Journal
NATURE METHODS
Volume 10, Issue 12, Pages 1200-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/NMETH.2658
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Funding
- US National Science Foundation [DGE0750616]
- Bill and Melinda Gates Foundation [42917]
- US National Institutes of Health [5R01HG005220]
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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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