MetaFIND: a feature analysis tool for metabolomics data.
MetaFIND: a feature analysis tool for metabolomics data.
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DOI：
10.1186/1471-2105-9-470
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发表时间：
2008-11-05
影响因子：
3
通讯作者：
Cunningham P
中科院分区：
文献类型：
--
作者：
Bryan K;Brennan L;Cunningham P
代谢组学（Metabolomics）是指对生物样品中存在的所有代谢物进行定量分析，通常使用NMR光谱法或质谱法进行。这种分析产生一组峰或特征，指示样品的代谢组成，并且可以用作样品分类的基础。特征选择可用于通过建立类别区分特征的子集来提高分类准确性或辅助模型解释。实验噪声、技术选择和阈值选择等因素可能会对检索到的选定特征集产生不利影响。此外，代谢组学数据中固有的高维数和多重共线性可能会加剧检索的特征集与提供代谢物签名的完整解释所需的特征集之间的差异。鉴于这些问题，特别是后者，我们提出了MetaFIND应用程序的代谢组学数据的“后特征选择”相关性分析。在我们的评估中，我们展示了MetaFIND如何用于从两个代谢组学数据集上通过不同技术选择的一组特征中阐明代谢物特征。重要的是，我们还展示了MetaFIND如何增强标准特征选择，并帮助发现其他重要特征，包括那些代表新类别鉴别代谢物的特征。MetaFIND还支持发现更高水平的代谢物相关性。在高维、多共线代谢组学数据的情况下，标准特征选择技术可能无法捕获完整的相关特征集。我们表明，MetaFIND的“后特征选择”分析工具，可以帮助代谢物的签名说明，特征发现和代谢相关性的推断。
Metabolomics, or metabonomics, refers to the quantitative analysis of all metabolites present within a biological sample and is generally carried out using NMR spectroscopy or Mass Spectrometry. Such analysis produces a set of peaks, or features, indicative of the metabolic composition of the sample and may be used as a basis for sample classification. Feature selection may be employed to improve classification accuracy or aid model explanation by establishing a subset of class discriminating features. Factors such as experimental noise, choice of technique and threshold selection may adversely affect the set of selected features retrieved. Furthermore, the high dimensionality and multi-collinearity inherent within metabolomics data may exacerbate discrepancies between the set of features retrieved and those required to provide a complete explanation of metabolite signatures. Given these issues, the latter in particular, we present the MetaFIND application for 'post-feature selection' correlation analysis of metabolomics data. In our evaluation we show how MetaFIND may be used to elucidate metabolite signatures from the set of features selected by diverse techniques over two metabolomics datasets. Importantly, we also show how MetaFIND may augment standard feature selection and aid the discovery of additional significant features, including those which represent novel class discriminating metabolites. MetaFIND also supports the discovery of higher level metabolite correlations. Standard feature selection techniques may fail to capture the full set of relevant features in the case of high dimensional, multi-collinear metabolomics data. We show that the MetaFIND 'post-feature selection' analysis tool may aid metabolite signature elucidation, feature discovery and inference of metabolic correlations.
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影响因子：
4.4
作者：
Holmes, E.;Cloarec, O.;Nicholson, J. K.
通讯作者：
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DOI：
10.1016/s0169-7439(98)00110-5
发表时间：
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