【报告摘要】Inferring microbial networks is complicated by the compositional nature of microbiome sequencing data, which are also sparse, high-dimensional, highly overdispersed, and occasionally derived from clustered samples. Most existing methods, such as SparCC, CCLasso, and COAT, provide only point estimates of Pearson's correlations. We demonstrate for the first time that the correlation estimates tend to be biased downward in the presence of overdispersion. In this article, we introduce a novel testing method called TestNet, which produces well calibrated results by controlling the false discovery rate (FDR). TestNet is based on Pearson's covariance and distance covariance of the centered-log-ratio data to capture linear and nonlinear dependencies, respectively. Because the correlations of interest are unidentifiable, we focus on testing the deviations of the covariances from the null hypotheses. We developed a permutation-based procedure for generating valid null replicates that account for compositional effects and extensive zeros in microbiome data. Our extensive simulation studies indicate that TestNet is the only method that effectively controls the FDR while achieving high power across a wide range of scenarios. Applying TestNet to two real microbiome datasets uncovered scientifically plausible networks.

【报告人简介】胡懿娟，北京大学生物统计系教授，入选国家级人才计划。于北京大学数学科学学院获得统计学学士学位，并在美国北卡罗来纳大学教堂山分校获得生物统计学博士学位，曾于2011-2024年在Emory大学任教，并于2024年7月回到北京大学任职。主要致力于开发生物统计学领域中针对高维度和高噪声组学数据的统计理论和方法，特别关注微生物组数据和遗传数据中的高维假设检验、稳健推测以及缺失或偏差数据等问题。以第一作者或者通讯作者在《Journal of American Statistical Association》(JASA) 、《Proceedings of the National Academy of Sciences》(PNAS) 、《Microbiome》、《American Journal of Human Genetics》 (AJHG) 等期刊上发表了多篇文章。多次受邀担任美国国家卫生研究院基金评审组成员，并担任《BMC Bioinformatics》 和《Statistics in Biosciences》期刊的副主编。