title: What can go wrong at the data normalization step for identification of biomarkers?
abstract: Our study focuses on the removal of the so-called size effect, related to a different sample volume and/or concentration. This effect is associated with many types of instrumental signals, particularly with those originating from HPLC-DAD, LC-MS, and UPLC-MS. These signals do not carry any absolute information about the sample components. If the data comparison has to be performed based on sample fingerprints, then the size effect is undesired, and the shape effect is of main interest. With "shape", we refer to data information which is contained in the ratios between the variables. So far, different normalization methods have been applied to the removal of size effect. In our study, the performance of popular normalization methods is compared with those of the CODA (Compositional Data Analysis) methods, relying on log-ratio transformations, and the performance is evaluated through the prism of proper identification of biomarkers.
authors.affiliation: Department of Statistics and Probability Theory, Vienna University of Technology, Vienna, Austria.
authors.forename: P
authors.initials: P
authors.lastname: Filzmoser
authors.affiliation: Department of Analytical Chemistry, Institute of Chemistry, University of Silesia, Katowice, Poland. Electronic address: beata.walczak@us.edu.pl.
authors.forename: B
authors.initials: B
authors.lastname: Walczak
mesh_terms: D015415:Biomarkers / Q000032:analysis; D002853:Chromatography, Liquid / Q000295:instrumentation / Q000379:methods*; D003198:Computer Simulation; D013058:Mass Spectrometry / Q000379:methods
chemical_list: D015415:Biomarkers
keywords: Compositional data analysis.; Fingerprints; Log-ratio methodology; Shape effect; Size effect