View my complete list of publications here.
High-dimensional spatial data modeling with applications in spatial transcriptomics
8. L. Zhong#, B. Li S. Zhang, Q. Li*, and G. Xiao, "Computational identification of migrating T cells in spatial transcriptomics data," Journal of Clinical Investigation Insight, 2025+, in revision [bioRxiv]
7. B. Zhu#, A. Cassese, M. Guindani, and M. Vannucci, and Q. Li*, "BISON: Bi-clustering of spatial omics data with feature selection," Bioinformatics, 2025+, in revision [arXiv]
6. B. Zhu#, G. Hu, L. Xu, X. Fan, and Q. Li*, "Bayesian nonparametric clustering with feature selection for spatially resolved transcriptomics data," The Annals of Applied Statistics, 2025, Volume 19, Number 2, pp.1028-1047 [link] [GitHub]
5. Y. Guo#, B. Zhu#, C. Tang, R. Rong, Y. Ma, G. Xiao, L. Xu, and Q. Li*, "BayeSMART: Bayesian clustering of multi-sample spatially resolved transcriptomics data," Briefings in Bioinformatics, 2024, Volume 25, Issue 6, bbae524 [link] [GitHub]
4. H. Li#, X. Jiang#, L. Guo, Y. Xie, L. Xu, and Q. Li*, "An interpretable Bayesian clustering approach with feature selection for analyzing spatially resolved transcriptomics data," Biometrics, 2024, Volume 80, Issue 3, ujae066 [link] [GitHub]
3. X. Jiang#, S. Wang, B. Zhu#, L. Guo, B. Zhu#, Z. Wen, L. Jia, L. Xu, G. Xiao, and Q. Li*, "iIMPACT: integrating image and molecular profiles for spatial transcriptomics analysis," Genome Biology, 2024, Volume 25, 147 [link] [GitHub]
2. X. Jiang#, G. Xiao, and Q. Li*, "A Bayesian modified Ising model for identifying spatially variable genes from spatial transcriptomics data," Statistics in Medicine, 2022, Volume 41, Issue 23, pp.4511-4743 [link] [GitHub]
1. Q. Li*, M. Zhang, Y. Xie, and G. Xiao, "Bayesian modeling of spatial molecular profiling data via Gaussian process," Bioinformatics, 2021, Volume 37, Issue 22, pp.4129–4136 [link] [GitHub]
Statistical shape and spatial analysis
4. C. Zhang#, C. Moon, Y. Xie, M. Chen, and Q. Li*, "Bayesian landmark-based shape analysis of tumor pathology images," Journal of the American Statistical Association, 2024, Volume 119, Issue 546, pp.798-810 [link] [GitHub]
3. C. Moon, Q. Li, and G. Xiao, "Using persistent homology topological features to characterize medical images: Case studies on lung and brain cancers," The Annals of Applied Statistics, 2023, Volume 17, Number 3, pp.2192-2211 [link] [GitHub]
2. Q. Li, X. Wang, F. Liang, and G. Xiao, "A Bayesian mark interaction model for analysis of tumor pathology images," The Annals of Applied Statistics, 2019, Volume 13, Number 3, pp.1708-1732 [link] [GitHub]
1. Q. Li, X. Wang, F. Liang, F. Yi, Y. Xie, A. Gazdar, and G. Xiao, "A Bayesian hidden Potts mixture model for analyzing lung cancer pathology images," Biostatistics, 2019, Volume 20, Issue 4, pp.565-581 [link] [GitHub]
Temporal data modeling with application in epidemiology
3. T. Bedi#, Y. Xu, and Q. Li* "Bayesian segmentation modeling of epidemic growth," Bayesian Analysis, 2025+, revision submitted [arXiv] [GitHub]
2. S. Jiang#, Q. Zhou, X. Zhan, and Q. Li*, "BayesSMILES: Bayesian segmentation modeling for longitudinal epidemiological studies," Journal of Data Science, 2021, Volume 19, Number 3, pp.365-389 [link] [GitHub] [web app]
1. Q. Li*, T. Bedi#, C. U. Lehmann, G. Xiao, and Y. Xie, "Evaluating short-term forecasting of COVID-19 cases among different epidemiological models under a Bayesian framework," GigaScience, 2021, Volume 10, Issue 2, giab009 [link] [GitHub] [web app]
