Pixu Shi

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Publications

Statistical methods

• Tan, J., Shi, P., Zhang, A. R. (2026): Functional Singular Value Decomposition.
  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), 317
  
• Han, 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

• Shi, P. and Zhang, A. R. (2025): Calculating the upper bound of the Gini coefficient using grouped data and a case study of China.
  Science China Mathematics, 1-18.