A broken promise: microbiome differential abundance methods do not control the false discovery rate
Abstract
High-throughput sequencing technologies allow easy characterization of the human microbiome, but the statistical methods to analyze microbiome data are still in their infancy. Differential abundance methods aim at detecting associations between the abundances of bacterial species and subject grouping factors. The results of such methods are important to identify the microbiome as a prognostic or diagnostic biomarker or to demonstrate efficacy of prodrug or antibiotic drugs. Because of a lack of benchmarking studies in the microbiome field, no consensus exists on the performance of the statistical methods. We have compared a large number of popular methods through extensive parametric and nonparametric simulation as well as real data shuffling algorithms. The results are consistent over the different approaches and all point to an alarming excess of false discoveries. This raises great doubts about the reliability of discoveries in past studies and imperils reproducibility of microbiome experiments. To further improve method benchmarking, we introduce a new simulation tool that allows to generate correlated count data following any univariate count distribution; the correlation structure may be inferred from real data. Most simulation studies discard the correlation between species, but our results indicate that this correlation can negatively affect the performance of statistical methods.
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If the compositional property is not accounted for, the likelihood of introducing false positives in differential abundance analysis and negative correlation biases in correlation-based analysis increases heavily.
“If this statistical property is not accounted for, the likelihood of introducing false positives in differential abundance analysis59,60 and negative correlation biases in correlation-based analysis61,62 increases heavily.”
From Longitudinal gut microbiome changes in immune checkpoint blockade-treated advanced melanoma (2024) · cites this paper as [59]
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Model-based joint visualization of multiple compositional omics datasets
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Self-citation
- Authors
- Hawinkel, S., Mattiello, F., Bijnens, L., & Thas, O.
- Affiliations
- Ghent University (BE)
- Hasselt University (BE)
- Corresponding author
- Hawinkel, Stijn
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-08-22
- Volume
- 20
- Issue
- 1
- Pages
- 210-221
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- OpenAlex
- W2749493208
- Cited by
- 199
- References
- 60
- Citations by year
- 2026: 15 · 2025: 22 · 2024: 22 · 2023: 23 · 2022: 36 · 2021: 37 · 2020: 22 · 2019: 13 · 2018: 7 · 2017: 2
- FWCI
- 4.91
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Briefings in Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion)
- Document Server@UHasselt (UHasselt) (submittedVersion)
- Funders
- Janssen Pharmaceuticals (NCT3.0_2015.13, NCT3.0_2015.2 SPL/RP)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- false discovery rate, method benchmarking, simulation software, correlated count data, reproducibility, statistical power, biomarker discovery, human microbiome
- MeSH terms
- Algorithms, Computer Simulation, Humans, Statistics, Nonparametric, Computational Biology, Databases, Genetic, Biodiversity, High-Throughput Nucleotide Sequencing, Microbiota
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