Correcting for batch effects in case-control microbiome studies
Author(s)Gibbons, Sean Michael; Duvallet, Claire; Alm, Eric J
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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.
DepartmentMassachusetts Institute of Technology. Department of Biological Engineering
PLOS Computational Biology
Public Library of Science (PLoS)
Gibbons, Sean M., et al. “Correcting for Batch Effects in Case-Control Microbiome Studies.” PLOS Computational Biology, edited by Morgan Langille, vol. 14, no. 4, Apr. 2018, p. e1006102. © 2018 Gibbons et al.
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