View my complete list of publications here.
High-dimensional spatial data modeling with applications in spatial transcriptomics
14. X. Jiang#, L. Dong, B. Zhu#, S. Wang, Z. Wen, M. Chen, L. Xu, G. Xiao, and Q. Li*, "Bayesian reconstruction of single-cell-resolution spatial transcriptomics," The Annals of Applied Statistics, 2026+, in revision [bioRxiv] [GitHub]
13. L. Zhong#, B. Li, Z. Chi, S. Zhang, G. Xiao, and Q. Li*, "Systematic investigation of T cell entry regions in the tumor microenvironment," Nature Communications, 2026+, in revision [GitHub]
12. B. Zhu#, G. Hu, X. Fan, and Q. Li*, "Generalized Bayesian nonparametric clustering framework for high-dimensional spatial omics data," Journal of the American Statistical Association, 2026+, in revision [arXiv] [GitHub]
11. X. Jiang#, Y. Guo#, L. Guo, L, Zhong#, J. Wang, G. Xiao, Q. Li*, and L. Xu, "SpaFun: Discovering domain-specific spatial expression patterns and new disease-relevant genes using functional principal component analysis," Briefings in Bioinformatics, 2026, accepted [bioRxiv] [GitHub]
10. 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, 2026, accepted [bioRxiv]
9. C. Tang, Y. Zhou, X. Xiao, L. Dong, L. Yu, Q. Li, G. Xiao, and L. Xu, "3D reconstruction of spatial transcriptomics with spatial pattern enhanced graph convolutional neural network," Briefings in Bioinformatics, 2026, Volume 27, Issue 1, bbag060 [link] [GitHub]
8. B. Zhu#, A. Cassese, M. Guindani, and M. Vannucci, and Q. Li*, "BISON: Bi-clustering of spatial omics data with feature selection," Bioinformatics, 2025, Volume 41, Issue 9, btaf495 [link] [GitHub]
7. 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]
6. 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]
5. 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]
4. 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]
3. 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]
2. 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]
1. 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]
Statistical shape and spatial analysis
5. B. M. Brakefield#, H. Li#, B. Zhu#, K. W. Jin#, S. E. McKeown, and Q. Li*, "Bayesian clustering of n-gons," Bayesian Analysis, 2026+, in revision [GitHub]
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]
High-dimensional compositional data modeling with applications in microbiome
6. Q. Li*, S. Jiang#, K. C. Lutz#, A. Y. Koh, M. L. Neugent, N. J. De Nisco, and X. Zhan, "Bayesian modeling of metagenomic sequencing data for discovering microbial biomarkers in colorectal cancer," Bayesian Analysis, 2026+, in revision [arXiv] [GitHub]
5. 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, 2026+, in revision [link] [GitHub]
4. 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, Volume 41, Issue 7, btaf384 [link] [web app]
3. 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]
2. 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]
1. 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]
Temporal data modeling with application in epidemiology
3. T. Bedi#, Y. Guo, Y. Xu, and Q. Li*, "Bayesian analysis of growth curves for epidemiological longitudinal studies," Bayesian Analysis, 2025, accepted [link] [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
5. A. H. Beachum, X. Xiao, Y. Zhou, Q. Li, G. Xiao, and L. Xu , "Advances in predicting omics profiles from imaging data," Briefings in Bioinformatics, 2026, Volume 27, Issue 2, bbag090 [link]
4. 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]
3. 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]
2. 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]
1. 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]
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.