Simplified methods for variance estimation in microbiome abundance count data analysis - 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; et al. (Frontiers in Genetics, 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 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.