Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, MB, Canada.
Children's Hospital Research Institute of Manitoba, Winnipeg, MB, Canada.
Microbiome. 2021 Feb 10;9(1):41. doi: 10.1186/s40168-020-00998-4.
Quality control including assessment of batch variabilities and confirmation of repeatability and reproducibility are integral component of high throughput omics studies including microbiome research. Batch effects can mask true biological results and/or result in irreproducible conclusions and interpretations. Low biomass samples in microbiome research are prone to reagent contamination; yet, quality control procedures for low biomass samples in large-scale microbiome studies are not well established.
In this study, we have proposed a framework for an in-depth step-by-step approach to address this gap. The framework consists of three independent stages: (1) verification of sequencing accuracy by assessing technical repeatability and reproducibility of the results using mock communities and biological controls; (2) contaminant removal and batch variability correction by applying a two-tier strategy using statistical algorithms (e.g. decontam) followed by comparison of the data structure between batches; and (3) corroborating the repeatability and reproducibility of microbiome composition and downstream statistical analysis. Using this approach on the milk microbiota data from the CHILD Cohort generated in two batches (extracted and sequenced in 2016 and 2019), we were able to identify potential reagent contaminants that were missed with standard algorithms and substantially reduce contaminant-induced batch variability. Additionally, we confirmed the repeatability and reproducibility of our results in each batch before merging them for downstream analysis.
This study provides important insight to advance quality control efforts in low biomass microbiome research. Within-study quality control that takes advantage of the data structure (i.e. differential prevalence of contaminants between batches) would enhance the overall reliability and reproducibility of research in this field. Video abstract.
质量控制包括评估批次变异性以及确认重复性和再现性，这是高通量组学研究（包括微生物组研究）的一个组成部分。批次效应可能会掩盖真实的生物学结果，或者导致不可重复的结论和解释。微生物组研究中的低生物量样本容易受到试剂污染；然而，大规模微生物组研究中低生物量样本的质量控制程序尚未得到很好的建立。
在这项研究中，我们提出了一个框架，用于深入逐步解决这一差距。该框架由三个独立的阶段组成：（1）通过使用模拟群落和生物对照来评估结果的技术重复性和再现性，验证测序准确性；（2）通过应用统计算法（例如 decontam）去除污染物并纠正批次变异性，然后比较批次之间的数据结构；（3）验证微生物组组成和下游统计分析的重复性和再现性。我们使用这种方法对 CHILD 队列的牛奶微生物组数据进行了分析，该数据来自两个批次（2016 年和 2019 年提取和测序），我们能够识别出标准算法遗漏的潜在试剂污染物，并大大减少了污染物引起的批次变异性。此外，我们在将它们合并进行下游分析之前，在每个批次中确认了结果的重复性和再现性。
本研究为推进低生物量微生物组研究中的质量控制工作提供了重要的见解。利用数据结构（即批次之间污染物的差异流行率）进行的研究内质量控制将提高该领域研究的整体可靠性和再现性。
