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Updated: May 22, 2025

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

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Published on: August 25, 2018

Composite quantile regression approach to batch effect correction in microbiome data.

Jiwon Park¹, Taesung Park²

¹Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Republic of Korea.

Frontiers in Microbiology

|March 12, 2025

Summary

This summary is machine-generated.

This study introduces a novel method to correct for systematic and nonsystematic batch effects in biological data. The approach effectively removes unwanted variations, ensuring more accurate downstream analysis of microbiome datasets.

Keywords:

batch effect composite quantile regression microbiome over-dispersion zero inflation

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Published on: July 31, 2019

Area of Science:

Microbiome research
Bioinformatics
Statistical modeling

Background:

Batch effects are non-biological variations in experimental data.
These effects can distort biological signals and compromise analysis integrity.

Purpose of the Study:

To develop a comprehensive approach for correcting both systematic and nonsystematic batch effects.
To improve the accuracy and reliability of biological data analysis.

Main Methods:

Applied negative binomial regression for systematic batch effects.
Utilized composite quantile regression for nonsystematic batch effects.
Employed Kruskal-Walis test for reference batch selection and OTU-level adjustment.

Main Results:

The new model effectively corrected batch effects across diverse microbiome datasets.
Performance was validated using PERMANOVA R-squared, PCoA plots, and Average Silhouette Coefficient.
Successfully applied to human microbiome data.

Conclusions:

The proposed method offers a robust solution for batch effect correction in microbiome studies.
Enhances the reliability of downstream analyses by preserving true biological variations.