bioconductor-miasim

---

name: bioconductor-miasim description: 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. when_to_use: Use when: miaSim; ATAC-seq chromatin accessibility (miaSim). Not for: Requires R ≥ 4.0.0 and Bioconductor ≥ 3.16 user-invocable: false

miaSim

Workflows

Standard Workflow

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.

When to Use

• Simulating microbiome species abundance matrices and time series under varying ecological assumptions.
• Modeling population dynamics using the generalized Lotka-Volterra model (simulateGLV) or its discrete version, the Ricker model (simulateRicker).
• Simulating neutral community dynamics with migration, births, and deaths using Hubbell's Neutral model (simulateHubbell).
• Generating species interaction matrices with specific distributional assumptions using powerlawA or randomA.

When NOT to Use

• For fitting ecological models to real-world experimental data (use parameter estimation packages instead).
• For simulating non-microbial networks that do not fit into the TreeSummarizedExperiment data container.

Data Requirements

• Input parameters such as interaction matrices generated by powerlawA or randomA.
• Outputs are structured as TreeSummarizedExperiment objects containing simulated abundance matrices.

Key Parameters

• n_species: Number of species to include in the simulation or interaction matrix.
• alpha: Power-law exponent parameter used in powerlawA.
• diagonal: Diagonal values of the interaction matrix in randomA.
• connectance: Connectance of the interaction matrix in randomA.
• carrying_capacity: Carrying capacity of the community in simulateHubbell and simulateSOI.
• migration_p: Migration probability in simulateHubbell and simulateHubbellRates.
• t_end: End time of the simulation.
• norm: Logical indicating whether to normalize the simulated abundances.

Best Practices

• Ensure the number of species specified in the interaction matrix matches the number of species used in the simulation models.
• Generate interaction matrices using powerlawA or randomA before running models like GLV, SOI, or Ricker.
• Use randomE to generate production and consumption rate matrices specifically for the consumer-resource model (simulateConsumerResource).

Common Pitfalls

• Specifying a different number of species in the interaction matrix than in the simulation model, which causes dimension mismatch errors. Ensure n_species matches the dimensions of A.

Alternatives

• micodymora: A Python package for microbiome simulation.

Citations

• Gao et al. 2023 (Case studies on microbiome simulation).

References

• Homepage: bioconductor.org/packages/miaSim
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/miaSim/inst/doc/vignette.html