High-dimensional data modeling with applications in omics
10. B. Zhu#, T. Bedi#, M. L. Neugent, K. C. Lutz#, N. J. De Nisco, and Q. Li*, "Bayesian modeling of co-occurrence microbial interaction networks," Journal of the Royal Statistical Society: Series C, 2025+, in revision [arXiv] [GitHub]
9. K. C. Lutz#, S. Yang, T. Bedi, M. L. Neugent, N. Madhavaram, B. Yao, X. Zhan, N. J. De Nisco, and Q. Li*, "MiCoDe: A web tool for performing microbiome community detection using a Bayesian weighted stochastic block model," Bioinformatics, 2025, btaf384 [link] [web app]
8. Y. Guo#, L. Yu, L. Guo, L. Xu, and Q. Li*, "A regularized Bayesian Dirichlet-multinomial regression model for integrating single-cell-level omics and patient-level clinical study data," Biometrics, 2025, Volume 81, Issue 1, ujaf005 [link] [GitHub]
7. K. C. Lutz#, M. L. Neugent, T. Bedi#, N. J. De Nisco, and Q. Li*, "A generalized Bayesian stochastic block model for microbiome community detection," Statistics in Medicine, 2025, Volume 44, Issue 3-4, e10291 [link] [GitHub]
6. F. Zhou, K. He, Q. Li, R. S. Chapkin, and Y. Ni, "Bayesian biclustering for microbial metagenomic sequencing data via multinomial matrix factorization," Biostatistics, 2022, Volume 23, Issue 3, pp.891–909 [link]
5. S. Jiang#, G. Xiao, A. Y. Koh, Q. Li*, and X. Zhan, "A Bayesian zero-inflated negative binomial regression model for the integrative analysis of microbiome data," Biostatistics, 2021, Volume 22, Issue 3, pp.522-540 [link] [GitHub]
4. G. Jia, X. Wang, Q. Li, W. Lu, X. Tang, I. Wistuba, and Y. Xie, "RCRnorm: An integrated system of random-coefficient hierarchical regression models for normalizing NanoString nCounter data," The Annals of Applied Statistics, 2019, Volume 13, Number 3, pp.1617-1647 [link]
3. Q. Li, A. Cassese, M. Guindani, and M. Vannucci, "Bayesian negative binomial mixture regression models for the analysis of sequence count and methylation data," Biometrics, 2018, Volume 75, Issue 1, pp.183-192 [link] [GitHub]
2. L. Cai, Q. Li, Y. Du, J. Yun, Y. Xie, R. J. DeBerardinis, and G. Xiao, "Genomic regression analysis of coordinated expression," Nature Communications, 2017, Volume 8, Number 2187 [link] [web app]
1. Q. Li, M. Guindani, B. J. Reich, H. D. Bondell, and M. Vannucci, "A Bayesian mixture model for clustering and selection of feature occurrence rates under mean constraints," Statistical Analysis and Data Mining, 2017, Volume 10, Issue 6, pp.393-409 [link] [GitHub]
Sequence analysis
3. Q. Li*, D. B. Dahl, M. Vannucci, H. Joo, and J. W. Tsai , "KScons: A Bayesian approach for protein residue contact prediction using the knob-socket model of protein tertiary structure," Bioinformatics, 2016, Volume 32, Issue 24, pp.3774-3781 [link]
2. T. Liang, X. Fan, Q. Li, and S. -Y. R. Li , "Detection of short dispersed tandem repeats by reversible jump Markov chain Monte Carlo," Nucleic Acids Research, 2012, Volume 40, Issue 19, pp. e147 [link]
1. Q. Li, X. Fan, T. Liang, and S. -Y. R. Li , "An Markov chain Monte Carlo algorithm for detecting short adjacent repeats shared by multiple sequences," Bioinformatics, 2011, Volume 27, Issue 13, pp.1772-1779 [link]
Meta analysis
1. J. Yang#, Q. Li, S. Shin, "Sparse and heterogeneous meta-analysis with semiparametric models," Biometrics, 2025+, in revision.
