Microbiome time series simulation with generalized Lotka-Volterra model, Self-Organized Instability (SOI), and other models. Hubbell's Neutral model is used to determine the abundance matrix. The resulting abundance matrix is applied to (Tree)SummarizedExperiment objects.
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library(miaSim)
# 1. Generate species interaction matrix
A_normal <- powerlawA(n_species = 4, alpha = 3)
# 2. Simulate time series using generalized Lotka-Volterra (gLV) model
tse_glv <- simulateGLV(
n_species = 4,
A = A_normal,
t_start = 0,
t_store = 1000,
stochastic = FALSE,
norm = FALSE
)
# 3. Simulate community using Hubbell Neutral model
tse_hubbell <- simulateHubbell(
n_species = 8,
M = 10,
carrying_capacity = 1000,
k_events = 50,
migration_p = 0.02,
t_end = 100
)
Input/Output Note: Inputs are ecological parameters (species count, interaction matrices, carrying capacity); outputs are simulated abundance matrices stored in TreeSummarizedExperiment objects.
simulateGLV) or its discrete version, the Ricker model (simulateRicker).simulateHubbell).powerlawA or randomA.TreeSummarizedExperiment data container.powerlawA or randomA.TreeSummarizedExperiment objects containing simulated abundance matrices.powerlawA.randomA.randomA.simulateHubbell and simulateSOI.simulateHubbell and simulateHubbellRates.powerlawA or randomA before running models like GLV, SOI, or Ricker.randomE to generate production and consumption rate matrices specifically for the consumer-resource model (simulateConsumerResource).n_species matches the dimensions of A.micodymora: A Python package for microbiome simulation.No reviews yet. Tell others what you thought.
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