%0 Journal Article
%A Jiang, Lingjing
%A Amir, Amnon
%A Morton, James T.
%A Heller, Ruth
%A Arias-Castro, Ery
%A Knight, Rob
%E Neufeld, Josh D.
%T Discrete False-Discovery Rate Improves Identification of Differentially Abundant Microbes
%D 2017
%R 10.1128/mSystems.00092-17
%J mSystems
%P e00092-17
%V 2
%N 6
%X Differential abundance testing is a critical task in microbiome studies that is complicated by the sparsity of data matrices. Here we adapt for microbiome studies a solution from the field of gene expression analysis to produce a new method, discrete false-discovery rate (DS-FDR), that greatly improves the power to detect differential taxa by exploiting the discreteness of the data. Additionally, DS-FDR is relatively robust to the number of noninformative features, and thus removes the problem of filtering taxonomy tables by an arbitrary abundance threshold. We show by using a combination of simulations and reanalysis of nine real-world microbiome data sets that this new method outperforms existing methods at the differential abundance testing task, producing a false-discovery rate that is up to threefold more accurate, and halves the number of samples required to find a given difference (thus increasing the efficiency of microbiome experiments considerably). We therefore expect DS-FDR to be widely applied in microbiome studies. IMPORTANCE DS-FDR can achieve higher statistical power to detect significant findings in sparse and noisy microbiome data compared to the commonly used Benjamini-Hochberg procedure and other FDR-controlling procedures.
%U https://msystems.asm.org/content/msys/2/6/e00092-17.full.pdf