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Correcting for batch effects in case-control microbiome studies

Sean M. Gibbons, Claire Duvallet, Eric J. Alm
doi: https://doi.org/10.1101/165910
Sean M. Gibbons
1Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA;
2Center for Microbiome Informatics and Therapeutics, Cambridge, MA, USA;
3The Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Claire Duvallet
1Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA;
2Center for Microbiome Informatics and Therapeutics, Cambridge, MA, USA;
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Eric J. Alm
1Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA;
2Center for Microbiome Informatics and Therapeutics, Cambridge, MA, USA;
3The Broad Institute of MIT and Harvard, Cambridge, MA, USA
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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 multiple 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 ComBat, a widely used batch-correction model developed for RNA microarray data, and traditional meta-analysis methods for combining independent p-values. Overall, we show that percentile-normalization is a simple, model-free approach for removing batch effects and improving sensitivity in case-control meta-analyses.

Author Summary Batch effects present a significant obstacle to comparing results across independent studies. Traditional meta-analysis techniques for combining p-values from independent studies, like Fisher’s method, are effective, but statistically conservative. If batch-effects can be corrected, then statistical tests can be performed on data pooled across studies, increasing sensitivity to detect differences between treatment groups. Here, we show how a simple, model-free approach corrects for batch effects in case-control datasets.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted July 24, 2017.
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Correcting for batch effects in case-control microbiome studies
Sean M. Gibbons, Claire Duvallet, Eric J. Alm
bioRxiv 165910; doi: https://doi.org/10.1101/165910
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Correcting for batch effects in case-control microbiome studies
Sean M. Gibbons, Claire Duvallet, Eric J. Alm
bioRxiv 165910; doi: https://doi.org/10.1101/165910

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