Correcting for batch effects in case-control microbiome studies
Name
pcbi.1006102.pdf
Size
25.56 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Gibbons, Sean Michael
Duvallet, Claire
Alm, Eric J
Date Issued
April 2018
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
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.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Creative Commons Attribution 4.0 International License
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DOI of Published Version
https://doi.org/10.1371/JOURNAL.PCBI.1006102
