Differential abundance analysis for microbial marker-gene surveys
Nature methods, 2013•nature.com
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.
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.
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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