Variance component analysis to assess protein quantification in biomarker validation: application to selected reaction monitoring-mass spectrometry

Amna Klich,Pierre Mahé,Jean-Philippe Charrier,Pierre Grangeat,Corinne Beaulieu,Pascal Roy,Elodie Degout-Charmette,Catherine Mercier,Laurent Gerfault,Audrey Giremus,Delphine Maucort-Boulch,Jean-François Giovannelli,Tanguy Fortin

doi:10.1186/s12859-018-2075-8

Abstract

BackgroundIn 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.ResultsWe 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.ConclusionsThe extension of the variance component analysis was able to estimate correctly the variance components of protein concentration measurement in case of unbalanced design.

Highlights

In the field of biomarker validation with mass spectrometry, controlling the technical variability is a critical issue
With Bayesian Hierarchical Algorithm (BHI), we have introduced a protocol for estimating the digestion yields
Among all results obtained for all protein reads, the correlation coefficient between the theoretical concentration and the measured protein concentration was ≥0.7 in 9 out of 21 proteins: Liver-Fatty Acid Binding Protein (L-FABP), 14.3.3 sigma, Calgizzarin or S100 A11 (Calgi), Def.A6, Villin, Calmo, Intestinal-Fatty Acid Binding Protein (I-FABP), Peroxi-5, and S100 calcium-binding protein A14 (S100A14) (Additional files 3 and 4)

Summary

Introduction

In the field of biomarker validation with mass spectrometry, controlling the technical variability is a critical issue. In case of unbalanced data (unequal number of observations in all factor combinations), the classical methods cannot correctly estimate the various sources of variability, 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. Because of the random sampling of the proteome within populations and the high false discovery rates, it became necessary to validate candidate biomarkers through quantitative assays [1]. The limits with ELISA are the restricted possibility of performing multiple assays, the unavailability of antibodies for every new candidate biomarker, and the long and expensive developments of new assays [2]. Eckel-Passow et al [4] have discussed the difficulties of achieving good repeatability and reproducibility in MS and expressed the need

Methods

Results

Conclusion