Comparison of the Mahalanobis Distance and Pearson’s χ2 Statistic as Measures of Similarity of Isotope Patterns

  • Fatemeh Zamanzad Ghavidel
  • Jürgen Claesen
  • Tomasz Burzykowski
  • Dirk Valkenborg
Application Note

Abstract

To extract a genuine peptide signal from a mass spectrum, an observed series of peaks at a particular mass can be compared with the isotope distribution expected for a peptide of that mass. To decide whether the observed series of peaks is similar to the isotope distribution, a similarity measure is needed. In this short communication, we investigate whether the Mahalanobis distance could be an alternative measure for the commonly employed Pearson’s χ2 statistic. We evaluate the performance of the two measures by using a controlled MALDI-TOF experiment. The results indicate that Pearson’s χ2 statistic has better discriminatory performance than the Mahalanobis distance and is a more robust measure.

Key words

Similarity statistics Isotope distributions Mass spectral data interpretation Bioinformatics  Mahalanobis distance 

Notes

Acknowledgments

D.V. acknowledges the support of the SBO grant ‘InSPECtor’ (120025) of the Flemish agency for Innovation by Science and Technology (IWT).

References

  1. 1.
    Valkenborg, D., Mertens, I., Lemière, F., Witters, F., Burzykowski, T.: The isotopic distribution conundrum. Mass Spectrom. Rev. 31(1), 96–109 (2011)CrossRefGoogle Scholar
  2. 2.
    Rockwood, A.L., Palmblad, M.: Isotopic distributions. Methods Mol. Biol. 1007, 65–99 (2013)CrossRefGoogle Scholar
  3. 3.
    Renard, B.Y., Kirchner, M., Steen, H., Steen, J.A.J., Hamprech, F.A.: NITPICK: Peak identification for mass spectrometry data. BMC Bioinforma 9, 355 (2008)CrossRefGoogle Scholar
  4. 4.
    Nicolardi, S., Palmblad, M., Dalebout, H., Bladergroen, M., Tollenaar, R.A., Deelder, A.M., van der Burgt, Y.E.: Quality control based on isotopic distributions for high-throughput MALDI-TOF and MALDI-FTICR serum peptide profiling. J. Am. Soc. Mass Spectrom. 21(9), 1515–1525 (2010)CrossRefGoogle Scholar
  5. 5.
    Senko, M.W., Beu, S.C., McLafferty, F.W.: Determination of monoisotopic masses and ion populations for large biomolecules from resolved isotopic distribution. J. Am. Soc. Mass Spectrom. 6, 229–233 (2005)CrossRefGoogle Scholar
  6. 6.
    Hsieh, E.J., Hoopmann, M.R., Maclean, B., MacCoss, M.J.: Comparison of database search strategies for high precursor mass accuracy MS/MS data. J. Proteome Res. 9(2), 1138–1143 (2010)CrossRefGoogle Scholar
  7. 7.
    Palmblad, M., Buijs, J., Hakanson, P.: Automatic analysis of hydrogen/deuterium exchange mass spectra of peptides and proteins using calculations of isotopic distributions. J. Am. Soc. Mass Spectrom. 12, 1153–1162 (2001)CrossRefGoogle Scholar
  8. 8.
    Valkenborg, D., Assam, P., Thomas, G., Krols, L., Kas, K., Burzykowski, T.: Using a Poisson approximation to predict the isotopic distribution of sulphur-containing peptides in a peptide-centric proteomic approach. Rapid Commun. Mass Spectrom. 21, 3387–3391 (2007)CrossRefGoogle Scholar
  9. 9.
    Valkenborg, D., Thomas, G., Krols, L., Kas, K., Burzykowski, T.: A strategy to analyse data from high performance liquid chromatography combined with high resolution mass spectrometry. J. Mass Spectrom. 44, 516–529 (2009)CrossRefGoogle Scholar
  10. 10.
    Senko, M.W., Beu, S.C., McLafferty, F.W.: Automated assignment of charge states from resolved isotopic peaks for multiply-charged ions. J. Am. Soc. Mass Spectrom. 6, 52–56 (1995)CrossRefGoogle Scholar
  11. 11.
    Breen, E.J., Hopwood, F.G., Williams, K.L., Wilkins, M.R.: Automatic poisson peak harvesting for high throughput protein identification. Electrophoresis 21, 2243–2251 (2000)CrossRefGoogle Scholar
  12. 12.
    Gay, S., Binz, P.A., Hochstrasser, D.F., Appel, R.D.: Modeling peptide mass fingerprinting data using the atomic composition of peptides. Electrophoresis 20, 3527–3534 (1999)CrossRefGoogle Scholar
  13. 13.
    Mahalanobis, P.C.: On the generalized distance in statistics. Proc. Natl. Inst. Sci. India 2(1), 49–55 (1936)Google Scholar
  14. 14.
    Matzke, M.M., Waters, K.M., Metz, T.O., Jacobs, J.M., Sims, A.C., Baric, R.S., Pounds, J.G., Webb-Robertson, B.J.: Improved quality control processing of peptide-centric LC-MS proteomics data. Bioinformatics 27(20), 2866–2872 (2011)CrossRefGoogle Scholar
  15. 15.
    Schulz-Trieglaff, O., Machtejevas, E., Reinert, K., Schlüter, H., Thiemann, J., Unger, K.: Statistical quality assessment and outlier detection for liquid chromatography-mass spectrometry experiments. BioDataMining 2(1) Article 4 (2009)Google Scholar
  16. 16.
    Cairns, D.A., Perkins, D.N., Stanley, A.J., Thompson, D., Barrett, J.H., Selby, P.J., Banks, R.E.: Integrated multi-level quality control for proteomic profiling studies using mass spectrometry. BMC Bioinforma 9, 519 (2008)CrossRefGoogle Scholar
  17. 17.
    Liu, Q., Sung, A.H., Qiao, M., Chen, Z., Yang, J.Y., Yang, M.Q., Huang, X., Deng, Y.: Comparison of feature selection and classification for MALDI-MS data. BMC Genomics 10(Suppl 1), S3 (2009)CrossRefGoogle Scholar
  18. 18.
    Picotti, P., Aebersold, R., Domon, B.: The implications of proteolytic background for shotgun proteomics. Mol. Cell. Proteomics 6(9), 1589–1598 (2007)CrossRefGoogle Scholar
  19. 19.
    Valkenborg, D., Jansen, I., Burzykowski, T.: A model-based method for the prediction of the isotopic distribution of peptides. J. Am. Soc. Mass Spectrom. 19(5), 703–712 (2008)CrossRefGoogle Scholar
  20. 20.
    Dittwald, P., Valkenborg, D., Claesen, J., Burzykowski, T., Gambin, A.: BRAIN: A universal tool for high-throughput calculations of isotopic distribution for mass spectrometry. Anal. Chem. 85(4), 1991–1994 (2013)CrossRefGoogle Scholar

Copyright information

© American Society for Mass Spectrometry 2013

Authors and Affiliations

  • Fatemeh Zamanzad Ghavidel
    • 1
  • Jürgen Claesen
    • 1
  • Tomasz Burzykowski
    • 1
  • Dirk Valkenborg
    • 1
    • 2
    • 3
  1. 1.I-BioStatHasselt UniversityHasseltBelgium
  2. 2.Applied Bio and Molecular SystemsFlemish Institute for Technological Research, VITOMolBelgium
  3. 3.Center for ProteomicsAntwerpBelgium

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