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Correcting for batch effects in case-control microbiome studies

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  • Sean M Gibbons
  • Claire Duvallet
  • Eric J Alm

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.Author summary: Batch effects are obstacles to comparing results across 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 microbiome datasets.

Suggested Citation

  • Sean M Gibbons & Claire Duvallet & Eric J Alm, 2018. "Correcting for batch effects in case-control microbiome studies," PLOS Computational Biology, Public Library of Science, vol. 14(4), pages 1-17, April.
  • Handle: RePEc:plo:pcbi00:1006102
    DOI: 10.1371/journal.pcbi.1006102
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    References listed on IDEAS

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    1. Claire Duvallet & Sean M. Gibbons & Thomas Gurry & Rafael A. Irizarry & Eric J. Alm, 2017. "Meta-analysis of gut microbiome studies identifies disease-specific and shared responses," Nature Communications, Nature, vol. 8(1), pages 1-10, December.
    2. Peter J. Turnbaugh & Micah Hamady & Tanya Yatsunenko & Brandi L. Cantarel & Alexis Duncan & Ruth E. Ley & Mitchell L. Sogin & William J. Jones & Bruce A. Roe & Jason P. Affourtit & Michael Egholm & Be, 2009. "A core gut microbiome in obese and lean twins," Nature, Nature, vol. 457(7228), pages 480-484, January.
    3. Chao Chen & Kay Grennan & Judith Badner & Dandan Zhang & Elliot Gershon & Li Jin & Chunyu Liu, 2011. "Removing Batch Effects in Analysis of Expression Microarray Data: An Evaluation of Six Batch Adjustment Methods," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-10, February.
    4. Edoardo Pasolli & Duy Tin Truong & Faizan Malik & Levi Waldron & Nicola Segata, 2016. "Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights," PLOS Computational Biology, Public Library of Science, vol. 12(7), pages 1-26, July.
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    1. Wodan Ling & Jiuyao Lu & Ni Zhao & Anju Lulla & Anna M. Plantinga & Weijia Fu & Angela Zhang & Hongjiao Liu & Hoseung Song & Zhigang Li & Jun Chen & Timothy W. Randolph & Wei Li A. Koay & James R. Whi, 2022. "Batch effects removal for microbiome data via conditional quantile regression," Nature Communications, Nature, vol. 13(1), pages 1-14, December.

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