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

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.

[1]  S. Weisberg.  Applied Linear Regression: Weisberg/Applied Linear Regression 3e , 2005 .

[2]  S. von Felten,et al.  Analysis of variance with unbalanced data: an update for ecology & evolution. , 2010, The Journal of animal ecology.

[3]  Mark I. Appelbaum,et al.  Nonorthogonal analysis of variance--once again. , 1980 .

[4]  Steven A Carr,et al.  Protein biomarker discovery and validation: the long and uncertain path to clinical utility , 2006, Nature Biotechnology.

[5]  Pascal Szacherski,et al.  Reconstruction de profils protéiques pour la recherche de biomarqueurs. (Reconstruction of proteomic profiles for biomarker discovery) , 2012 .

[6]  T M Therneau,et al.  An insight into high-resolution mass-spectrometry data. , 2009, Biostatistics.

[7]  John A. Nelder,et al.  The statistics of linear models: back to basics , 1995 .

[8]  Xingdong Feng,et al.  Variance Component Analysis of a Multi-Site Study for the Reproducibility of Multiple Reaction Monitoring Measurements of Peptides in Human Plasma , 2011, PloS one.

[9]  Malik Beshir Malik,et al.  Applied Linear Regression , 2005, Technometrics.

[10]  Jean-Philippe Charrier,et al.  Impact of Serum and Plasma Matrices on the Titration of Human Inflammatory Biomarkers Using Analytically Validated SRM Assays. , 2016, Journal of proteome research.

[11]  Jean-Philippe Charrier,et al.  The current status of clinical proteomics and the use of MRM and MRM3 for biomarker validation , 2012, Expert review of molecular diagnostics.

[12]  Jonas Grossmann,et al.  Implementation and evaluation of relative and absolute quantification in shotgun proteomics with label-free methods. , 2010, Journal of proteomics.

[13]  Rafael A Irizarry,et al.  Exploration, normalization, and summaries of high density oligonucleotide array probe level data. , 2003, Biostatistics.

[14]  J. Garin,et al.  Isotope dilution strategies for absolute quantitative proteomics. , 2009, Journal of proteomics.

[15]  Laurent Gerfault,et al.  Bayesian hierarchical reconstruction of protein profiles including a digestion model , 2011, 1202.4868.

[16]  R. Beynon,et al.  Multiplexed absolute quantification in proteomics using artificial QCAT proteins of concatenated signature peptides , 2005, Nature Methods.

[17]  Pierre Grangeat,et al.  A hierarchical SRM acquisition chain model for improved protein quantification in serum samples , 2012, RECOMB 2012.

[18]  John A. Nelder,et al.  The Computer Analysis of Factorial Experiments: In Memoriam—Frank Yates , 1995 .

[19]  Pierre Grangeat,et al.  Classification of Proteomic MS Data as Bayesian Solution of an Inverse Problem , 2014, IEEE Access.

[20]  S. Weisberg,et al.  Applied Linear Regression (2nd ed.). , 1986 .

[21]  Ruedi Aebersold,et al.  Protein Significance Analysis in Selected Reaction Monitoring (SRM) Measurements* , 2011, Molecular & Cellular Proteomics.

[22]  Pierre Grangeat,et al.  MRM protein quantification and serum sample classification , 2013 .

[23]  Øyvind Langsrud,et al.  ANOVA for unbalanced data: Use Type II instead of Type III sums of squares , 2003, Stat. Comput..

[24]  Christoph H Borchers,et al.  Multi-site assessment of the precision and reproducibility of multiple reaction monitoring–based measurements of proteins in plasma , 2009, Nature Biotechnology.

[25]  R. Aebersold,et al.  Selected reaction monitoring for quantitative proteomics: a tutorial , 2008, Molecular systems biology.