Back to Search Start Over

Correcting for batch effects in case-control microbiome studies.

Authors :
Gibbons, Sean M.
Duvallet, Claire
Alm, Eric J.
Source :
PLoS Computational Biology; 4/23/2018, Vol. 14 Issue 3, p1-17, 17p, 1 Diagram, 1 Chart, 6 Graphs
Publication Year :
2018

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1553734X
Volume :
14
Issue :
3
Database :
Complementary Index
Journal :
PLoS Computational Biology
Publication Type :
Academic Journal
Accession number :
129235999
Full Text :
https://doi.org/10.1371/journal.pcbi.1006102