microeco

microeco provides a workflow for statistical analysis and visualization of microbiome omics data in R.

Key Features:

• Multi-omics data support: The package supports amplicon sequencing data, metagenomic sequencing data, and nontargeted metabolomics data.
• Amplicon sequencing analysis: Analysis involves data preprocessing and normalization, core taxa identification, alpha diversity, beta diversity, differential abundance testing, and machine learning.
• Normalization flexibility: Different normalized data produced by various methods are selected for subsequent analysis of each part based on best analytical practices.
• Differential abundance evaluation: Parametric community simulation is adopted to enable performance evaluation of various testing approaches.
• Metagenomic data handling: Focus is on how bioinformatic analysis data are read and preprocessed, which refers to the major usage differences from amplicon sequencing data.
• Metabolomics analysis: Demonstrates differential test, machine learning, and association analysis with microbial abundances.
• Integrated pipeline construction: Extensively combines different types of methods to build an analysis pipeline for complex analyses.

Scientific Applications:

• Microbiome statistical analysis: Statistical analysis and visualization of microbiome data across amplicon sequencing, metagenomic sequencing, and metabolomics data types.
• Alpha and beta diversity analysis: Diversity analysis of amplicon sequencing data following preprocessing and normalization.
• Differential abundance testing: Identification of differentially abundant taxa with performance evaluation through parametric community simulation.
• Machine learning classification: Machine learning approaches applied to amplicon sequencing and metabolomics data.
• Multi-omics association: Association analysis between metabolomics data and microbial abundances.

Methodology:

The workflow involves data preprocessing and normalization, with different normalization methods selected for subsequent analyses based on best practices. Parametric community simulation is used for evaluating differential abundance testing approaches. The protocol combines different method types into analysis pipelines.