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Correcting for batch effects in case-control microbiome studies

Author(s)
Gibbons, Sean Michael; Duvallet, Claire; Alm, Eric J
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Abstract
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.
Date issued
2018-04
URI
http://hdl.handle.net/1721.1/117510
Department
Massachusetts Institute of Technology. Department of Biological Engineering
Journal
PLOS Computational Biology
Publisher
Public Library of Science (PLoS)
Citation
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.
Version: Final published version
ISSN
1553-7358

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