Genomes. Cell, 184 (8) 2239–2254

Keywords: whole-genome sequencing, pan-cancer genomics, intra-tumor heterogeneity, cancer driver genes, cancer evolution, tumor phylogeny, subclonal reconstruction, branching evolution

• Shuler, K., Verbanic, S., Chen, I. A. and Lee, J. (2021) A Bayesian Nonparametric Analysis for Zero Inflated Multivariate Count Data with Application to Microbiome Study. Journal of the Royal Statistical Society: Series C (Applied Statistics), 70 (4), 961-979

KEYWORDS: Bayesian nonparametrics, dependent Dirichlet process, high- throughput sequencing, microbiome, multivariate count, normalization, operational taxonomic unit, zero inflation

• Lee, J., Thall, P. F. and Msaouel, P. (2020) A Phase I-II Design Based On Periodic and Continuous Monitoring of Disease Status and Times to Toxicity and Death. Statistics in Medicine, 39 (15), 2035-2050

KEYWORDS: adaptive randomization, Bayesian design, dose finding, interim response, mixed hazard, phase I-II clinical trial

• Verbanic, S., Shen, Y., Lee, J., Deacon, J. M. and Chen, I. A. (2020) Microbial Predictors of Healing and Short-term Effect of Debridement on the Microbiome of Chronic Wounds. NPJ Biofilms and Microbiomes, 6 (20)

• Shuler, K., Sison-Mangus, M. and Lee, J. (2020) Bayesian Sparse Multivariate Regression with Asymmetric Nonlocal Priors for Microbiome Data Analysis. Bayesian Analysis, 15 (2), 559-578

Keywords: count data , harmful algal bloom, microbiome, negative binomial, next-generation sequencing, nonlocal prior, stochastic search variable selection 

• Lee, J., Thall, P.F. and Lin, S.K. (2019) Bayesian Semiparametric Joint Regression Analysis of Recurrent Adverse Events and Survival in Esophageal Cancer Patients. The Annals of Applied Statistics, 13 (1), 221-247

Keywords: Accelerated failure time , Bayesian nonparametrics, chemoradiation, Dirichlet process, esophageal cancer, joint model , nonhomogeneous point process 

• Lee, J., Thall, P.F. and Rezvani, K. (2019) Optimizing Natural Killer Cell Doses for Heterogeneous Cancer Patients Based on Multiple Event Times. Journal of the Royal Statistical Society: Series C (Applied Statistics), 69, Part 2, 461-474

Keywords: Cellular therapy; Dose finding; Natural killer cells; Phase I–II clinical trial; Precision medicine

• Lee, J. and Sison-Mangus, M. (2018) A Bayesian Semiparametric Regression Model for Joint Analysis of Microbiome Data. Frontiers in Microbiology, 9:522. doi: 10.3389/fmicb.2018.00522

Keywords: count data, Laplace prior, metagenomics, microbiome, regularizing prior, process convolution, negative binomial model, 16S ribosomal RNA sequencing  

• Lee, J., Muller, P., Sengupta, S., Ji, Y. and Gulukota, K. (2016) Bayesian Inference for Intra-Tumor Heterogeneity in Mutations and Copy Number Variation. Journal of the Royal Statistical Society: Series C (Applied Statistics), 65 (4), 547–563

Keywords:  Categorical Indian buffet process; Feature allocation models; Markov chain Monte Carlo methods; Next generation sequencing; Random matrices; Subclone; Variant calling

• Lee, J., Thall, P. F., Ji, Y. and Muller, P. (2016) A Decision-Theoretic Phase I-II Design for Ordinal Outcomes in Two Cycles. Biostatistics, 17 (2), 304–319

Keywords: Adaptive design; Bayesian design; Decision theory; Dynamic treatment regime; Latent probit model; Ordinal outcomes; Phase I–II clinical trial.

• Lee, J., Muller, P., Ji, Y. and Gulukota, K. (2015) A Bayesian Feature Allocation Model for Tumor Heterogeneity. The Annals of Applied Statistics, 9 (2), 621–639

Keywords: feature allocation models, Haplotypes, Indian buffet process, Markov chain Monte Carlo, next-generation sequencing, random binary matrices, variant calling 

• Lee, J., Thall, P. F., Ji, Y. and Muller, P. (2015) Bayesian Adaptive Dose Selection in Multiple Treatment Cycles Based on the Joint Utility of Toxicity and Efficacy. Journal of the American Statistics Association, 110 (510), 711–722

Keywords: Adaptive design, Bayesian design, Dynamic treatment regime, Latent probit model, Phase I-II clinical trial, Q-learning

• Lee, J. and MacEachern, S. N. (2014) Inference Functions in High Dimensional Bayesian Inference. Statistics and Its Interface, 7 (4), 477-486

Keywords: nonparametric Bayes, Dirichlet process, loss function

• Lee, J., MacEachern, S. N., Lu, Y. and Mills, G. B. (2014) Local-Mass Preserving Prior Distribution for Nonparameteric Bayesian Models. Bayesian Analysis, 9 (2), 307–330

