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Automated Microbial Diagnostics

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A Method to Define the Effects of Environmental Enrichment on Colon Microbiome Biodiversity in a Mouse Colon Tumor Model

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MBECS: Microbiome Batch Effects Correction Suite.

Michael Olbrich^1,2,3, Axel Künstner^4,5, Hauke Busch⁶

¹Lübeck Institute for Experimental Dermatology, University of Lübeck, Lübeck, Germany. roland.olbrich@ku.ac.ae.

BMC Bioinformatics

|May 3, 2023

Summary

This summary is machine-generated.

Batch effects can skew microbiome data analysis. This study introduces a new R package that combines multiple batch effect correction algorithms and evaluation metrics for improved microbiome data quality.

Keywords:

Batch effects Bioconductor Microbiome R-package phyloseq

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Published on: July 31, 2019

Area of Science:

Microbiology
Bioinformatics
Computational Biology

Background:

Microbiome datasets are susceptible to batch effects, which can confound downstream analyses.
Existing batch effect correcting algorithms (BECA) often lack integrated evaluation metrics.
A unified approach is needed to address batch effects and assess correction performance in microbiome studies.

Purpose of the Study:

To develop a comprehensive software package for the R statistical environment.
To integrate multiple batch effect correction algorithms.
To incorporate robust evaluation metrics for assessing correction efficacy.

Main Methods:

Development of the Microbiome Batch Effects Correction Suite (MBECS) in R.
Integration of several established BECAs within the MBECS package.
Inclusion of diverse statistical metrics for evaluating the performance of different correction methods.

Main Results:

The MBECS package provides a unified platform for applying and comparing various BECAs.
The integrated evaluation metrics allow for objective assessment of batch effect removal.
The software facilitates improved reliability and reproducibility of microbiome data analysis.

Conclusions:

The Microbiome Batch Effects Correction Suite offers a valuable tool for researchers.
This integrated approach enhances the quality and interpretability of microbiome data.
MBECS addresses a critical need for combined correction and evaluation in microbiome bioinformatics.