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Nat Methods. 2013 Dec;10(12):1200-2. doi: 10.1038/nmeth.2658. Epub 2013 Sep 29.

Differential abundance analysis for microbial marker-gene surveys.

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1
1] Graduate Program in Applied Mathematics & Statistics, and Scientific Computation, University of Maryland, College Park, Maryland, USA. [2] Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA.

Abstract

We introduce a methodology to assess differential abundance in sparse high-throughput microbial marker-gene survey data. Our approach, implemented in the metagenomeSeq Bioconductor package, relies on a novel normalization technique and a statistical model that accounts for undersampling-a common feature of large-scale marker-gene studies. Using simulated data and several published microbiota data sets, we show that metagenomeSeq outperforms the tools currently used in this field.

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PMID:
24076764
PMCID:
PMC4010126
DOI:
10.1038/nmeth.2658
[Indexed for MEDLINE]
Free PMC Article
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