AI 中文总结
该研究针对微生物组数据统计挑战，提出经验自助框架，用于跨组微生物群落均值相等性检验，考虑数据单纯形结构，通过两项大规模研究验证，能识别传统方法未检测到的临床差异，凸显重采样推断的潜力。
AI 中文摘要
了解微生物群落结构差异对于揭示诸如结直肠癌和早产等疾病的风险因素和潜在机制至关重要。微生物组数据存在独特的统计挑战，因其本质上是成分数据，违反了许多经典推断程序的假设。我们提出了一个经验自助框架，能够对跨组微生物群落均值的相等性进行稳健的假设检验，包括两样本、配对和多样本设置。该方法考虑了微生物组数据的单纯形结构，即使在高维情况下也能提供有效的推断。通过应用于两项大规模研究，即结直肠腺瘤和癌症患者的粪便微生物群以及有早产结局的孕妇阴道微生物群，我们证明了我们的方法能够识别传统方法未能检测到的具有临床意义的差异，如腺瘤患病率的年龄相关差异和阴道微生物组组成的种族相关差异。这些结果突出了基于重采样推断在推进微生物组研究、提高可重复性和揭示临床相关微生物特征方面的潜力。
英文摘要
Understanding differences in microbial community structure is critical for uncovering risk factors and mechanisms underlying diseases such as colorectal cancer and preterm birth. Microbiome data present unique statistical challenges because they are compositional in nature, violating assumptions of many classical inference procedures. We propose an empirical bootstrap framework that enables robust hypothesis testing for equality of microbial community means across groups, including two-sample, paired, and multi-sample settings. The method accounts for the simplex structure of microbiome data and provides valid inference even in high-dimensional regimes. Through applications to two large-scale studies, fecal microbiota in colorectal adenoma and cancer patients, and vaginal microbiota in pregnancy with preterm birth outcomes-we demonstrate that our approach identifies clinically meaningful differences that conventional methods fail to detect, such as age-related differences in adenoma prevalence and race-associated disparities in vaginal microbiome composition. These results highlight the potential of resampling-based inference for advancing microbiome research, improving reproducibility, and uncovering clinically relevant microbial signatures.
