Correlation Patterns in Experimental Data Are Affected by Normalization Procedures: Consequences for Data Analysis and Network Inference

Normalization is a fundamental step in data processingto account for the sample-to-sample variation observed in biologicalsamples. However, data structure is affected by normalization. In thispaper, we show how, and to what extent, the correlation structure isaffected by the application of 11 different normalization procedures.We also discuss the consequences for data analysis and interpretation,including principal component analysis, partial least-squares discrimination,and the inference of metabolite−metabolite associationnetworks