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Repeatability and reproducibility assessment in a large-scale population-based microbiota study: case study on human
Shirin Moossavi1,2,3,4,5, Kelsey Fehr6,7, Ehsan Khafipour8,9
1Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, MB, Canada. Shirin.moossavi@ucalgary.ca.
Microbiome
|February 11, 2021
Summary
A new framework enhances quality control for low biomass microbiome studies. It identifies contaminants and corrects batch effects, improving the reliability of microbiome research findings.
Area of Science:
- Microbiome Research
- Bioinformatics
- Quality Control
Background:
- High-throughput omics studies, including microbiome research, require robust quality control to assess batch variability and ensure reproducibility.
- Batch effects can obscure true biological signals and lead to erroneous conclusions in microbiome studies.
- Low biomass samples are susceptible to reagent contamination, and established quality control methods for these samples are lacking.
Purpose of the Study:
- To propose and validate a comprehensive framework for quality control in large-scale, low biomass microbiome studies.
- To address the gap in established quality control procedures for low biomass samples.
- To improve the reliability and reproducibility of microbiome research findings.
Main Methods:
- A three-stage framework was developed: (1) verifying sequencing accuracy using mock and biological controls, (2) removing contaminants and correcting batch effects with a two-tier strategy (statistical algorithms and batch comparison), and (3) corroborating repeatability and reproducibility.
- The framework was applied to milk microbiota data from the CHILD Cohort, generated in two distinct batches.
- A combination of statistical algorithms (e.g., decontam) and batch data structure comparison was employed for contaminant removal and batch effect correction.
Main Results:
- The proposed framework successfully identified potential reagent contaminants missed by standard algorithms.
- Substantial reduction in contaminant-induced batch variability was achieved.
- Repeatability and reproducibility of microbiome composition and downstream analyses were confirmed within each batch prior to data merging.
Conclusions:
- The study provides critical insights for advancing quality control in low biomass microbiome research.
- Integrating within-study quality control that leverages data structure, such as differential contaminant prevalence between batches, enhances research reliability.
- The developed framework offers a robust approach to mitigate challenges in low biomass microbiome studies.