10. T. Ebrahimzadeh, K. C. Lutz#, U. Basu, J. Gadhvi, J. V. Komarovsky, Q. Li, P. Zimmern, and N. J. De Nisco, "Inflammatory markers for improved recurrent urinary tract infection diagnosis in women," Life Science Alliance, 2024, Volume 7, Number 4, e202302323 [link]
9. M. L. Neugent, A. Kumar, N. V. Hulyalkar, K. C. Lutz#, V. H. Nguyen, J. Fuentes, C. Zhang#, A. Nguyen, B. M. Sharon, A. Kuprasertkul, A. P. Arute, T. Ebrahimzadeh, N. Natesan, C. Xing, V. Shulaev, Q. Li, P. E. Zimmern, K. L. Palmer, and N. J. De Nisco, "Recurrent urinary tract infection and estrogen shape the taxonomic ecology and functional potential of the postmenopausal urobiome," Cell Report Medicine, 2022, Volume 3, Issue 10, 100753 [link]
8. S. Yang, S. Wang, Y. Wang, R. Rong, J. Kim, B. Li, A. Y. Koh, G. Xiao, Q. Li, D. Liu, and X. Zhan, "MB-SupCon: Microbiome-based predictive models via supervised contrastive learning," Journal of Molecular Biology, 2022, Volume 434, Issue 15, 167693 [link]
7. A. Czysz, B. L. Mason, Q. Li, C. Chin-Fatt, A. Minhajuddin, T. Carmody, and M. H. Trivedi, "Comparison of inflammatory markers as moderators of depression outcomes: A COMED study," Journal of Affective Disorders, 2021, Volume 295, pp.1066-1071 [link]
6. T. Ebrahimzadeh, A. Kuprasertkul, M. L. Neugent, K. C. Lutz#, J. Fuentes, J. Gadhvi, F. Khan, C. Zhang#, B. Sharon, K. Orth, Q. Li, P. Zimmern, and N. J. De Nisco, "Urinary prostaglandin E2 is a biomarker for recurrent urinary tract infection in postmenopausal women," Life Science Alliance, 2021, Volume 4, Number 7, e202000948 [link]
5. R. Rong, S. Jiang#, L. Xu, G. Xiao, Y. Xie, D. Liu, Q. Li, and X. Zhan, "MB-GAN: Microbiome simulation via generative adversarial network," GigaScience, 2021, Volume 10, Issue 2, giab005 [link] [GitHub]
4. M. Zhang, T. Sheffield, X. Zhan, Q. Li, D. M. Yang, Y. Wang, S. Wang, Y. Xie, T. Wang, and G. Xiao, "Spatial molecular profiling: Platforms, applications and analysis tools," Briefings in Bioinformatics, 2021, Volume 22, Issue 3, bbaa145 [link]
3. L. Zhang, R. Rong, Q. Li^, D. M. Yang, B. Yao, D. Luo, X. Zhang, X. Zhu, J. Luo, Y. Liu, X. Yang, X. Ji, Y. Xie, Y. Sha, Z. Li, and G. Xiao, "A deep learning-based model for screening and staging pneumoconiosis," Scientific Reports, 2021, Volume 11, Number 1, 2201 [link]
2. B. L. Mason, Q. Li, A. Minhajuddin, A. H. Czysz, L. A. Coughlin, S. Hussain, A. Y. Koh, and M. H. Trivedi, "Reduced anti-inflammatory gut microbiota are associated with depression and anhedonia," Journal of Affective Disorders, 2020, Volume 266, pp.394-401 [link]
1. Y. Li, M. Chen, Q. Li, and W. Zhang, "Enabling multi-level trust in privacy preserving data mining," IEEE Transaction on Knowledge and Data Engineering, 2012, Volume 24, Issue 9, pp. 1598-1612 [link]