MIGsim

MIGsim provides tools for simulating microbiome data, enabling power analysis, methods benchmarking, and reliability assessment.

Key Features:

• Semisynthetic simulation: Creates simulation settings that mimic real high-throughput sequencing data while providing ground truth.
• Power analysis support: Enables estimation of statistical power for microbiome study designs.
• Methods benchmarking: Provides a sandbox to test and compare candidate analytical approaches.
• Reliability assessment: Supports evaluation of method performance and consistency.
• Case study implementations: Includes reproducible examples for differential abundance testing, dimensionality reduction, network analysis, and data integration.

Scientific Applications:

• Differential abundance testing: Simulation-based evaluation of methods to detect taxonomic differences between conditions.
• Dimensionality reduction: Benchmarking of ordination and visualization methods for microbiome data.
• Network analysis: Assessment of methods for inferring microbial association networks.
• Data integration: Testing approaches for combining multiple microbiome datasets or data types.

Methodology:

MIGsim implements probability, multivariate analysis, and regression concepts for microbiome data simulation, with trade-offs between generality, faithfulness, and controllability.