In mass spectrometry (MS) experiments, more than thousands of peaks are detected in the space of mass-to-charge ratio and chromatographic retention time, each associated with an abundance measurement. However, a large proportion of the peaks consists of experimental noise and low abundance compounds are typically masked by noise peaks, compromising the quality of the data. In this paper, we propose a new measure of similarity between a pair of MS experiments, called truncated rank correlation (TRC). To provide a robust metric of similarity in noisy high-dimensional data, TRC uses truncated top ranks (or top m-ranks) for calculating correlation. A comprehensive numerical study suggests that TRC outperforms traditional sample correlation and Kendall’s τ. We apply TRC to measuring test-retest reliability of two MS experiments, including biological replicate analysis of the metabolome in HEK293 cells and metabolomic profiling of benign prostate hyperplasia (BPH) patients. An R package trc of the proposed TRC and related functions is available at https://sites.google.com/site/dhyeonyu/software.

中文翻译：

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截断秩相关 (TRC) 作为质谱数据中重测可靠性的稳健测量

在质谱 (MS) 实验中，在质荷比和色谱保留时间的空间中检测到数千个峰，每个峰都与丰度测量相关。然而，大部分峰由实验噪声组成，低丰度化合物通常被噪声峰掩盖，影响数据质量。在本文中，我们提出了一种新的测量一对 MS 实验之间相似性的方法，称为截断秩相关 (TRC)。为了在嘈杂的高维数据中提供稳健的相似性度量，TRC 使用截断的最高排名（或最高 m 排名）来计算相关性。一项全面的数值研究表明，TRC 优于传统的样本相关性和 Kendall 的τ. 我们将 TRC 应用于测量两个 MS 实验的重测可靠性，包括 HEK293 细胞中代谢组的生物复制分析和良性前列腺增生 (BPH) 患者的代谢组学分析。一个 R 包trc建议的 TRC 和相关功能的详细信息可在https://sites.google.com/site/dhyeonyu/software.