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Statistical Evaluation of Labeled Comparative Profiling Proteomics Experiments Using Permutation Test

  • Hien D. Nguyen
  • Geoffrey J. McLachlan
  • Michelle M. Hill
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1549)

Abstract

Comparative profiling proteomics experiments are important tools in biological research. In such experiments, tens to hundreds of thousands of peptides are measured simultaneously, with the goal of inferring protein abundance levels. Statistical evaluation of these datasets are required to determine proteins that are differentially abundant between the test samples. Previously we have reported the non-normal distribution of SILAC datasets, and demonstrated the permutation test to be a superior method for the statistical evaluation of non-normal peptide ratios. This chapter outlines the steps and the R scripts that can be used for performing permutation analysis with false discovery rate control via the Benjamini–Yekutieli method.

Key words

Comparative profiling Simultaneous testing SILAC Hypothesis test Permutation test False discovery rate 

References

  1. 1.
    Bantscheff M, Schirle M, Sweetman G, Rick J, Kuster B (2007) Quantitative mass spectrometry in proteomics: a critical review. Anal Bioanal Chem 389(4):1017–1031. doi: 10.1007/s00216-007-1486-6 CrossRefPubMedGoogle Scholar
  2. 2.
    Ong SE, Blagoev B, Kratchmarova I, Kristensen DB, Steen H, Pandey A, Mann M (2002) Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics 1(5):376–386CrossRefPubMedGoogle Scholar
  3. 3.
    Ross PL, Huang YN, Marchese JN, Williamson B, Parker K, Hattan S, Khainovski N, Pillai S, Dey S, Daniels S, Purkayastha S, Juhasz P, Martin S, Bartlet-Jones M, He F, Jacobson A, Pappin DJ (2004) Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics 3(12):1154–1169. doi: 10.1074/mcp.M400129-MCP200 CrossRefPubMedGoogle Scholar
  4. 4.
    Gygi SP, Rist B, Gerber SA, Turecek F, Gelb MH, Aebersold R (1999) Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat Biotechnol 17(10):994–999. doi: 10.1038/13690 CrossRefPubMedGoogle Scholar
  5. 5.
    Thompson A, Schafer J, Kuhn K, Kienle S, Schwarz J, Schmidt G, Neumann T, Johnstone R, Mohammed AK, Hamon C (2003) Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal Chem 75(8):1895–1904CrossRefPubMedGoogle Scholar
  6. 6.
    Lau KW, Jones AR, Swainston N, Siepen JA, Hubbard SJ (2007) Capture and analysis of quantitative proteomic data. Proteomics 7(16):2787–2799. doi: 10.1002/pmic.200700127 CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Rigbolt KT, Blagoev B (2010) Proteome-wide quantitation by SILAC. Methods Mol Biol 658:187–204. doi: 10.1007/978-1-60761-780-8_11 CrossRefPubMedGoogle Scholar
  8. 8.
    Mann M (2006) Functional and quantitative proteomics using SILAC. Nat Rev Mol Cell Biol 7(12):952–958. doi: 10.1038/nrm2067 CrossRefPubMedGoogle Scholar
  9. 9.
    Nguyen H, Wood IA, Hill MM (2012) A robust permutation test for quantitative SILAC proteomics experiments. J Integr Omics 2:80–93Google Scholar
  10. 10.
    Chen D, Shah A, Nguyen H, Loo D, Inder KL, Hill MM (2014) Online quantitative proteomics p-value calculator for permutation-based statistical testing of peptide ratios. J Proteome Res 13(9):4184–4191. doi: 10.1021/pr500525e CrossRefPubMedGoogle Scholar
  11. 11.
    Benjamini Y, Yekutieli D (2001) The control of the false discovery rate in multiple testing under dependency. Ann Stat 29:1165–1188CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  • Hien D. Nguyen
    • 1
    • 2
  • Geoffrey J. McLachlan
    • 1
  • Michelle M. Hill
    • 2
  1. 1.School of Mathematics and Physics,The University of QueenslandSt. LuciaAustralia
  2. 2.The University of Queensland Diamantina InstituteTranslational Research InstituteWoolloongabbaAustralia

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