Evaluating replicability in microbiome data.

Area of Science:

• Microbiology
• Bioinformatics
• Genomics

Background:

• High-throughput sequencing is a standard method for analyzing microbial communities.
• Laboratory protocols significantly influence microbiome sequencing data, impacting inter-community comparisons.
• The extent of cross-laboratory variability in microbiome data remains largely unquantified.

Purpose of the Study:

• To develop and apply a novel method for assessing data replicability in high-dimensional datasets.
• To evaluate the cross-laboratory replicability of microbial community signals using the Microbiome Quality Control Project dataset.
• To determine if distinctions observed within one lab hold true across different sequencing laboratories.

Main Methods:

• Proposed a novel approach to evaluate replicability in high-dimensional microbiome data.
• Utilized the Microbiome Quality Control Project dataset for cross-laboratory analysis.
• Assessed the consistency of sample distinctions across multiple laboratories using genus-level proportion data.

Main Results:

• Laboratories could consistently distinguish between samples internally (median 87% correct classification).
• These internal distinctions frequently failed to replicate across different laboratories (median 55% correct classification).
• Identical samples processed in different labs yielded substantially different quantitative results.

Conclusions:

• 16S sequencing does not reliably resolve differences in human microbiome samples due to cross-laboratory variability.
• The choice of laboratory protocol significantly impacts microbiome data interpretation.
• Certain data transformations can enhance replicability, informing future microbiome data analysis strategies.