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A workflow for statistical analysis and visualization of microbiome omics data using the R microeco package.

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This study introduces an R package protocol for microbiome data analysis, covering amplicon, metagenomic, and metabolomics data. It provides a comprehensive and scalable pipeline for statistical analysis and visualization, simplifying complex omics data challenges.

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • Microbiome research faces challenges due to complex experimental designs and diverse omics data types.
  • Statistical analysis and visualization of large microbiome datasets require robust and adaptable methods.

Purpose of the Study:

  • To present a step-by-step protocol using the R microeco package for comprehensive microbiome data analysis and visualization.
  • To demonstrate the application of the protocol across various omics data types, including amplicon sequencing, metagenomics, and metabolomics.

Main Methods:

  • Utilized the R microeco package for a standardized pipeline.
  • Included data preprocessing, normalization, diversity analysis (alpha and beta), differential abundance testing, and machine learning.
  • Employed parametric community simulation for evaluating differential abundance testing methods.

Main Results:

  • The protocol effectively handles amplicon, metagenomic, and metabolomics data analysis.
  • Demonstrated comprehensive analysis scenarios, including differential testing, machine learning, and association analysis.
  • The R code is scalable and runs efficiently on standard hardware.

Conclusions:

  • The microeco package provides a comprehensive, scalable, and efficient protocol for microbiome data analysis.
  • This protocol addresses the growing complexity and data volume in microbiome research, facilitating robust statistical analysis and visualization.