IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v75y2019i3p875-884.html
   My bibliography  Save this article

Prediction analysis for microbiome sequencing data

Author

Listed:
  • Tao Wang
  • Can Yang
  • Hongyu Zhao

Abstract

One goal of human microbiome studies is to relate host traits with human microbiome compositions. The analysis of microbial community sequencing data presents great statistical challenges, especially when the samples have different library sizes and the data are overdispersed with many zeros. To address these challenges, we introduce a new statistical framework, called predictive analysis in metagenomics via inverse regression (PAMIR), to analyze microbiome sequencing data. Within this framework, an inverse regression model is developed for overdispersed microbiota counts given the trait, and then a prediction rule is constructed by taking advantage of the dimension‐reduction structure in the model. An efficient Monte Carlo expectation‐maximization algorithm is proposed for maximum likelihood estimation. The method is further generalized to accommodate other types of covariates. We demonstrate the advantages of PAMIR through simulations and two real data examples.

Suggested Citation

  • Tao Wang & Can Yang & Hongyu Zhao, 2019. "Prediction analysis for microbiome sequencing data," Biometrics, The International Biometric Society, vol. 75(3), pages 875-884, September.
    • Handle: RePEc:bla:biomet:v:75:y:2019:i:3:p:875-884
    DOI: 10.1111/biom.13061
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.13061
    Download Restriction: no
    File URL: https://libkey.io/10.1111/biom.13061?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Li, Junlan & Wang, Tao, 2021. "Dimension reduction in binary response regression: A joint modeling approach," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
    2. Mei Dong & Longhai Li & Man Chen & Anthony Kusalik & Wei Xu, 2020. "Predictive analysis methods for human microbiome data with application to Parkinson’s disease," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-18, August.
    3. Konstantin Shestopaloff & Mei Dong & Fan Gao & Wei Xu, 2021. "Dcmd: Distance-based classification using mixture distributions on microbiome data," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-18, March.

    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:75:y:2019:i:3:p:875-884. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.