Shi Yiming, Liu Lili, Chen Jun, Wylie Kristine M, Wylie Todd N, Stout Molly J, Wang Chan, Zhang Haixiang, Shih Ya-Chen T, Xu Xiaoyi, Zhang Ai, Park Sung Hee, Jiang Hongmei, Liu Lei
Institute for Informatics Data Science and Biostatistics, Washington University in St. Louis, St. Louis, MO, United States.
Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States.
Front Genet. 2024 Oct 21;15:1458851. doi: 10.3389/fgene.2024.1458851. eCollection 2024.
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 approach is validated through extensive simulation studies, demonstrating its effectiveness in addressing overdispersion and improving inference accuracy. Additionally, we apply our approach to two real datasets collected from the human gut and vagina, respectively, demonstrating the wide applicability of our methods. The results highlight the efficacy of our covariance estimators in addressing the challenges of microbiome data analysis. The corresponding software implementation is publicly available at https://github.com/yimshi/robustestimates.
微生物组数据的复杂性使得差异丰度分析具有挑战性。微生物组丰度计数往往向右偏斜且具有异方差性（也称为过度离散），如果处理不当，可能会导致错误的推断。在本文中，我们提出了一个简单而有效的框架，通过将泊松（对数线性）回归与通过自助法和三明治稳健估计的标准误差估计相结合来应对这些挑战。即使分布假设或方差结构不正确，这种标准误差估计也是准确的，并能产生令人满意的推断。我们的方法通过广泛的模拟研究得到验证，证明了其在解决过度离散和提高推断准确性方面的有效性。此外，我们将我们的方法分别应用于从人类肠道和阴道收集的两个真实数据集，证明了我们方法的广泛适用性。结果突出了我们的协方差估计器在应对微生物组数据分析挑战方面的功效。相应的软件实现可在https://github.com/yimshi/robustestimates上公开获取。
