Hospices Civils de Lyon, Service de Biostatistique-Bioinformatique, 162, avenue Lacassagne, Lyon, France, Université de Lyon, Lyon, France, PRABI, Université Lyon 1, Villeurbanne, France, CNRS UMR 5558, LBBE, Équipe Biostatistique Santé, Villeurbanne, France, Université Grenoble-Alpes, Grenoble, France, Commissariat à l'Énergie Atomique, Laboratoire d'Électronique et de Technologie de l'Information,MINATEC Campus, Département Micro-technologies pour la Biologie et la Santé, Grenoble, France, Innovation Unit, Technology Research Department, BioMérieux, Marcy l'Étoile, France, Innovation Unit, Technology Research Department, BioMérieux, Grenoble, France, Intégration du Matériau au Système (Université de Bordeaux, CNRS, Bordeaux Aquitaine INP), Talence, France, Villeurbanne, France

Background: In the field of biomarker validation with mass spectrometry, controlling the technical variability is a critical issue. In selected reaction monitoring (SRM) measurements, this issue provides the opportunity of using variance component analysis to distinguish various sources of variability. However, in case of unbalanced data (unequal number of observations in all factor combinations), the classical methods cannot correctly estimate the various sources of variability, particularly in presence of interaction. The present paper proposes an extension of the variance component analysis to estimate the various components of the variance, including an interaction component in case of unbalanced data. Results: We applied an experimental design that uses a serial dilution to generate known relative protein concentrations and estimated these concentrations by two processing algorithms, a classical and a more recent one. The extended method allowed estimating the variances explained by the dilution and the technical process by each algorithm in an experiment with 9 proteins: L-FABP, 14.3.3 sigma, Calgi, Def.A6, Villin, Calmo, I-FABP, Peroxi-5, and S100A14. Whereas, the recent algorithm gave a higher dilution variance and a lower technical variance than the classical one in two proteins with three peptides (L-FABP and Villin), there were no significant difference between the two algorithms on all proteins. Conclusions: The extension of the variance component analysis was able to estimate correctly the variance components of protein concentration measurement in case of unbalanced design. © 2018 The Author(s).

Experimental design, Mass spectrometry, SRM, Technical variability, Validation biomarkers, Variance component analysis

Bioinformatics, Design of experiments, Mass spectrometry, Proteins, Spectrometry, Statistics, Processing algorithms, Protein concentrations, Protein quantification, Selected reaction monitoring, Sources of variability, Technical variability, Variance component analysis, Variance components, Biomarkers