May Jody C, McLean John A
Department of Chemistry, Center for Innovative Technology, Vanderbilt Institute for Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, Tennessee 37235; email:
Annu Rev Anal Chem (Palo Alto Calif). 2016 Jun 12;9(1):387-409. doi: 10.1146/annurev-anchem-071015-041734. Epub 2016 Mar 30.
Hybrid analytical instrumentation constructed around mass spectrometry (MS) is becoming the preferred technique for addressing many grand challenges in science and medicine. From the omics sciences to drug discovery and synthetic biology, multidimensional separations based on MS provide the high peak capacity and high measurement throughput necessary to obtain large-scale measurements used to infer systems-level information. In this article, we describe multidimensional MS configurations as technologies that are big data drivers and review some new and emerging strategies for mining information from large-scale datasets. We discuss the information content that can be obtained from individual dimensions, as well as the unique information that can be derived by comparing different levels of data. Finally, we summarize some emerging data visualization strategies that seek to make highly dimensional datasets both accessible and comprehensible.
围绕质谱(MS)构建的混合分析仪器正成为应对科学和医学中诸多重大挑战的首选技术。从组学科学到药物发现和合成生物学,基于质谱的多维分离提供了获得用于推断系统级信息的大规模测量所需的高分离度和高测量通量。在本文中,我们将多维质谱配置描述为大数据驱动技术,并回顾一些从大规模数据集中挖掘信息的新出现策略。我们讨论了可从各个维度获得的信息内容,以及通过比较不同数据水平可得出的独特信息。最后,我们总结了一些新兴的数据可视化策略,这些策略旨在使高维数据集易于访问和理解。