Normalizing and Integrating Metabolomics Data

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• 作者：Alysha M. De Livera ; Daniel A. Dias ; David De Souza ; Thusitha Rupasinghe ; James Pyke ; Dedreia Tull ; Ute Roessner ; Malcolm McConville ; Terence P. Speed
• 刊名：Analytical Chemistry
• 出版年：2012
• 出版时间：December 18, 2012
• 年：2012
• 卷：84
• 期：24
• 页码：10768-10776
• 全文大小：432K
• 年卷期：v.84,no.24(December 18, 2012)
• ISSN：1520-6882

文摘

Metabolomics research often requires the use of multiple analytical platforms, batches of samples, and laboratories, any of which can introduce a component of unwanted variation. In addition, every experiment is subject to within-platform and other experimental variation, which often includes unwanted biological variation. Such variation must be removed in order to focus on the biological information of interest. We present a broadly applicable method for the removal of unwanted variation arising from various sources for the identification of differentially abundant metabolites and, hence, for the systematic integration of data on the same quantities from different sources. We illustrate the versatility and the performance of the approach in four applications, and we show that it has several advantages over the existing normalization methods.