Many biological high-throughput datasets, such as targeted amplicon-based and metagenomic sequencing data, are compositional. A common exploratory data analysis task is to infer robust statistical associations between high-dimensional microbial compositions and habitat- or host-related covariates. To address this, a general robust statistical regression framework RobRegCC (Robust Regression with Compositional Covariates) is proposed, which extends the linear log-contrast model by a mean shift formulation for capturing outliers. RobRegCC includes sparsity-promoting convex and non-convex penalties for parsimonious model estimation, a data-driven robust initialization procedure, and a novel robust cross-validation model selection scheme. The procedure is implemented in the R package robregcc. Extensive simulation studies show the RobRegCC's ability to perform simultaneous sparse log-contrast regression and outlier detection over a wide range of settings. To demonstrate the seamless applicability of the workflow to real data, the gut microbiome dataset from HIV patients are analyzed and robust associations between a sparse set of microbial species and host immune response from soluble CD14 measurements are inferred.

许多生物高通量数据集，例如基于目标扩增子的和宏基因组测序数据，都是组成的。一个常见的探索性数据分析任务是推断高维微生物组成与栖息地或宿主相关的协变量之间的稳健统计关联。为了解决这个问题，提出了一个通用的稳健统计回归框架RobRegCC（具有组合协变量的稳健回归），它通过用于捕获异常值的均值偏移公式扩展了线性对数对比模型。RobRegCC包括用于简约模型估计的稀疏促进凸和非凸惩罚、数据驱动的鲁棒初始化程序和新颖的鲁棒交叉验证模型选择方案。程序在R包中实现罗布瑞克。广泛的模拟研究表明RobRegCC能够在广泛的设置范围内同时执行稀疏对数对比度回归和异常值检测。为了证明该工作流程对真实数据的无缝适用性，我们分析了来自 HIV 患者的肠道微生物组数据集，并推断了一组稀疏的微生物物种与可溶性 CD14 测量值的宿主免疫反应之间的稳健关联。