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mSystems. 2017 Nov 21;2(6). pii: e00092-17. doi: 10.1128/mSystems.00092-17. eCollection 2017 Nov-Dec.

Discrete False-Discovery Rate Improves Identification of Differentially Abundant Microbes.

Author information

1
Department of Pediatrics, University of California San Diego, La Jolla, California, USA.
2
Department of Family Medicine and Public Health, University of California San Diego, La Jolla, California, USA.
3
Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA.
4
Department of Statistics and Operations Research, Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel.
5
Department of Mathematics, University of California San Diego, La Jolla, California, USA.
6
Center for Microbiome Innovation, University of California San Diego, La Jolla, California, USA.

Abstract

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.

KEYWORDS:

FDR; differential abundance; discrete test statistics; high dimension; microbiome; multiple comparison; multiple testing; sparse; statistics

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