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ORIGINAL RESEARCH article

Front. Genet.
Sec. Statistical Genetics and Methodology
Volume 15 - 2024 | doi: 10.3389/fgene.2024.1458851
This article is part of the Research Topic Statistical Approaches, Applications, and Software for Longitudinal Microbiome Data Analysis and Microbiome Multi-Omics Data Integration View all articles

Simplified Methods for Variance Estimation in Microbiome Abundance Count Data Analysis

Provisionally accepted
Yiming Shi Yiming Shi 1Lili Liu Lili Liu 1JUN CHEN JUN CHEN 2Kristine Wylie Kristine Wylie 1Todd N. Wylie Todd N. Wylie 1Molly Stout Molly Stout 3Chan Wang Chan Wang 4Haixiang Zhang Haixiang Zhang 5Tina Shih Tina Shih 6Xiaoyi Xu Xiaoyi Xu 1Ai Zhang Ai Zhang 1Sung Hee Park Sung Hee Park 1Hongmei Jiang Hongmei Jiang 7Lei Liu Lei Liu 1*
  • 1 Washington University in St. Louis, St. Louis, Missouri, United States
  • 2 Mayo Clinic, Rochester, Minnesota, United States
  • 3 University of Michigan, Ann Arbor, Michigan, United States
  • 4 New York University, New York City, New York, United States
  • 5 Tianjin University, Tianjin, Tianjin, China
  • 6 University of California, Los Angeles, Los Angeles, California, United States
  • 7 Northwestern University, Evanston, Illinois, United States

The final, formatted version of the article will be published soon.

    The complex nature of microbiome data has made the differential abundance analysis challenging. Microbiome abundance counts are often skewed to the right and heteroscedastic (also known as overdispersion), potentially leading to incorrect inferences if not properly addressed. In this paper, we propose a simple yet effective framework to tackle the challenges by integrating Poisson (log-linear) regression with standard error estimation through the Bootstrap method and Sandwich robust estimation. Such standard error estimates are accurate and yield satisfactory inference even if the distributional assumption or the variance structure is incorrect. Our findings are supported by an extensive series of simulation studies, highlighting the efficacy of our covariance estimators in addressing the challenges of microbiome data analysis.We apply our approach to two real datasets collected from human gut and vagina, respectively, demonstrating the wide applicability of our methods. The software is available at https://github.com/yimshi/robustestimates.

    Keywords: Microbiome abundance count, Robust variance estimation, heteroscedasticity, Sandwich estimates, Bootstrap

    Received: 03 Jul 2024; Accepted: 30 Sep 2024.

    Copyright: © 2024 Shi, Liu, CHEN, Wylie, Wylie, Stout, Wang, Zhang, Shih, Xu, Zhang, Park, Jiang and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Lei Liu, Washington University in St. Louis, St. Louis, 63130, Missouri, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.