Software


Github site


Microbiome analysis


  • CAT: conditional association testing for microbiome analysis

      Reference: Shi Y, Zhang L, Do K, Jenq RR, Peterson CB. (2024+) CAT: a conditional association test for microbiome data using a leave-out approach. [preprint]

  • TARO: tree-aggregated factor regression for microbiome data integration

      Reference: Mishra AK, Mahmud I, Lorenzi PL, Jenq RR, Wargo JA, Ajami NJ, Peterson CB. (2024+) TARO: tree-aggregated factor regression for microbiome data integration. [preprint]

  • aPCoA: covariate adjusted principal coordinates analysis

      Reference: Shi Y, Zhang L, Do K, Peterson CB†, Jenq RR†. (2020) aPCoA: covariate adjusted principal coordinates analysis. Bioinformatics. 36(13): 4099-4101. †Co-senior authors. [pdf] [Shiny app] [R package]

  • BAZE: Bayesian zero-constrained regression

      Reference: Zhang L, Shi Y, Jenq RR, Do K, Peterson CB. (2021) Bayesian compositional regression with structured priors for microbiome feature selection. Biometrics. 77(3): 824-838. [pdf]

  • TreeBH: Error control for tree-structured hypotheses

      Reference: Bogomolov M*, Peterson CB*, Benjamini Y, Sabatti C. (2021) Hypotheses on a tree: new error rates and testing strategies. Biometrika. 108(3): 575-590. *Authors contributed equally. [pdf]

  • ProgPerm: Assess robustness of microbiome discoveries

      Reference: Zhang L, Shi Y, Do K, Peterson CB†, Jenq RR†. (2021) ProgPermute: Progressive permutation for a dynamic representation of the robustness of microbiome discoveries. BMC Bioinformatics. 22:1-21. †Co-senior authors. [pdf] [Shiny app]

  • COZINE: Compositional zero-inflated network estimation

      Reference: Ha MJ, Kim J, Galloway-Pena J, Do KA, Peterson CB. (2020) Compositional zero-inflated network estimation for microbiome data. BMC Bioinformatics. 21:1-20. [pdf]


Graphical models


  • multiGGM: Bayesian inference of multiple graphical models

      Reference: Peterson CB, Stingo F, and Vannucci, M. (2015) Bayesian inference of multiple Gaussian graphical models. Journal of the American Statistical Association. 110(509): 159—174. [pdf]

  • Linked precision matrices: Estimation of networks with related edge values

      Reference: Peterson CB, Osborne N, Stingo FC, Bourgeat P, Doecke J, Vannucci M. (2020) Bayesian modeling of multiple structural connectivity networks during the progression of Alzheimer's disease. Biometrics. 76(4): 1120-1132. [pdf]

  • SINC: Simultaneous inference of networks and covariates

      Reference: Osborne N, Peterson CB, Vannucci M. (2022) Latent network estimation and variable selection for compositional data via variational EM. Journal of Computational and Graphical Statistics. 31(1): 163-175. [pdf]

  • bHUB: Identification of hub nodes across multiple networks

      Reference: Kim J, Do K, Ha MJ, Peterson CB. (2019) Bayesian inference of hub nodes across multiple networks. Biometrics. 75(1): 172-182. [pdf]

Other software


  • survivalContour: plot predicted survival or cumulative incidence

      Reference: Shi Y, Zhang L, Do K, Jenq RR, >Peterson CB. (2024+) survivalContour: Visualizing predicted survival via colored contour plots. [preprint] [Shiny app]

  • sBMI: Bayesian sparse modeling of censored safety data in meta-analysis

      Reference: Qi X, Zhou S, Wang Y, Peterson CB. (2022) Bayesian sparse modeling to identify high-risk subgroups in meta-analysis of safety data. Research Synthesis Methods. [pdf]

  • TreeQTL: Error control for single and multi-tissue eQTL analysis

      Reference: Peterson CB, Bogomolov M, Benjamini Y, Sabatti C. (2016) TreeQTL: hierarchical error control for eQTL findings. Bioinformatics. 32(16): 2556-2558. [pdf]