Data integration in plant biology:: the O2PLS method for combined modeling of transcript and metabolite data
Data integration in plant biology:: the O2PLS method for combined modeling of transcript and metabolite data
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DOI：
10.1111/j.1365-313x.2007.03293.x
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发表时间：
2007-12-01
期刊：
影响因子：
7.2
通讯作者：
Trygg, Johan
中科院分区：
文献类型：
--
作者：
Bylesjo, Max;Eriksson, Daniel;Trygg, Johan
生命科学中使用的仪器的技术进步使得从多个来源收集几乎无限数量的数据成为可能。通过从多个分析平台收集数据，目的是并行监测，例如转录组学、代谢组学或蛋白质组学事件，人们希望回答和理解生物学问题和观察结果。这种“系统生物学”方法通常涉及高级统计学以促进数据的解释。在本研究中，我们证明 O2PLS 多元回归方法可用于组合“组学”类型的数据。通过这种方法，可以将跨分析平台重叠的系统变异与平台特定的系统变异分开。采用美洲山杨 x 美洲山杨的研究，调查了短日照在转录物和代谢物水平上引起的影响，以证明该方法的好处。我们展示了如何验证和解释模型以识别生物学相关事件，并讨论与成对单变量相关方法和主成分分析相关的结果。
The technological advances in the instrumentation employed in life sciences have enabled the collection of a virtually unlimited quantity of data from multiple sources. By gathering data from several analytical platforms, with the aim of parallel monitoring of, e.g. transcriptomic, metabolomic or proteomic events, one hopes to answer and understand biological questions and observations. This 'systems biology' approach typically involves advanced statistics to facilitate the interpretation of the data. In the present study, we demonstrate that the O2PLS multivariate regression method can be used for combining 'omics' types of data. With this methodology, systematic variation that overlaps across analytical platforms can be separated from platform-specific systematic variation. A study of Populus tremula x Populus tremuloides, investigating short-day-induced effects at transcript and metabolite levels, is employed to demonstrate the benefits of the methodology. We show how the models can be validated and interpreted to identify biologically relevant events, and discuss the results in relation to a pairwise univariate correlation approach and principal component analysis.