Statistical methods
Tan, J., Shi, P. , Zhang, A. R. (2026): Functional-SVD for Heterogeneous Trajectories: Case Studies in Health. Journal of the American Statistical Association , accepted.
Zhuang, H., Gai, X., Zhang, A. R., Ji, Z., Shi, P. (2026): Trajectory-guided dimension reduction for multi-sample single-cell RNA-seq data reveals sample-level heterogeneity. Bioinformatics , accepted.
Tan, J., Shi, P. (2026): Associating High-Dimensional Longitudinal Datasets through an Efficient Cross-Covariance Decomposition. arXiv preprint arXiv:2601.13405
Alam, M. S., Staicu, A., and Shi, P. (2024+): Supervised Low-Rank Approximation of High-Dimensional Multivariate Functional Data via Tensor Decomposition. arXiv
Shi, P. et al. (2024): Time-Informed Dimensionality Reduction for Longitudinal Microbiome Studies. Genome Biology , 25 (1), 317Han, R., Shi, P. and Zhang, A. R. (2024): Guaranteed Functional Tensor Singular Value Decomposition. Journal of the American Statistical Association , 119 (546), 995-1007.
Shi, P. , Zhou, Y. and Zhang, A. R. (2022): High-dimensional Log-Error-in-Variable Regression with Applications to Microbial Compositional Data Analysis. Biometrika , 109(2): 405-420.Lu, J. Shi, P. and Li, H. (2019): Generalized Linear Models with Linear Constraints for Microbiome Compositional Data. Biometrics , 75(1): 235-244.
Shi, P. and Li, H. (2017): A model for paired-multinomial data and its application to analysis of data on a taxonomic tree. Biometrics , 73(4): 1266-1278.Download the R codes
Shi, P. , Zhang, A. and Li, H. (2016): Regression analysis for microbiome compositional data. Annals of Applied Statistics , 10(2): 1019-1040. Download the Matlab codes
Fogarty, C. B., Shi, P. , Mikkelsen, M. E. and Small, D. S. (2014): Randomization inference and sensitivity analysis
for composite null hypotheses with binary
outcomes in matched observational studies. Journal of the American Statistical Association , 112(517): 321-331.
Lin, W., Shi, P. , Feng, R. and Li, H. (2014): Variable selection in regression with compositional covariates. Biometrika , 101(4): 785-797. Download the R codes
Microbiome and omics
McCann, J. R. et al. (2026): Branched chain amino acid metabolism and microbiome in adolescents with obesity during weight loss therapy. Journal of Clinical Investigation , accepted.
Kelly, M. S., Shi, P. , et al. (2025): Role of the upper airway microbiota in respiratory virus and bacterial pathobiont dynamics in the first year of life. Nature Communications , 16 (1), 1-13.
Kim, Y. J., McQuade, V. L., Shi, P. , et al. (2025): Glucagon-like peptide 1 receptor (Glp1r) deficiency does not appreciably alter airway inflammation or gut-lung microbiome axis in a mouse model of obese allergic airways disease and bariatric surgery. Journal of Asthma and Allergy , 285-305.
Serbanescu, M. A., et al. (2025): Impact of fiber-containing enteral nutrition on microbial community dynamics in critically ill trauma patients: a pilot-randomized trial. BMC Medicine , 23 (1), 706.
Seitz, V. A. et al. (2025): CaDAVEr: a metagenome-assembled genome catalog of microbial decomposers across vertebrate environments. Microbiology Resource Announcements , e01323-24.
Kennedy, L. B., et al. (2025): A comprehensive, multi-center, immunogenomic analysis of melanoma brain metastases. Acta Neuropathologica Communications , 13, 123.
Van Swearingen, A. E. D., et al. (2025): Genomic and immune profiling of breast cancer brain metastases. Acta Neuropathologica Communications , 13, 99.
Hudson, A., et al., Shi, P. , and Reeves, K. (2025): Longitudinal analysis of rhesus macaque metabolome during acute SIV infection reveals disruption in broad metabolite classes. Journal of Virology , accepted.
Burcham A. M. et al. (2024): A Conserved Interdomain Microbial Network Underpins Cadaver Decomposition Despite Environmental Variables Nature Microbiology , 9 (3), 595-613
Schweickart, A. et al. (2024): Serum and CSF Metabolomics Analysis Shows Mediterranean Ketogenic Diet Mitigates Risk Factors of Alzheimer's Disease npj Metabolic Health and Disease , 2(1), 15
SenNet Consortium (2022): NIH SenNet Consortium to map senescent cells throughout the human lifespan to understand physiological health. Nature Aging , 1-11. (Shi, P. among other authors as a member of
the SenNet consortium)
Other