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• W2892875286 startingPage "e1006102" @default.
• W2892875286 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." @default.
• W2892875286 created "2018-10-05" @default.
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• W2892875286 creator A5078594398 @default.
• W2892875286 creator A5091119492 @default.
• W2892875286 date "2018-04-23" @default.
• W2892875286 modified "2023-10-17" @default.
• W2892875286 title "Correcting for batch effects in case-control microbiome studies" @default.
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• W2892875286 doi "https://doi.org/10.1371/journal.pcbi.1006102" @default.
• W2892875286 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5940237" @default.
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• W2892875286 hasPublicationYear "2018" @default.
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