Data outliers can carry very valuable information and might be most informative for the interpretation. Nevertheless, they are often neglected. An algorithm called cellwise outlier diagnostics using robust pairwise log ratios (cell‐rPLR) for the identification of outliers in single cell of a data matrix is proposed. The algorithm is designed for metabolomic data, where due to the size effect, the measured values are not directly comparable. Pairwise log ratios between the variable values form the elemental information for the algorithm, and the aggregation of appropriate outlyingness values results in outlyingness information. A further feature of cell‐rPLR is that it is useful for biomarker identification, particularly in the presence of cellwise outliers. Real data examples and simulation studies underline the good performance of this algorithm in comparison with alternative methods.

中文翻译：

---

基于成对对数比的代谢组学中细胞异常值检测和生物标志物识别

数据异常值可以携带非常有价值的信息，并且可能对解释最有用。然而，它们常常被忽视。提出了一种称为单元格异常值诊断的算法，该算法使用稳健的成对对数比 (cell-rPLR) 来识别数据矩阵的单个单元格中的异常值。该算法专为代谢组学数据而设计，其中由于尺寸效应，测量值不能直接比较。变量值之间的成对对数比率形成了算法的基本信息，适当的离群值的聚合导致离群信息。cell-rPLR 的另一个特点是它可用于生物标志物识别，特别是在存在细胞异常值的情况下。