IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v73y2017i4p1266-1278.html
   My bibliography  Save this article

A model for paired‐multinomial data and its application to analysis of data on a taxonomic tree

Author

Listed:
  • Pixu Shi
  • Hongzhe Li

Abstract

In human microbiome studies, sequencing reads data are often summarized as counts of bacterial taxa at various taxonomic levels specified by a taxonomic tree. This article considers the problem of analyzing two repeated measurements of microbiome data from the same subjects. Such data are often collected to assess the change of microbial composition after certain treatment, or the difference in microbial compositions across body sites. Existing models for such count data are limited in modeling the covariance structure of the counts and in handling paired multinomial count data. A new probability distribution is proposed for paired‐multinomial count data, which allows flexible covariance structure and can be used to model repeatedly measured multivariate count data. Based on this distribution, a test statistic is developed for testing the difference in compositions based on paired multinomial count data. The proposed test can be applied to the count data observed on a taxonomic tree in order to test difference in microbiome compositions and to identify the subtrees with different subcompositions. Simulation results indicate that proposed test has correct type 1 errors and increased power compared to some commonly used methods. An analysis of an upper respiratory tract microbiome data set is used to illustrate the proposed methods.

Suggested Citation

  • Pixu Shi & Hongzhe Li, 2017. "A model for paired‐multinomial data and its application to analysis of data on a taxonomic tree," Biometrics, The International Biometric Society, vol. 73(4), pages 1266-1278, December.
  • Handle: RePEc:bla:biomet:v:73:y:2017:i:4:p:1266-1278
    DOI: 10.1111/biom.12681
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.12681
    Download Restriction: no

    File URL: https://libkey.io/10.1111/biom.12681?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Jeffrey R. Wilson, 1989. "Chi‐Square Tests for Overdispersion with Multiparameter Estimates," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 38(3), pages 441-453, November.
    2. Steven N. Evans & Frederick A. Matsen, 2012. "The phylogenetic Kantorovich–Rubinstein metric for environmental sequence samples," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(3), pages 569-592, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Frederick A Matsen IV & Steven N Evans, 2013. "Edge Principal Components and Squash Clustering: Using the Special Structure of Phylogenetic Placement Data for Sample Comparison," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-15, March.
    2. Pratheepa Jeganathan & Susan P. Holmes, 2021. "A Statistical Perspective on the Challenges in Molecular Microbial Biology," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(2), pages 131-160, June.
    3. Vo Nguyen Le Duy & Ichiro Takeuchi, 2023. "Exact statistical inference for the Wasserstein distance by selective inference," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(1), pages 127-157, February.
    4. Mark Reiser & Karl F. Schuessler, 1991. "A Hierarchy for Some Latent Structure Models," Sociological Methods & Research, , vol. 19(4), pages 419-465, May.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:biomet:v:73:y:2017:i:4:p:1266-1278. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.