Examples

Jupyter Notebook Examples by Model

Consumer Resource Model (CRM)

• Bayesian inference to infer the parameters of a Consumer Resource model
• Read in simulated data
• Infer parameter c only
• Simulate some time course data from the CRM
  • Generate parameters for model with two species
  • Simulate single time course

Gaussian Processes (GP)

• Imputing Missing Values using Gaussian Process
• Using the Data Imputator on a Real Public Dataset - Stein et al. (2013)

Multivariate Autoregressive Model (MVAR)

• MVAR Simulation for Microbiota and Metabolites
  • MVAR Process
  • Objective
  • Simulation with VARsim.py for sVAR Models
  • Example Usage of VARsim.py for MVAR Simulation
• MVAR Simulation for Microbiota and Metabolites
  • MVAR Process
  • Objective
  • Simulation with VARsim.py for sVAR Models
  • Example Usage of VARsim.py for MVAR Simulation
  • Visualization

Multi-Model Analysis

• VAR inference on a Real Public Dataset, Herold et al. (2020)
• Sparse VAR on a Real Public Dataset, Herold et al. (2020)
• Data imputation on a Real Public Dataset, Herold et al. (2020)
  • Figure 3 - Community Structure and Function Dynamics:
  • Figure 4 - Metabolite Levels and Environmental Dynamics:

Vector Autoregression (VAR)

• Bayesian Inference for Vector AutoRegression (VAR) Models
  • Introduction to Bayesian VAR
  • Notebook Structure
  • Objective
  • Example Usage
• Vector AutoRegression (VAR) Simulation
  • Introduction
  • Notebook Structure
  • Objective

Generalized Lotka-Volterra (gLV)

• Rutter & Dekker et al 2024 analysis
  • Data Preparation
  • Perform Bayesian inference without shrinkage
• Repeat Stein et al. 2013 analysis
• Used Bayesian inference to infer the parameters of a (linearised) gLV model
  • Bayesian inference with no shrinkage
  • Bayesian inference with shrinkage: Horseshoe prior
  • Bayesian inference with shrinkage and a perturbation with unknown interactions
• Example Lasso gLV model
• Simulate some time course data and perform ridge regression as in Stein et al. 2013
  • Single time course
  • Multiple time course
  • Multiple time course including perturbations
• Simulate some time course data from the gLV
  • Model with five species
  • Simulate single time course
  • Simulate single time course with a perturbation
  • Simulate multiple time courses
  • Simulate multiple time courses with a perturbation

Generalized Metabolic Lotka-Volterra (gMLV)

• Run ridge regression and lasso for the gMLV model
  • Five species, six metabolites, single time course
  • Perform ridge regression to get the abundance model
  • Perform lasso regression to obtain interactions between X and S
• Simulate some time course data from the gMLV
  • Five species, six metabolites, single time course
  • Five species, single time course, with a perturbation