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Microbiome differential abundance methods produce different results across 38 datasets

Author

Listed:
  • Jacob T. Nearing

    (Dalhousie University)

  • Gavin M. Douglas

    (Dalhousie University)

  • Molly G. Hayes

    (Dalhousie University)

  • Jocelyn MacDonald

    (Dalhousie University)

  • Dhwani K. Desai

    (Dalhousie University)

  • Nicole Allward

    (Dalhousie University)

  • Casey M. A. Jones

    (Dalhousie University)

  • Robyn J. Wright

    (Dalhousie University)

  • Akhilesh S. Dhanani

    (Dalhousie University)

  • André M. Comeau

    (Dalhousie University)

  • Morgan G. I. Langille

    (Dalhousie University
    Dalhousie University)

Abstract

Identifying differentially abundant microbes is a common goal of microbiome studies. Multiple methods are used interchangeably for this purpose in the literature. Yet, there are few large-scale studies systematically exploring the appropriateness of using these tools interchangeably, and the scale and significance of the differences between them. Here, we compare the performance of 14 differential abundance testing methods on 38 16S rRNA gene datasets with two sample groups. We test for differences in amplicon sequence variants and operational taxonomic units (ASVs) between these groups. Our findings confirm that these tools identified drastically different numbers and sets of significant ASVs, and that results depend on data pre-processing. For many tools the number of features identified correlate with aspects of the data, such as sample size, sequencing depth, and effect size of community differences. ALDEx2 and ANCOM-II produce the most consistent results across studies and agree best with the intersect of results from different approaches. Nevertheless, we recommend that researchers should use a consensus approach based on multiple differential abundance methods to help ensure robust biological interpretations.

Suggested Citation

  • Jacob T. Nearing & Gavin M. Douglas & Molly G. Hayes & Jocelyn MacDonald & Dhwani K. Desai & Nicole Allward & Casey M. A. Jones & Robyn J. Wright & Akhilesh S. Dhanani & André M. Comeau & Morgan G. I., 2022. "Microbiome differential abundance methods produce different results across 38 datasets," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28034-z
    DOI: 10.1038/s41467-022-28034-z
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    Cited by:

    1. Maria Rita Perrone & Salvatore Romano & Giuseppe De Maria & Paolo Tundo & Anna Rita Bruno & Luigi Tagliaferro & Michele Maffia & Mattia Fragola, 2022. "Compositional Data Analysis of 16S rRNA Gene Sequencing Results from Hospital Airborne Microbiome Samples," IJERPH, MDPI, vol. 19(16), pages 1-21, August.
    2. Kai Markus Schneider & Antje Mohs & Wenfang Gui & Eric J. C. Galvez & Lena Susanna Candels & Lisa Hoenicke & Uthayakumar Muthukumarasamy & Christian H. Holland & Carsten Elfers & Konrad Kilic & Caroli, 2022. "Imbalanced gut microbiota fuels hepatocellular carcinoma development by shaping the hepatic inflammatory microenvironment," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    3. Karen D. Corbin & Elvis A. Carnero & Blake Dirks & Daria Igudesman & Fanchao Yi & Andrew Marcus & Taylor L. Davis & Richard E. Pratley & Bruce E. Rittmann & Rosa Krajmalnik-Brown & Steven R. Smith, 2023. "Host-diet-gut microbiome interactions influence human energy balance: a randomized clinical trial," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    4. Zachary D. Wallen & Ayse Demirkan & Guy Twa & Gwendolyn Cohen & Marissa N. Dean & David G. Standaert & Timothy R. Sampson & Haydeh Payami, 2022. "Metagenomics of Parkinson’s disease implicates the gut microbiome in multiple disease mechanisms," Nature Communications, Nature, vol. 13(1), pages 1-20, December.
    5. Braden T Tierney & Yingxuan Tan & Zhen Yang & Bing Shui & Michaela J Walker & Benjamin M Kent & Aleksandar D Kostic & Chirag J Patel, 2022. "Systematically assessing microbiome–disease associations identifies drivers of inconsistency in metagenomic research," PLOS Biology, Public Library of Science, vol. 20(3), pages 1-18, March.

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