IDEAS home Printed from https://ideas.repec.org/a/oup/biomet/v109y2022i2p405-420..html
   My bibliography  Save this article

High-dimensional log-error-in-variable regression with applications to microbial compositional data analysis
[Log contrast models for experiments with mixtures]

Author

Listed:
  • Pixu Shi
  • Yuchen Zhou
  • Anru R Zhang

Abstract

SummaryIn microbiome and genomic studies, the regression of compositional data has been a crucial tool for identifying microbial taxa or genes that are associated with clinical phenotypes. To account for the variation in sequencing depth, the classic log-contrast model is often used where read counts are normalized into compositions. However, zero read counts and the randomness in covariates remain critical issues. We introduce a surprisingly simple, interpretable and efficient method for the estimation of compositional data regression through the lens of a novel high-dimensional log-error-in-variable regression model. The proposed method provides corrections on sequencing data with possible overdispersion and simultaneously avoids any subjective imputation of zero read counts. We provide theoretical justifications with matching upper and lower bounds for the estimation error. The merit of the procedure is illustrated through real data analysis and simulation studies.

Suggested Citation

  • Pixu Shi & Yuchen Zhou & Anru R Zhang, 2022. "High-dimensional log-error-in-variable regression with applications to microbial compositional data analysis [Log contrast models for experiments with mixtures]," Biometrika, Biometrika Trust, vol. 109(2), pages 405-420.
    • Handle: RePEc:oup:biomet:v:109:y:2022:i:2:p:405-420.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/biomet/asab020
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Jingxuan Luo & Lili Yue & Gaorong Li, 2023. "Overview of High-Dimensional Measurement Error Regression Models," Mathematics, MDPI, vol. 11(14), pages 1-22, July.
    2. Yuan, Panxu & Jin, Changhan & Li, Gaorong, 2024. "FDR control for linear log-contrast models with high-dimensional compositional covariates," Computational Statistics & Data Analysis, Elsevier, vol. 197(C).

    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:oup:biomet:v:109:y:2022:i:2:p:405-420.. 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: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/biomet .

    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.