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Peptide-level Robust Ridge Regression Improves Estimation, Sensitivity, and Specificity in Data-dependent Quantitative Label-free Shotgun Proteomics.

Authors
  • Goeminne, Ludger J E1
  • Gevaert, Kris2
  • Clement, Lieven3
  • 1 From the ‡Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Belgium; §VIB Medical Biotechnology Center, Ghent University, Belgium; ¶Department of Biochemistry, Ghent University, Belgium. , (Belgium)
  • 2 §VIB Medical Biotechnology Center, Ghent University, Belgium; ¶Department of Biochemistry, Ghent University, Belgium. , (Belgium)
  • 3 From the ‡Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Belgium; [email protected] [email protected] [email protected] , (Belgium)
Type
Published Article
Journal
Molecular & Cellular Proteomics
Publisher
American Society for Biochemistry and Molecular Biology
Publication Date
Feb 01, 2016
Volume
15
Issue
2
Pages
657–668
Identifiers
DOI: 10.1074/mcp.M115.055897
PMID: 26566788
Source
Medline
License
Unknown

Abstract

Peptide intensities from mass spectra are increasingly used for relative quantitation of proteins in complex samples. However, numerous issues inherent to the mass spectrometry workflow turn quantitative proteomic data analysis into a crucial challenge. We and others have shown that modeling at the peptide level outperforms classical summarization-based approaches, which typically also discard a lot of proteins at the data preprocessing step. Peptide-based linear regression models, however, still suffer from unbalanced datasets due to missing peptide intensities, outlying peptide intensities and overfitting. Here, we further improve upon peptide-based models by three modular extensions: ridge regression, improved variance estimation by borrowing information across proteins with empirical Bayes and M-estimation with Huber weights. We illustrate our method on the CPTAC spike-in study and on a study comparing wild-type and ArgP knock-out Francisella tularensis proteomes. We show that the fold change estimates of our robust approach are more precise and more accurate than those from state-of-the-art summarization-based methods and peptide-based regression models, which leads to an improved sensitivity and specificity. We also demonstrate that ionization competition effects come already into play at very low spike-in concentrations and confirm that analyses with peptide-based regression methods on peptide intensity values aggregated by charge state and modification status (e.g. MaxQuant's peptides.txt file) are slightly superior to analyses on raw peptide intensity values (e.g. MaxQuant's evidence.txt file).

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