Workflow for Evaluating Normalization Tools for Omics Data Using Supervised and Unsupervised Machine Learning
Workflow for Evaluating Normalization Tools for Omics Data Using Supervised and Unsupervised Machine Learning
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DOI：
10.1021/jasms.3c00295
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发表时间：
2023-10-28
影响因子：
3.2
通讯作者：
Desaire，Heather
中科院分区：
文献类型：
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作者：
Chua，Aleesa E.;Pfeifer，Leah D.;Desaire，Heather
为了获得高质量的组学结果，必须充分处理质谱学(MS)数据中的系统变异性。有效的数据标准化对于将这种可变性降至最低至关重要。方法的丰富性和归一化对数据的依赖性使一些研究人员开发了用于选择最佳方法的开源学术软件。虽然这些工具肯定对社区有益，但它们都不能满足所有用户的所有需求，尤其是那些想要测试这些产品中没有的新策略的用户。在这里，我们提出了一个简单而直接的工作流程，它使用直接的评估度量，使用监督和非监督机器学习来促进识别最优归一化策略。该工作流程提供了一个“DIY”方面，可以针对任何类型的MS数据评估任何标准化策略的性能。作为其实用性的演示，我们将该工作流应用于两个不同的数据集，一个是从潜在指纹中提取脂质的ESI-MS数据集，另一个是由MALDI-MSI电离的代谢物的癌症球体数据集，对于这两个数据集，我们确定了执行最好的归一化策略。
To achieve high quality omics results, systematic variability in mass spectrometry (MS) data must be adequately addressed. Effective data normalization is essential for minimizing this variability. The abundance of approaches and the data-dependent nature of normalization have led some researchers to develop open-source academic software for choosing the best approach. While these tools are certainly beneficial to the community, none of them meet all of the needs of all users, particularly users who want to test new strategies that are not available in these products. Herein, we present a simple and straightforward workflow that facilitates the identification of optimal normalization strategies using straightforward evaluation metrics, employing both supervised and unsupervised machine learning. The workflow offers a “DIY” aspect, where the performance of any normalization strategy can be evaluated for any type of MS data. As a demonstration of its utility, we apply this workflow on two distinct datasets, an ESI-MS dataset of extracted lipids from latent fingerprints and a cancer spheroid dataset of metabolites ionized by MALDI-MSI, for which we identified the best-performing normalization strategies.