{Reference Type}: Comparative Study
{Title}: Correcting for batch effects in case-control microbiome studies.
{Author}: Gibbons SM;Duvallet C;Alm EJ;
{Journal}: PLoS Comput Biol
{Volume}: 14
{Issue}: 4
{Year}: 04 2018
{Factor}: 4.779
{DOI}: 10.1371/journal.pcbi.1006102
{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.