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Statistical models and computational algorithms for discovering relationships in microbiome data

Author

Listed:
  • Shaikh Mateen R.

    (Department of Clinical Epidemiology and Biostatistics, McMaster University, 1280 Main Street West, Hamilton, Ontario, L8S 4L8, Canada)

  • Beyene Joseph

    (Department of Clinical Epidemiology and Biostatistics, McMaster University, 1280 Main Street West, Hamilton, Ontario, L8S 4L8, Canada)

Abstract

Microbiomes, populations of microscopic organisms, have been found to be related to human health and it is expected further investigations will lead to novel perspectives of disease. The data used to analyze microbiomes is one of the newest types (the result of high-throughput technology) and the means to analyze these data is still rapidly evolving. One of the distributions that have been introduced into the microbiome literature, the Dirichlet-Multinomial, has received considerable attention. We extend this distribution’s use uncover compositional relationships between organisms at a taxonomic level. We apply our new method in two real microbiome data sets: one from human nasal passages and another from human stool samples.

Suggested Citation

  • Shaikh Mateen R. & Beyene Joseph, 2017. "Statistical models and computational algorithms for discovering relationships in microbiome data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(1), pages 1-12, March.
    • Handle: RePEc:bpj:sagmbi:v:16:y:2017:i:1:p:1-12:n:1
    DOI: 10.1515/sagmb-2015-0096
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    References listed on IDEAS

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    3. Ian Holmes & Keith Harris & Christopher Quince, 2012. "Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-15, February.
    4. Patricio S La Rosa & J Paul Brooks & Elena Deych & Edward L Boone & David J Edwards & Qin Wang & Erica Sodergren & George Weinstock & William D Shannon, 2012. "Hypothesis Testing and Power Calculations for Taxonomic-Based Human Microbiome Data," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-13, December.
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