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Related Experiment Video

Updated: Nov 19, 2025

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing
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Effects of Rare Microbiome Taxa Filtering on Statistical Analysis.

Quy Cao1, Xinxin Sun2, Karun Rajesh3,4

  • 1Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Pennsylvania, PA, United States.

Frontiers in Microbiology
|January 29, 2021
PubMed
Summary

Filtering rare microbial taxa in microbiome studies reduces technical variability and preserves data integrity for more reproducible results. This approach aids in accurate disease state discrimination and enhances data analysis comparability across different labs.

Keywords:
contaminantsfast permutation testfilteringmicrobiomequality control

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Area of Science:

  • Microbiome analysis
  • Bioinformatics
  • Genomics

Background:

  • Microbial community detection in 16S rRNA and metagenomic studies is prone to contamination and sequencing errors, leading to inaccurate taxonomic identification.
  • Common strategies to address these issues include contaminant removal and filtering of rare features.
  • Filtering rare taxa reduces data sparsity and has shown promise in removing contaminants in controlled datasets, but its impact on downstream analysis is not well-reported.

Purpose of the Study:

  • To assess the effect of filtering rare features on alpha and beta diversity estimations in microbiome data.
  • To evaluate the impact of filtering on the identification of taxa that discriminate between disease states.
  • To compare filtering with contaminant removal methods for microbiome data preprocessing.

Main Methods:

  • Analysis of four datasets: two mock quality control datasets with known microbial composition and two disease study datasets.
  • Evaluation of filtering's effect on alpha and beta diversity metrics.
  • Application of DESeq2, LEfSe, and random forest models to identify differentially abundant taxa and assess classification ability (AUC) in disease studies.

Main Results:

  • In quality control datasets, filtering reduced alpha diversity differences and alleviated inter-laboratory technical variability while maintaining beta diversity.
  • In disease datasets, filtering retained significant taxa and preserved the classification ability of random forest models (AUC).
  • Filtering and contaminant removal methods demonstrated complementary effects, suggesting their combined use is beneficial.

Conclusions:

  • Filtering simplifies microbiome data complexity while preserving analytical integrity for downstream applications.
  • This approach mitigates sensitivity issues in classification methods and reduces technical variability, leading to more reproducible and comparable microbiome data analysis.
  • Combining filtering with contaminant removal is recommended for robust microbiome data preprocessing.