Electronic Journal of Statistics

Proteomics profiles from mass spectrometry

Inge Koch, Peter Hoffmann, and J. S. Marron

Full-text: Open access


Proteomics is a rapidly growing research area within bioinformatics which focuses on quantification of peptide concentrations and on the identification of proteins and peptides. In quantitative proteomics the identification of biomarkers from peptide concentrations is important for diagnostic purposes and treatment of diseases.

The goal of this paper is to facilitate research in this area, by providing a test bed for comparison of 1D curve registration methods. This is done in a novel way, by providing not only curves, but also an answer key as to how the peaks should align. In the following papers a number of approaches to this problem are given, and the answer key provides unusually useful insights into how the methods compare.

For this reason, we consider proteomics mass spectrometry profiles which are part of a larger study into the identification of biomarkers in Acute Myeloid Leukaemia (AML). For these profiles large ion counts result in large peaks, but these peaks may occur at different retention times for different profiles. The first step in the quantification of peptides in proteomics profiles is the alignment of the 1D curves of total ion count (TIC).

The paper includes a description of proteomics mass spectrometry profiling, and considers profiles from five patients with AML. It outlines the preprocessing steps we applied to the multiple TIC samples from each patient, and introduces the reference peptides. The retention times of the reference peptides are known for each profile, and using these times as an answer key makes the 1D TIC curves a particularly informative test bed for curve registration.

Article information

Electron. J. Statist., Volume 8, Number 2 (2014), 1703-1713.

First available in Project Euclid: 29 October 2014

Permanent link to this document

Digital Object Identifier

Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Mass spectrometry one-dimensional curve registration peak alignment proteomics total ion counts


Koch, Inge; Hoffmann, Peter; Marron, J. S. Proteomics profiles from mass spectrometry. Electron. J. Statist. 8 (2014), no. 2, 1703--1713. doi:10.1214/14-EJS900. https://projecteuclid.org/euclid.ejs/1414588153

Export citation


  • [1] America, A. H. P. and Cordewener, J. H. G. (2008). Comparative LC-MS: A landscape of peaks and valleys., Proteomics 8, 731–749.
  • [2] Czibere, A., Grall, F. and Aivado, M. (2006). Perspectives of proteomics in acute myeloid leukemia., Expert Rev. Anticancer Ther. 6, 1663–1675.
  • [3] Ho, Y. Y. (2011)., Protein profiling of acute myeloid leukemia-specific membrane proteins using label-free liquid chromatography-mass spectrometry. Honours thesis in Chemistry, The University of Adelaide (available from peter.hoffmann@adelaide.edu.au).
  • [4] Koschmieder, S., Rosenbauer, F., Steidl, U., Owens, B. M. and Tenen, D. G. (2005). Role of transcription factors C/EBP$\alpha$ and PU.1 in normal hematopoiesis and leukemia., Int. J. Hematol. 81, 368–377.
  • [5] Lowenberg, B., Downing, J. R., Burnett, A. N. and Engl, J. (1999)., Medicine 341, 1051.
  • [6] Mueller, L. N., Rinner, O., Schmidt, A., Letarte, S., Bodenmiller, B., Brusniak, M. Y., Vitek, O., Aebersold, R. and Mueller, M. (2007). SuperHirn – A novel tool for high resolution LC-MS-based peptide/protein profiling., Proteomics 7, 3470–3480.
  • [7] Wasinger, V. C., Cordwell, S. J., Cerpa-Poljak, A., Yan, J. X., Gooley, A. A., Wilkins, M. R., Duncan, M. W., Harris, R., Williams, K. L. and Humphery-Smith, I. (1995)., Electrophoresis 16, 1090.

See also

  • Related item: Bernardi, M., Sangalli, L. M., Secchi, P., Vantini, S. (2014). Analysis of proteomics data: Block $-mean alignment. Electron. J. Statist. 8 1714–1723.
  • Related item: Tucker, J. D., Wu, W., Srivastava, A. (2014). Analysis of proteomics data: Phase amplitude separation using an extended Fisher-Rao metric. Electron. J. Statist. 8 1724–1733.
  • Related item: Cheng, W., Dryden, I. L., Hitchcock, D. B., Le, H. (2014). Analysis of proteomics data: Bayesian alignment of functions. Electron. J. Statist. 8 1734–1741.
  • Related item: Lu, X., Koch, I., Marron, J. S. (2014). Analysis of proteomics data: Impact of alignment on classification. Electron. J. Statist. 8 1742–1747.
  • Related item: Zhang, I., Liu, X. (2014). Analysis of proteomics data: An improved peak alignment approach. Electron. J. Statist. 8 1748–1755.
  • Related item: Marron, J. S., Koch, I., Hoffmann, P. (2014). Rejoinder: Analysis of proteomics data. Electron. J. Statist. 8 1756–1758.