title: Correlation Patterns in Experimental Data Are Affected by Normalization Procedures: Consequences for Data Analysis and Network Inference.
abstract: Normalization is a fundamental step in data processing to account for the sample-to-sample variation observed in biological samples. However, data structure is affected by normalization. In this paper, we show how, and to what extent, the correlation structure is affected 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 association networks.
authors.affiliation: Laboratory of Systems and Synthetic Biology, Wageningen University , Stippeneng 4 6708 HB Wageningen, The Netherlands.
authors.forename: Edoardo
authors.initials: E
authors.lastname: Saccenti
mesh_terms: D000818:Animals; D016018:Least-Squares Analysis; D055442:Metabolome / Q000235:genetics*; D025341:Principal Component Analysis; D020543:Proteome / Q000737:chemistry / Q000235:genetics / Q000592:standards*; D040901:Proteomics / Q000592:standards / Q000706:statistics & numerical data*; D013552:Swine; D014556:Urine / Q000737:chemistry
chemical_list: D020543:Proteome
keywords: COVSCA; NMR; PCA; PLS-DA; covariance analysis; sample-to-sample variation; spurious correlation; urine dilution