RT Journal Article
SR Electronic
T1 Discrete False-Discovery Rate Improves Identification of Differentially Abundant Microbes
JF mSystems
FD American Society for Microbiology
SP e00092-17
DO 10.1128/mSystems.00092-17
VO 2
IS 6
A1 Jiang, Lingjing
A1 Amir, Amnon
A1 Morton, James T.
A1 Heller, Ruth
A1 Arias-Castro, Ery
A1 Knight, Rob
A2 Neufeld, Josh D.
YR 2017
UL http://msystems.asm.org/content/2/6/e00092-17.abstract
AB 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.