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Statistical Design and Analysis of Label-free LC-MS Proteomic Experiments: A Case Study of Coronary Artery Disease

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 728))

Abstract

This chapter presents a case study, which applies statistical design and analysis to an LC–MS-based ­investigation of subjects with coronary artery disease. First, we discuss the principles of statistical ­experimental design, and the specification of an Analysis of Variance (ANOVA) model that describes the major sources of variation in the data. Second, we discuss procedures for detecting differentially abundant proteins, estimating protein abundance in individual samples, testing predefined groups of proteins for enrichment in differential abundance, and calculating sample size for a future experiment. The discussion is accompanied by examples of computer code implemented in the open-source statistical software R, which can be followed for an independent implementation of a similar investigation.

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References

  1. Aebersold R and Mann M (2003). Mass spectrometry-based proteomics. Nature, 422, 198–207.

    Article  PubMed  CAS  Google Scholar 

  2. Gstaiger M and Aebersold R (2009). Applying mass spectrometry-based proteomics to genetics, genomics and network biology. Nature Reviews Genetics, 10, 617–27.

    Article  PubMed  CAS  Google Scholar 

  3. Nesvizhskii AI, Vitek O, and Aebersold R (2007). Analysis and validation of proteomic data generated by tandem mass spectrometry. Nature Methods, 4, 787–797.

    Article  PubMed  CAS  Google Scholar 

  4. Mueller LN, Brusniak M, Mani DR, et al. (2008). An assessment of software solutions for the analysis of mass spectrometry based quantitative proteomics data. Journal of Proteome Research, 7, 51–61.

    Article  PubMed  CAS  Google Scholar 

  5. R Development Core Team (2009). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.

    Google Scholar 

  6. Gentleman R, Carey V, Huber W, et al., eds. (2005). Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Springer Verlag.

    Google Scholar 

  7. Ragg S, Fokin V, Podgorski K, et al. (2007). Proteomic profiling of plasma samples in coronary artery disease. Circulation, 116, II.575.

    Google Scholar 

  8. Higgs RE, Knierman MD, Gelfanova V, et al. (2005). Comprehensive label-free method for the relative quantification of proteins from biological samples. Journal of Proteome Research, 4, 1442–1450.

    Article  PubMed  CAS  Google Scholar 

  9. Higgs RE, Knierman MD, Gelfanova V, et al. (2008). Label-free LC-MS method for the identification of biomarkers. Methods in Molecular Biology, 428, 209–230.

    Article  PubMed  CAS  Google Scholar 

  10. Kemper M and Levinson SS (1997). Serum Proteins in Clinical Medicine, Vol. 1, Laboratory Section, 1st ed. Robert Richie, ed., Olga Navolotskaia, asst. ed. Foundation for Blood Research, PO Box 190, Scarborough ME 04070-0190. Clin Chem, 43(3), 550a-551.

    Google Scholar 

  11. Oberg AL and Vitek O (2009). Statistical design of quantitative mass spectrometry-based proteomic experiments. Journal of Proteome Research, 8, 2144–2156.

    Article  PubMed  CAS  Google Scholar 

  12. Bolstad BM, Irizarry RA, Astrand M, et al. (2003). A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics, 19, 185–193.

    Article  PubMed  CAS  Google Scholar 

  13. Patil ST, Higgs RE, Brandt JE, et al. (2007). Identifying pharmacodynamic protein markers of centrally active drugs in humans: A pilot study in a novel clinical model. Journal of Proteome Research, 6, 955–66.

    Article  PubMed  CAS  Google Scholar 

  14. Clough T, Key M, Ott I, et al. (2009). Protein quantification in label-free LC-MS experiments. Journal of Proteome Research, 8, 5275–5284.

    Article  PubMed  CAS  Google Scholar 

  15. Kutner M, Nachtsheim C, Neter J, et al. (2004). Applied Linear Statistical Models. McGraw-Hill/Irwin, New York, 5th edition.

    Google Scholar 

  16. Montgomery DC (2000). Design and Analysis of Experiments. John Wiley and Sons, New York, 5th edition.

    Google Scholar 

  17. Benjamini Y and Hochberg Y (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B, 57, 289–300.

    Google Scholar 

  18. Dudoit S, Shaffer JP, and Boldrick JC (2003). Multiple hypothesis testing in microarray experiments. Statistical Science, 18, 71–103.

    Article  Google Scholar 

  19. Ashburner M, Ball CA, Blake JA, et al. (2000). Gene Ontology: Tool for the unification of biology. Nature Genetics, 25, 25–29.

    Article  PubMed  CAS  Google Scholar 

  20. Kanehisa M and Goto S (2000). KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Research, 28, 27–30.

    Article  PubMed  CAS  Google Scholar 

  21. Ackermann M and Strimmer K (2009). A general modular framework for gene set enrichment analysis. BMC Bioinformatics, 10, 47.

    Article  PubMed  Google Scholar 

  22. Rao PV (1998). Statistical Research Methods in the Life Sciences. Brooks/Cole Publishing Company, Pacific Grove, CA.

    Google Scholar 

  23. Karpievitch Y, Stanley J, Taverner T, et al. (2009). A statistical framework for protein quantitation in bottom-up MS-based proteomics. Bioinformatics, 25, 2028–2034.

    Article  PubMed  CAS  Google Scholar 

  24. Corzett TH, Fodor IK, Choi MW, et al. (2010). Statistical analysis of variation in the human plasma proteome. Journal of Biomedicine and Biotechnology, 2010. doi:10.1155/2010/258494.

    Google Scholar 

  25. Zuber V and Strimmer K (2009). Gene ranking and biomarker discovery under correlation. Bioinformatics, 25, 2700.

    Article  PubMed  CAS  Google Scholar 

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Correspondence to Olga Vitek .

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Clough, T. et al. (2011). Statistical Design and Analysis of Label-free LC-MS Proteomic Experiments: A Case Study of Coronary Artery Disease. In: Simpson, R., Greening, D. (eds) Serum/Plasma Proteomics. Methods in Molecular Biology, vol 728. Humana Press. https://doi.org/10.1007/978-1-61779-068-3_20

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  • DOI: https://doi.org/10.1007/978-1-61779-068-3_20

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  • Publisher Name: Humana Press

  • Print ISBN: 978-1-61779-067-6

  • Online ISBN: 978-1-61779-068-3

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