State-of-the art data normalization methods improve NMR-based metabolomic analysis.

ABSTRACT: Extracting biomedical information from large metabolomic datasets by multivariate data analysis is of considerable complexity. Common challenges include among others screening for differentially produced metabolites, estimation of fold changes, and sample classification. Prior to these analysis steps, it is important to minimize contributions from unwanted biases and experimental variance. This is the goal of data preprocessing. In this work, different data normalization methods were compared systematically employing two different datasets generated by means of nuclear magnetic resonance (NMR) spectroscopy. To this end, two different types of normalization methods were used, one aiming to remove unwanted sample-to-sample variation while the other adjusts the variance of the different metab

SUBMITTER: Kohl SM 

PROVIDER: S-EPMC3337420 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature