Zacharias, H.U.; Altenbuchinger, M.; Gronwald, W. Statistical Analysis of NMR Metabolic Fingerprints: Established Methods and Recent Advances. Metabolites2018, 8, 47.
Zacharias, H.U.; Altenbuchinger, M.; Gronwald, W. Statistical Analysis of NMR Metabolic Fingerprints: Established Methods and Recent Advances. Metabolites 2018, 8, 47.
Zacharias, H.U.; Altenbuchinger, M.; Gronwald, W. Statistical Analysis of NMR Metabolic Fingerprints: Established Methods and Recent Advances. Metabolites2018, 8, 47.
Zacharias, H.U.; Altenbuchinger, M.; Gronwald, W. Statistical Analysis of NMR Metabolic Fingerprints: Established Methods and Recent Advances. Metabolites 2018, 8, 47.
Abstract
The aim of this article is to summarize recent bioinformatic and statistical developments applicable to NMR-based metabolomics. Extracting relevant information from large multivariate datasets by statistical data analysis strategies may be of considerable complexity. Typical tasks comprise for example classification of specimens, identification of differentially produced metabolites, and estimation of fold changes. In this context it is of prime importance to minimize contributions from unwanted biases and experimental variance prior to these analyses. This is the goal of data normalization. Therefore, special emphasize is given to different data normalization strategies. In the first part, we will discuss the requirements and the pros and cons for a variety of commonly applied strategies. In the second part, we will concentrate on possible solutions in case that the requirements for the standard strategies are not fulfilled. In the last part, very recent developments will be discussed that allow reliable estimation of metabolic signatures for sample classification without prior data normalization. In this contribution special emphasis will be given to techniques that have worked well in our hands.
Keywords
data normalization; data scaling; zero-sum; metabolic fingerprinting; NMR; statistical data analysis
Subject
Biology and Life Sciences, Biophysics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
