Parameter Optimization for Feature and Hit Generation in a General Unknown Screening Method-Proof of Concept Study Using a Design of Experiment Approach for a High Resolution Mass Spectrometry Procedure after Data Independent Acquisition
Parameter Optimization for Feature and Hit Generation in a General Unknown Screening Method-Proof of Concept Study Using a Design of Experiment Approach for a High Resolution Mass Spectrometry Procedure after Data Independent Acquisition
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DOI：
10.1021/acs.analchem.7b05387
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发表时间：
2018-03-06
影响因子：
7.4
通讯作者：
Kraemer, Thomas
中科院分区：
文献类型：
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作者：
Elmiger, Marco P.;Poetzsch, Michael;Kraemer, Thomas
高分辨率质谱和现代数据独立采集（DIA）方法使得能够创建通用未知筛选（GUS）程序。然而，即使使用DIA，其潜力也远未得到开发，因为通常，非目标性获取之后是有针对性的搜索。应用实际GUS（包括非靶向筛选）会产生大量必须处理的数据。对调节数据处理软件的特征检测和命中生成算法的参数进行优化可以显著减少不必要的数据量，从而减少工作量。实验设计（DoE）方法允许同时优化多个参数。在第一步中，评估参数（关键或非关键）。第二，优化关键参数。本研究的目的是减少命中次数，而不丢失分析物。通过分析掺有22种相关分析物以及62个真实法医案例的血液样本的测试集，将从优化获得的参数设置与标准设置进行比较。优化导致显着减少的工作量（12.3至1.1%和3.8至1.1%的测试集和真实的情况下，分别命中），同时提高识别率（68.2至86.4%和68.8至88.1%，分别）。该概念验证研究强调了DoE方法的巨大潜力，以通过优化软件参数来掌握用于一般未知筛选程序的现代数据独立采集方法所导致的数据过载。
High resolution mass spectrometry and modern data independent acquisition (DIA) methods enable the creation of general unknown screening (GUS) procedures. However, even when DIA is used, its potential is far from being exploited, because often, the untargeted acquisition is followed by a targeted search. Applying an actual GUS (including untargeted screening) produces an immense amount of data that must be dealt with. An optimization of the parameters regulating the feature detection and hit generation algorithms of the data processing software could significantly reduce the amount of unnecessary data and thereby the workload. Design of experiment (DoE) approaches allow a simultaneous optimization of multiple parameters. In a first step, parameters are evaluated (crucial or noncrucial). Second, crucial parameters are optimized. The aim in this study was to reduce the number of hits, without missing analytes. The obtained parameter settings from the optimization were compared to the standard settings by analyzing a test set of blood samples spiked with 22 relevant analytes as well as 62 authentic forensic cases. The optimization lead to a marked reduction of workload (12.3 to 1.1% and 3.8 to 1.1% hits for the test set and the authentic cases, respectively) while simultaneously increasing the identification rate (68.2 to 86.4% and 68.8 to 88.1%, respectively). This proof of concept study emphasizes the great potential of DoE approaches to master the data overload resulting from modern data independent acquisition methods used for general unknown screening procedures by optimizing software parameters.