Correcting for batch effects in case-control microbiome studies

• Published in: PLoS computational biology Vol. 14; no. 4; p. e1006102
• Main Authors: Gibbons, Sean M, Duvallet, Claire, Alm, Eric J
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 23-04-2018; Public Library of Science (PLoS)
• Subjects: Artificial intelligence; Bioengineering; Biology and Life Sciences; Colorectal cancer; Computer and Information Sciences; Datasets; DNA microarrays; Genomes; Independent study; Informatics; Medicine and Health Sciences; Meta-analysis; Methods; Microbiomes; Microbiota; Microbiota (Symbiotic organisms); Multivariate analysis; Open access; Physical Sciences; Physiological aspects; Reagents; Research and Analysis Methods; Ribonucleic acid; RNA; Scientific imaging; Sensitivity analysis; Spectrometry; Studies; Taxonomy; United States > US; Massachusetts
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1006102

High-throughput data generation platforms, like mass-spectrometry, microarrays, and second-generation sequencing are susceptible to batch effects due to run-to-run variation in reagents, equipment, protocols, or personnel. Currently, batch correction methods are not commonly applied to microbiome sequencing datasets. In this paper, we compare different batch-correction methods applied to microbiome case-control studies. We introduce a model-free normalization procedure where features (i.e. bacterial taxa) in case samples are converted to percentiles of the equivalent features in control samples within a study prior to pooling data across studies. We look at how this percentile-normalization method compares to traditional meta-analysis methods for combining independent p-values and to limma and ComBat, widely used batch-correction models developed for RNA microarray data. Overall, we show that percentile-normalization is a simple, non-parametric approach for correcting batch effects and improving sensitivity in case-control meta-analyses.