Skip to main content
SpringerLink
Log in

Statistical Methods for Proteomics

  • Protocol
  • First Online:
Statistical Methods in Molecular Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 620))

Abstract

During the last decade, analytical methods for the detection and quantification of proteins and peptides in biological samples have been considerably improved. It is therefore now possible to compare simultaneously the expression levels of hundreds or thousands of proteins in different types of tissue, for example, normal and cancerous, or in different cell lines. In this chapter, we illustrate statistical designs for such proteomics experiments as well as methods for the analysis of resulting data. In particular, we focus on the preprocessing and analysis of protein expression levels recorded by the use of either two-dimensional gel electrophoresis or mass spectrometry.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Nesvizhskii, A. I., Keller, A., Kolker, E., and Aebersold, R. (2002) A statistical model for identifying proteins by tandem mass spectrometry. Anal Chem 75, 4646–4658.

    Article  Google Scholar 

  2. Urfer, W., Grzegorczyk, M., and Jung, K. (2006) Statistics for proteomics: a review of tools for analyzing experimental data. Pract Proteomics 1, 48–55.

    Article  CAS  Google Scholar 

  3. Klose, J., and Kobalz, U. (1995) Two-dimensional electrophoresis of proteins: and updated protocol and implications for functional analysis of the genome. Electrophoresis 4, 1034–1059.

    Article  Google Scholar 

  4. Ünlü, M., Morgan, M. E., and Minden, J. S. (1997) Difference gel electrophoresis: A single gel method for detecting changes in protein extracts. Electrophoresis 18, 2071–2077.

    Article  PubMed  Google Scholar 

  5. Aebersold, R., and Goodlett, D. R. (2001) Mass spectrometry in proteomics. Chem Rev 101, 269–295.

    Article  PubMed  CAS  Google Scholar 

  6. Stühler, K., Pfeiffer, K., Joppich, C., Stephan, C., Jung, K., Müller, M., Schmidt, O., van Hall, A., Hamacher, M., Urfer, W., Meyer, H. E., and Marcus, K. (2006) Pilot study of the Human Proteome Organisation Brain Proteome Project: Applying different 2-DE techniques to monitor proteomic changes during murine brain development. Proteomics 6, 4899–4913.

    Article  PubMed  Google Scholar 

  7. Karp, N. A., McCormick, P. S., Russell, M. R., and Lilley, K. S. (2007) Experimental and statistical considerations to avoid false conclusions in proteomic studies using differential in-gel electrophoresis. Mol Cell Proteomics 6, 1354–1364.

    Article  PubMed  CAS  Google Scholar 

  8. Fodor, I. K., Nelson, D. O., Alegria-Hartman, M., Robbins, K., Langlois, R. G., Turteltaub, K. W., Corzett, T.H., and McCutchen-Maloney, S.L. (2005) Statistical challenges in analysis of two-dimensional difference gel electrophoresis experiments using DeCyder. Bioinformatics 21, 3733–3740.

    Article  PubMed  CAS  Google Scholar 

  9. Chich, J.-F., David, O., Villers, F., Schaeffer, B., Lutomski, D., and Huet, S. (2007) Statistics for proteomics: Experimental design and 2-DE differential analysis. J Chromatogr B 849, 261–272.

    Article  CAS  Google Scholar 

  10. Kreil, D. P., Karp, N. A., and Lilley, K. S. (2004) DNA microarray normalization methods can remove bias from differential protein expression analysis of 2D difference gel electrophoresis results. Bioinformatics 20, 2026–3740.

    Article  PubMed  CAS  Google Scholar 

  11. Huber, W., Heydebreck, A., von Sültmann, H., Poustka, A., and Vingron, M. (2002) Variance stabilization applied to microarray data calibration and the quantification of differential expression. Bioinformatics 18, S96–S104.

    Article  PubMed  Google Scholar 

  12. Bolstad, B. M., Irizarry R. A., Astrand, M., and Speed, T. P. (2003) A comparison of normalization methods for high density oligonucleotide array data based on bias and variance. Bioinformatics 19, 185–193.

    Article  PubMed  CAS  Google Scholar 

  13. Jung, K., Gannoun, A., Sitek, B., Meyer, H. E., Stühler, K., and Urfer, W. (2005) Analysis of dynamic protein expression data. RevStat-Stat J 3, 99–111.

    Google Scholar 

  14. Jung, K., Gannoun, A., Sitek, B., Apostolov, O., Schramm, A., Meyer, H. E., Stühler, K., and Urfer, W. (2006) Statistical evaluation of methods for the analysis of dynamic protein expression data from a tumor study. RevStat-Stat J 4, 67–80.

    Google Scholar 

  15. Smyth, G. K. (2004) Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3, Article 3.

    Google Scholar 

  16. Dudoit, S., Shaffer, J. P., and Boldrick, J. C. (2003) Multiple hypothesis testing in microarray experiments. Stat Sci 18, 71–103.

