Torgrip R J O, Lindberg J, Linder M, Karlberg B, Jacobsson S P, Kolmert J, Gustafsson I, Schuppe-Koistinen I
Department of Analytical Chemistry, BioSysteMetrics Group, Stockholm University, SE-106 91 Stockholm, Sweden ; Safety Assessment, Molecular Toxicology, AstraZeneca R&D Södertälje, SE-151 85 Södertälje, Sweden.
Safety Assessment, Molecular Toxicology, AstraZeneca R&D Södertälje, SE-151 85 Södertälje, Sweden.
Metabolomics. 2006;2(1):1-19. doi: 10.1007/s11306-005-0013-z. Epub 2006 Apr 8.
This paper addresses the possibility of mathematically partition and process urine H-NMR spectra to enhance the efficiency of the subsequent multivariate data analysis in the context of metabolic profiling of a toxicity study. We show that by processing the NMR data with the peak alignment using reduced set mapping (PARS) algorithm and the use of sparse representation of the data results in the information contained in the original NMR data being preserved with retained resolution but free of the problem of peak shifts. We can now describe a method for differential expression analysis of NMR spectra by using prior knowledge, ., the onset of dosing, a partitioning not possible to achieve using raw or bucketed data. In addition we also outline a scheme for soft removal of "biological noise" from the aligned data: exhaustive bio-noise subtraction (EBS). The result is a straightforward protocol for detection of peaks that appear as a consequence of the drug response. In other words, it is possible to elucidate peak origin, either from endogenous substances from the administered drug/biomarkers. The partition of data originating from the normally regulating metabolome can, furthermore, be analyzed free of the superimposed biological noise. The proposed protocol results in enhanced interpretability of the processed data, ., a more refined metabolic trace, simplification of detection of consistent biomarkers, and a simplified search for metabolic end products of the administered drug.
本文探讨了在毒性研究的代谢谱分析背景下，对尿液氢核磁共振（H-NMR）谱进行数学划分和处理以提高后续多变量数据分析效率的可能性。我们表明，通过使用简化集映射（PARS）算法进行峰对齐处理核磁共振（NMR）数据以及使用数据的稀疏表示，原始NMR数据中包含的信息得以保留，分辨率得以维持，同时避免了峰位移问题。我们现在可以描述一种利用先验知识（即给药开始时间）对NMR谱进行差异表达分析的方法，这是使用原始数据或桶状数据无法实现的划分。此外，我们还概述了一种从对齐数据中软去除“生物噪声”的方案：穷举生物噪声减法（EBS）。结果是得到了一个用于检测由药物反应导致出现的峰的直接方案。换句话说，有可能阐明峰的来源，无论是内源性物质还是给药药物/生物标志物。此外，源自正常调节代谢组的数据划分可以在没有叠加生物噪声的情况下进行分析。所提出的方案提高了处理后数据的可解释性，即得到更精细的代谢轨迹、简化了一致生物标志物的检测以及简化了对给药药物代谢终产物的搜索。
