Robust methods for differential abundance analysis in marker gene surveys

We introduce a novel methodology for differential abundance analysis in sparse high-throughput 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 under-sampling: a common feature of large-scale marker gene studies. We show, using simulated data and several published microbiota datasets, that metagenomeSeq outperforms the tools currently used in this field.