    Article  Google Scholar 

  17. Jung, K., Poschmann, G., Podwojski, K., Eisenacher, M., Kohl, M., Pfeiffer, K., Meyer, H. E., Stühler, K., and Stephan, C. (2009) adjusted confidence intervals for the expression change of proteins observed in 2-dimensional difference gel electrophoresis. J Proteomics Bioinform 2, 78–87.

    Article  CAS  Google Scholar 

  18. Gygi, S. P., Rist, B., Gerber, S. A., Turecek, F., Gelb, M. H., and Aebersold, R. (1999) Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat Biotechnol 17, 994–999.

    Article  PubMed  CAS  Google Scholar 

  19. Ross, P. L., Huang, Y. N., Marchese, J. N., et al. (2004) Multiplexed protein quantitation in Saccharomyces cerevisiae using aminereactive isobaric tagging reagents. Mol Cell Proteomics 3, 1154–1169.

    Article  PubMed  CAS  Google Scholar 

  20. Boehm, A. M., Pütz, S., Altenhöfer, D., Sickmann, A., and Falk, M. (2007) Precise protein quantification based on peptide quantification using iTRAQ™. BMC Bioinformatics 8, 214.

    Article  PubMed  Google Scholar 

  21. Jeffries, N. (2005) Algorithms for alignment of mass spectrometry proteomic data. Bioinformatics 21, 3066–3073.

    Article  PubMed  CAS  Google Scholar 

  22. Pusch, W., Flocco, M. T., Leung, S.-M., Thiele, H., and Kostrzewa, M. (2003) Mass spectrometry-based clinical proteomics. Pharmacogenomics 4, 463–476.

    Article  PubMed  CAS  Google Scholar 

  23. Jeffries, N. O. (2004) Performance of a genetic algorithm for mass spectrometry proteomics. BMC Bioinformatics 5, 180.

    Article  PubMed  Google Scholar 

  24. Lilien, R. H., Farid, H., and Donald, B. R. (2003) Probabilistic disease classification of expression dependent proteomic data from mass spectrometry of human serum. J Comput Biol 10, 925–946.

    Article  PubMed  CAS  Google Scholar 

  25. Zhang, X., Lu, X., Shi, Q., Xu, X., Leung, H., Harris, L. N., Iglehart, J. D., Miron, A., Liu, J. S., and Wong, W. H. (2006) Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data. BMC Bioinformatics 7, 197.

    Article  PubMed  Google Scholar 

  26. Cairns, D. A., Barrett, J. H., Billingham, L. J., Stanley, A. J., Xinarianos, G., Field, J. K., Johnson, P. J., Selby, P. J., and Banks, R. E. (2009) Sample size determination in clinical proteomic profiling experiments using mass spectrometry for class comparison. Proteomics 9, 74–86.

    Article  PubMed  CAS  Google Scholar 

  27. Fu, W. J., Dougherty, E. R., Mallick, B., and Carrol, R. (2005) How many samples are needed to build a classifier: A general sequential approach. Bioinformatics 21, 63–70.

    Article  PubMed  CAS  Google Scholar 

  28. Sitek, B., Apostolov, O., K. S., Pfeiffer, K., Meyer, H. E., Eggert, A., and Schramm, A. (2005) Identification of dynamic proteome changes upon ligand activation of trk-receptors using two-dimensional fluorescence difference gel electrophoresis and mass spectrometry. Mol Cell Proteomics 4, 291–299.

    Google Scholar 

  29. Brunner, E., Domhof, S., and Langer, F. (2002) Nonparametric Analysis of Longitudinal Data in Factorial Experiments. John Wiley & Sons, New York.

    Google Scholar 

  30. Grzegorczyk, M. (2007) Extracting protein regulatory networks with graphical models. Proteomics 7(S1), 51–59.

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Medical Statistics, Georg-August-University Göttingen, Göttingen, Germany

    Klaus Jung

Authors
  1. Klaus Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Weill Medical College, Dept. Public Health, Cornell University, East 69th St. 411, New York, 10021, New York, USA

    Heejung Bang

  2. Weill Medical College, Dept. Public Health, Cornell University, East 69th St. 411, New York, 10021, New York, USA

    Xi Kathy Zhou

  3. Journal of Experimental Medicine, Rockefeller University Press, First Ave. 1114, New York, 10021, New York, USA

    Heather L. van Epps

  4. Weill Medical College, Dept. Public Health, Cornell University, East 69th St. 411, New York, 10021, New York, USA

    Madhu Mazumdar

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Humana Press, a part of Springer Science+Business Media, LLC

About this protocol

Cite this protocol

Jung, K. (2010). Statistical Methods for Proteomics. In: Bang, H., Zhou, X., van Epps, H., Mazumdar, M. (eds) Statistical Methods in Molecular Biology. Methods in Molecular Biology, vol 620. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-60761-580-4_18

Download citation

  • DOI: https://doi.org/10.1007/978-1-60761-580-4_18

  • Published:

  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-60761-578-1

  • Online ISBN: 978-1-60761-580-4

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation