截断秩相关(TRC)作为质谱数据测试-重测信度的可靠度量。

IF 0.9 4区 数学 Q3 Mathematics Statistical Applications in Genetics and Molecular Biology Pub Date : 2019-05-30 DOI:10.1515/sagmb-2018-0056
Johan Lim, Donghyeon Yu, Hsun-Chih Kuo, Hyungwon Choi, Scott Walmsley
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引用次数: 0

摘要

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以上的峰,每个峰都与丰度测量相关。然而,很大一部分峰由实验噪声组成,低丰度化合物通常被噪声峰掩盖,从而影响数据的质量。在本文中,我们提出了一种新的测量对质谱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一个稳健的相似性度量,TRC使用截断的顶级秩(或顶级m-秩)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和肯德尔τ。我们应用TRC测量了HEK293细胞代谢组的生物重复分析和良性前列腺增生(BPH)患者代谢组谱两项质谱实验的重测信度。建议的trc及其相关功能的R包trc可在https://sites.google.com/site/dhyeonyu/software获得。
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Truncated rank correlation (TRC) as a robust measure of test-retest reliability in mass spectrometry data.

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.

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来源期刊
CiteScore
1.20
自引率
11.10%
发文量
8
审稿时长
6-12 weeks
期刊介绍: Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.
期刊最新文献
Empirically adjusted fixed-effects meta-analysis methods in genomic studies. A CNN-CBAM-BIGRU model for protein function prediction. A heavy-tailed model for analyzing miRNA-seq raw read counts. Flexible model-based non-negative matrix factorization with application to mutational signatures. Choice of baseline hazards in joint modeling of longitudinal and time-to-event cancer survival data.
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