Statistical quality assessment and outlier detection for liquid chromatography-mass spectrometry experiments.

Statistical quality assessment and outlier detection for liquid chromatography-mass spectrometry experiments.
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液态色谱 - 质谱实验的统计质量评估和离群值检测。

DOI:
10.1186/1756-0381-2-4
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发表时间:
2009-04-07
期刊:
影响因子:
4.5
通讯作者:
Unger K
Unger K
中科院分区:
生物学3区
文献类型:
--
作者:
Schulz-Trieglaff O;Machtejevas E;Reinert K;Schlüter H;Thiemann J;Unger K

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质量评价方法在工程和工业生产中很常见,但在大规模蛋白质组学实验中应用并不广泛。但是现代技术如多维液相色谱联用质谱(LC-MS)产生了大量的蛋白质组学数据。这些数据容易出现测量误差和再现性问题,因此自动质量评估和控制变得越来越重要。我们提出了一种方法来评估定量LC-MS实验中产生的数据的质量和可重复性。我们引入质量描述符,捕获LC-MS数据集的质量和可重复性的不同方面。我们的方法是基于马氏距离和一个稳健的主成分分析。我们在不同复杂性的几个数据集上评估了我们的方法,并表明我们能够在大规模研究中精确检测信号质量差的LC-MS运行。
Quality assessment methods, that are common place in engineering and industrial production, are not widely spread in large-scale proteomics experiments. But modern technologies such as Multi-Dimensional Liquid Chromatography coupled to Mass Spectrometry (LC-MS) produce large quantities of proteomic data. These data are prone to measurement errors and reproducibility problems such that an automatic quality assessment and control become increasingly important. We propose a methodology to assess the quality and reproducibility of data generated in quantitative LC-MS experiments. We introduce quality descriptors that capture different aspects of the quality and reproducibility of LC-MS data sets. Our method is based on the Mahalanobis distance and a robust Principal Component Analysis. We evaluate our approach on several data sets of different complexities and show that we are able to precisely detect LC-MS runs of poor signal quality in large-scale studies.
DOI: 10.1186/1471-2105-9-163
发表时间: 2008-03-26
期刊: BMC bioinformatics
影响因子: 3
作者:
Sturm M;Bertsch A;Gröpl C;Hildebrandt A;Hussong R;Lange E;Pfeifer N;Schulz-Trieglaff O;Zerck A;Reinert K;Kohlbacher O
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