A recent breakthrough in differential network (DN) analysis of microbiome data has been realized with the advent of next-generation sequencing technologies. The DN analysis disentangles the microbial co-abundance among taxa by comparing the network properties between two or more graphs under different biological conditions. However, the existing methods to the DN analysis for microbiome data do not adjust for other clinical differences between subjects. We propose a Statistical Approach via Pseudo-value Information and Estimation for Differential Network Analysis (SOHPIE-DNA) that incorporates additional covariates such as continuous age and categorical BMI. SOHPIE-DNA is a regression technique adopting jackknife pseudo-values that can be implemented readily for the analysis. We demonstrate through simulations that SOHPIE-DNA consistently reaches higher recall and F1-score, while maintaining similar precision and accuracy to existing methods (NetCoMi and MDiNE). Lastly, we apply SOHPIE-DNA on two real datasets from the American Gut Project and the Diet Exchange Study to showcase the utility. The analysis of the Diet Exchange Study is to showcase that SOHPIE-DNA can also be used to incorporate the temporal change of connectivity of taxa with the inclusion of additional covariates. As a result, our method has found taxa that are related to the prevention of intestinal inflammation and severity of fatigue in advanced metastatic cancer patients.

翻译：随着下一代测序技术的出现，微生物组数据的差异网络（DN）分析取得了一项突破。DN分析通过比较不同生物学条件下两个或多个图之间的网络属性，揭示分类群之间的微生物共丰度关系。然而，现有的微生物组数据DN分析方法并未调整受试者之间的其他临床差异。我们提出了一种基于伪值信息和估计的差异网络分析统计方法（SOHPIE-DNA），该方法纳入了年龄（连续变量）和BMI（分类变量）等额外协变量。SOHPIE-DNA是一种采用Jackknife伪值的回归技术，可直接用于分析。通过模拟实验证明，SOHPIE-DNA在保持与现有方法（NetCoMi和MDiNE）相似精确度和准确性的同时，持续获得更高的召回率和F1分数。最后，我们将SOHPIE-DNA应用于美国肠道项目和饮食交换研究的两个真实数据集，以展示其实用性。对饮食交换研究的分析表明，SOHPIE-DNA还可用于整合分类群连接性的时间变化，同时纳入额外协变量。结果发现，我们的方法识别出与预防肠道炎症和晚期转移性癌症患者疲劳严重程度相关的分类群。