Keywords: clustering, Dirichlet process, local mass, nonparametric Bayes, prior misspecification, Two-parameter Poisson-Dirichlet process 

• Lee, J., Ji, Y., Liang, S., Cai, G. and Muller, P. (2013) Bayesian Hierarchical Model for Differential Gene Expression Using RNA-seq Data. Statistics in Biosciences, 7 (1), 48–67

Keywords: Bayes, Differential gene expression, FDR, Mixture models, Next-generation sequencing

• Xu, Y., Lee, J., Yuan, Y., Mitra, R., Shoudan, L., Muller, P. and Ji, Y. (2013) Nonparametric Bayesian Bi-Clustering for Next–Generation Sequencing Count Data. Bayesian Analysis, 8 (4), 759–780

Keywords: Bi-Clustering, ChIP-seq, histone modifications, Markov chain Monte Carlo, nonparametric Bayes 

• Lee, J., Muller, P., Zhu, Y. and Ji, Y. (2013) A Nonparametric Bayesian Model for Local Clustering with Application to Proteomics. Journal of the American Statistics Association, 108 (503), 775–778

KEY WORDS: Dirichlet process, Pólya urn, Protein expression, Random partitions, RPPA

• Cai, G., Li, H. Lu, Y., Lee, J., Mu ̈ller, P., Ji, Y. and Liang, S. (2012) Accuracy of RNA-Seq and Its Dependence on Sequencing Depth. BMC Bioinformatics, 13 (Suppl 13)

• Hans, C., Allenby, G. M., Craigmile, P. F., Lee, J., MacEachern, S. N. and Xu, X. (2012) Covariance Decompositions for Accurate Computation in Bayesian Scale-Usage Model. Journal of Computational and Graphical Statistics, 21 (2), 538-557

Key words: Approximate transition kernel, Convergence rate, Data augmentation, Limiting distribution, MCMC, Multivariate ordinal probit, Truncated multivariate normal

• Lee, J., Ji, Y., Liang, S., Cai, G. and Muller, P. (2011) On Differential Gene Expression Using RNA- seq Data. Cancer Informatics, 10, 205-215

Keywords: clustering, false discovery rate, mixture models, next-generation sequencing

• Lee, J. and MacEachern, S. N. (2011) Consistency of Bayes Estimators without the Assumption that the Model is Correct. Journal of Statistical Planning and Inference, 141 (2), 748-757

Keywords: Bayes, Consistency, Incorrect model specification

• Bush, C., Lee, J. and MacEachern, S. N. (2010) Minimally Informative Prior Distributions for Nonparametric Bayesian Analysis. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72 (2), 253-268

Keywords: Bayes; Dirichlet process; Improper prior; Local mass; Mixed modes analysis; Reference prior

Critical Reviews Written for Others

• MacEachern, S. N. and Lee, J. (2022) Discussion on “Evaluating Sensitivity to the Stick-Breaking Prior in Bayesian Nonparametrics” by Giordano, Liu, Jordan and Broderick. Bayesian Analysis (to appear)

• Review “Statistical Methods in Biology.” S.J. Welham, S.A. Gezan, S.J. Clark and A. Mead (CRC Press) in Journal of Agricultural, Biological, and Environmental Statistics (2015)

• Lee, J. (2014) Discussion on “Robust Bayesian Graphical Modeling Using Dirichlet t-Distributions” by Michael Finegold and Mathias Drton. Bayesian Analysis 9 (3), 572-573.

Papers in Conference Proceedings

• Sengupta, S., Guluokta, K., Lee, J., Mueller, P., and Ji, Y. (2015) Bayclone: Bayesian Non- parametric Inference of Tumor Subclones Using NGS Data. In Proceedings of The Pacific Symposium on Biocomputing (PSB) 2015, 20:467-478

Contributions to Books

• Lee, J., Mueller, P., Sengupta, S., Gulukota, K., and Ji, Y. (2016) “Bayesian Feature Allocation Models for Tumor Heterogeneity.” in Statistical Analysis for High-Dimensional Data, A. Frigessi et al. (eds), Abel Symposia, Springer

• Lee, J., Mueller, P., Zhu, Y. and Ji, Y. (2015) “A Nonparametric Bayesian Model for Nested Clustering.” in Statistical Planning and Analysis in Proteomics, Jung K.(eds), Springer

• Ji, Y., Sengupta, S., Lee, J., Mueller, P., and Gulutoka, K. (2015). “Estimating latent cell sub- populations with Bayesian feature allocation models.” in Nonparametric Bayesian Methods in Biostatistics and Bioinformatics, Mitra, R. and Mueller, P. (eds), Springer-Verlag.  

Other Publications

• BayClone2: Bayesian Feature Allocation Model for Tumor Heterogeneity (R package) released in December, 2014