Statistical Analysis of Spectral Count Data Generated by Label-Free Tandem Mass Spectrometry-Based Proteomics

  • Thang V. PhamEmail author
  • Connie R. Jimenez
Part of the Neuromethods book series (NM, volume 57)


Label-free strategies for quantitative proteomics provide a versatile and economical alternative to labeling-based proteomics strategies. We have shown for different types of biological samples that spectral counting-based label-free quantitation is a promising avenue for biomarker discovery. Analyzing spectral count data generated from these studies is, however, not straightforward, as commonly used techniques for genomics data analysis are not suitable. In this book chapter, we describe three methods to analyze spectral count data, namely, cluster analysis, significance analysis of independent samples, and significance analysis of paired samples. For cluster analysis, we devise a novel distance measure between samples based on the Jeffrey divergence. This measure prevents highly abundant proteins from dominating others in contribution to the total sample difference. We employ the beta-binomial distribution for significance analysis of independent samples, which integrates both within-sample variation and between-sample variation into a single statistical model. Finally, the Mantel–Haenszel test is used for significance analysis of paired samples. We provide detailed illustrations of the steps involved in the analyses.

Key words

Beta-binomial distribution Biomarker discovery Cluster analysis Comparative analysis Label-free tandem mass spectrometry-based proteomics Spectral counting 



This work is supported by the VUmc Cancer Center, Amsterdam.


  1. 1.
    Aebersold R, Mann M (2003) Mass ­spectrometry-based proteomics. Nature, 422(6928):198–207.PubMedCrossRefGoogle Scholar
  2. 2.
    Domon B, Aebersold R (2006) Mass ­spectrometry and protein analysis. Science, 312(5771):212–217.PubMedCrossRefGoogle Scholar
  3. 3.
    Liu H, Sadygov RG, Yates JR III (2004) A model for random sampling and estimation of relative protein abundance in shotgun ­proteomics. Analytical Chemistry, 76(14):4193–4201.PubMedCrossRefGoogle Scholar
  4. 4.
    Albrethsen J, Knol JC, Piersma SR, Pham TV, de Wit M, Mongera S, Carvalho B, Verheul HM, Fijneman RJ, Meijer GA, Jimenez CR (2010) Sub-nuclear proteomics in colorectal cancer: Identification of proteins enriched in the nuclear matrix fraction and regulation in adenoma to carcinoma progression. Molecular and Cellular Proteomics, 9(5):988–1005.PubMedCrossRefGoogle Scholar
  5. 5.
    Dix MM, Simon GM, Cravatt BF (2008) Global mapping of the topography and magnitude of proteolytic events in apoptosis. Cell, 134(4), 679–691.PubMedCrossRefGoogle Scholar
  6. 6.
    Piersma SR, Fiedler U, Span S, Lingnau A, Pham TV, Hoffmann S, Kubbutat MHG, Jimenez CR (2010) Workflow comparison for in-depth, quantitative secretome proteomics for cancer biomarker discovery: Method evaluation, differential analysis and verification in serum. Journal of Proteome Research, 9(4):1913–1922.PubMedCrossRefGoogle Scholar
  7. 7.
    Rajcevic U, Piersma SR, Bougnaud S, Pham TV, Enger P, Bjerkvig R, Jimenez CR, Niclou SP (2009) Enrichment of tumorigenic stem-like cells in biopsy spheroids from colorectal cancer. In Proceedings of the 8th Annual World Congress HUPO 2009, Toronto, Canada.Google Scholar
  8. 8.
    Ramani AK, Li ZH, Hart GT, Carlson MW, Boutz DR, Marcotte EM (2008) A map of human protein interactions derived from co-expression of human mRNAs and their orthologs. Molecular Systems Biology, 4:180.PubMedCrossRefGoogle Scholar
  9. 9.
    Saydam O, Senol O, Schaaij-Visser TB, Pham TV, Piersma SR, Stemmer-Rachamimov AO, Wurdinger T, Peerdeman SM, Jimenez CR (2010) Comparative protein profiling reveals minichromosome maintenance (MCM) ­proteins as novel potential tumor markers for meningiomas. Journal of Proteome Research, 9(1):485–494.PubMedCrossRefGoogle Scholar
  10. 10.
    Zybailov B, Friso G, Kim J, Rudella A, Rodriguez VR, Asakura Y, Sun Q, van Wijk KJ (2009) Large scale comparative proteomics of a Chloroplast Clp protease mutant reveals folding stress, altered protein homeostasis, and feedback regulation of metabolism. Molecular & Cellular Proteomics, 8(8), 1789–1810.CrossRefGoogle Scholar
  11. 11.
    R Development Core Team (2009) R: A ­language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL
  12. 12.
    Schmidt MW, Houseman A, Ivanov AR, Wolf DA (2007) Comparative proteomic and transcriptomic profiling of the fission yeast Schizosaccharomyces pombe. Molecular Systems Biology, 3:79.PubMedCrossRefGoogle Scholar
  13. 13.
    Bantscheff M, Schirle M, Sweetman G, Rick J, Kuster B (2007) Quantitative mass spectrometry in proteomics: a critical review. Analy­tical and Bioanalytical Chemistry, 389(4), 1017–1031.PubMedCrossRefGoogle Scholar
  14. 14.
    Zhang B, VerBerkmoes NC, Langston MA, Uberbacher E, Hettich RL, Samatova NF (2006) Detecting differential and correlated protein expression in label-free shotgun proteomics. Journal of Proteome Research, 5(11), 2909–2918.PubMedCrossRefGoogle Scholar
  15. 15.
    Sokal RR, Rohlf FJ (1995) Biometry: the principles and practice of statistics in biological research (3rd edition). W. H. Freeman: New York., Chapter 17. Analysis of frequencies, 685–793.Google Scholar
  16. 16.
    Pham TV, Piersma SR, Warmoes M, Jimenez CR (2010) On the beta binomial model for analysis of spectral count data in label-free tandem mass spectrometry-based proteomics. Bioinformatics, 26(3):363–369.PubMedCrossRefGoogle Scholar
  17. 17.
    Skellam JG (1948) A probability distribution derived from the binomial distribution by regarding the probability of success as variable between the sets of trials. Journal of the Royal Statistical Society. Series B (Methodological), 10(2), 257–261.Google Scholar
  18. 18.
    Williams DA (1975) The analysis of binary responses from toxicological experiments involving reproduction and teratogenicity. Biometrics, 31(4), 949–952.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.OncoProteomics Laboratory, Department of Medical OncologyVU University Medical Center-Cancer Center AmsterdamAmsterdamThe Netherlands

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