Proposed minimum reporting standards for data analysis in metabolomics
Proposed minimum reporting standards for data analysis in metabolomics
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DOI：
10.1007/s11306-007-0081-3
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发表时间：
2007-09-01
期刊：
影响因子：
3.6
通讯作者：
Wulfert, Florian
中科院分区：
文献类型：
--
作者：
Goodacre, Royston;Broadhurst, David;Wulfert, Florian
该组的目标是定义与代谢物数据相对于其他测量/收集的实验数据（通常称为元数据）的统计分析（包括单变量，多变量，信息学，机器学习等）相关的报告要求。目前，这些定义将尽可能多地包含完整代谢组学研究的各个方面。按时间顺序，这将包括：实验设计，包括样品收集/匹配，以及通过使用的任何光谱技术进行样品的数据采集调度；反卷积（如有需要）；预处理，例如数据清理、异常值检测、行/列缩放或其他转换；后续可视化的定义和参数化以及应用于数据集的统计/机器学习方法；如果需要，使用的模型验证方案的明确定义（包括如何将数据分成训练/验证/测试集）；关于数据分析是否经过独立测试的正式指示（通过实验再现或盲举测试集）。最后是数据解释以及从数据分析中得到的可视化表示和假设。
The goal of this group is to define the reporting requirements associated with the statistical analysis (including univariate, multivariate, informatics, machine learning etc.) of metabolite data with respect to other measured/collected experimental data (often called metadata). These definitions will embrace as many aspects of a complete metabolomics study as possible at this time. In chronological order this will include: Experimental Design, both in terms of sample collection/matching, and data acquisition scheduling of samples through whichever spectroscopic technology used; Deconvolution (if required); Pre-processing, for example, data cleaning, outlier detection, row/column scaling, or other transformations; Definition and parameterization of subsequent visualizations and Statistical/Machine learning Methods applied to the dataset; If required, a clear definition of the Model Validation Scheme used (including how data are split into training/validation/test sets); Formal indication on whether the data analysis has been Independently Tested (either by experimental reproduction, or blind hold out test set). Finally, data interpretation and the visual representations and hypotheses obtained from the data analyses.