Department of Biostatistics, 410 West 10th st., Indianapolis, IN 46202, USA.
BMC Bioinformatics. 2013 Apr 10;14:123. doi: 10.1186/1471-2105-14-123.
Since peak alignment in metabolomics has a huge effect on the subsequent statistical analysis, it is considered a key preprocessing step and many peak alignment methods have been developed. However, existing peak alignment methods do not produce satisfactory results. Indeed, the lack of accuracy results from the fact that peak alignment is done separately from another preprocessing step such as identification. Therefore, a post-hoc approach, which integrates both identification and alignment results, is in urgent need for the purpose of increasing the accuracy of peak alignment.
The proposed post-hoc method was validated with three datasets such as a mixture of compound standards, metabolite extract from mouse liver, and metabolite extract from wheat. Compared to the existing methods, the proposed approach improved peak alignment in terms of various performance measures. Also, post-hoc approach was verified to improve peak alignment by manual inspection.
The proposed approach, which combines the information of metabolite identification and alignment, clearly improves the accuracy of peak alignment in terms of several performance measures. R package and examples using a dataset are available at http://mrr.sourceforge.net/download.html.
由于代谢组学中的峰对齐对后续的统计分析有巨大影响，因此它被视为关键的预处理步骤，已经开发出了许多峰对齐方法。然而，现有的峰对齐方法并没有产生令人满意的结果。事实上，这种不准确性源于峰对齐是与另一个预处理步骤（如鉴定）分开进行的事实。因此，为了提高峰对齐的准确性，迫切需要一种后处理方法，该方法可以整合鉴定和对齐的结果。
使用三种数据集（如化合物标准混合物、小鼠肝提取物和小麦提取物）验证了所提出的后处理方法。与现有的方法相比，该方法在各种性能指标上都提高了峰对齐的准确性。此外，通过手动检查验证了后处理方法可以改善峰对齐。
所提出的方法结合了代谢物鉴定和对齐的信息，明显提高了几个性能指标的峰对齐准确性。可在 http://mrr.sourceforge.net/download.html 获得用于数据集的 R 包和示例。
