Simple data-reduction method for high-resolution LC-MS data in metabolomics
Simple data-reduction method for high-resolution LC-MS data in metabolomics
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DOI：
10.4155/bio.09.146
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发表时间：
2009-12-01
期刊：
影响因子：
1.8
通讯作者：
Breitling, R.
中科院分区：
文献类型：
--
作者：
Scheltema, R. A.;Decuypere, S.;Breitling, R.
背景：代谢组学LC-MS实验产生大量峰，其中很少可以通过数据库匹配识别。许多剩余的峰对应于所识别的峰的导数（例如，同位素峰、加合物、碎片和多电荷分子）。在这篇文章中，我们提出了一个数据减少的方法，自动识别这些衍生峰。结果如下：使用基于色谱峰形相关性和生物重复样品之间的强度模式的数据驱动聚类，可以可靠地识别衍生峰。使用从杜氏利什曼原虫提取物中获得的测试数据集，我们实现了峰数减少60%。经过质量控制过滤后，几乎80%的峰可以通过数据库匹配来识别。结论：自动峰过滤大大加快了数据解释过程。
Background: Metabolomics LC-MS experiments yield large numbers of peaks, few of which can be identified by database matching. Many of the remaining peaks correspond to derivatives of identified peaks (e.g., isotope peaks, adducts, fragments and multiply charged molecules). In this article, we present a data-reduction approach that automatically identifies these derivative peaks. Results: Using data-driven clustering based on chromatographic peak shape correlation and intensity patterns across biological replicates, derivative peaks can be reliably identified. Using a test data set obtained from Leishmania donovani extracts, we achieved a 60% reduction of the number of peaks. After quality control filtering, almost 80% of the peaks could putatively be identified by database matching. Conclusion: Automated peak filtering substantially speeds up the data-interpretation process.