Abstract
Mass spectrometry is frequently used in quantitative proteomics to detect differentially regulated proteins. A very important but unfortunately oftentimes neglected part in detecting differential proteins is the statistical analysis. Data from proteomics experiments are usually high-dimensional and hence require profound statistical methods. It is especially important to already correctly design a proteomic experiment before it is conducted in the laboratory. Only this can ensure that the statistical analysis is capable of detecting truly differential proteins afterwards. This chapter thus covers aspects of both statistical planning and the actual analysis of quantitative proteomic experiments.
*These authors are co-corresponding authors
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References
Cravatt BF, Simon GM, Yates JR III (2007) The biological impact of mass-spectrometry-based proteomics. Nature 450:991–1000
Hanash SM, Pitteri SJ, Faca VM (2008) Mining the plasma proteome for cancer biomarkers. Nature 452:571–579
Rifai N, Gillette MA, Carr SA (2006) Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat Biotechnol 24:971–983
Hu J, Coombes KR, Morris JS et al (2005) The importance of experimental design in proteomic mass spectrometry experiments: some cautionary tales. Brief Funct Genomics Proteomics 3:322–331
Chelius D, Bondarenko PV (2002) Quantitative profiling of proteins in complex mixtures using liquid chromatography and mass spectrometry. J Proteome Res 1:317–323
Ong SE, Mann M (2005) Mass spectrometry-based proteomics turns quantitative. Nat Chem Biol 1:252–262
Listgarten J, Emili A (2005) Statistical and computational methods for comparative proteomic profiling using liquid chromatography-tandem mass spectrometry. Mol Cell Proteomics 4:419–434
Podwojski K, Eisenacher M, Kohl M et al (2010) Peek a peak: a glance at statistics for quantitative label-free proteomics. Expert Rev Proteomics 7:249–261
Mueller LN, Brusniak MY, Mani DR et al (2008) An assessment of software solutions for the analysis of mass spectrometry based quantitative proteomics data. J Proteome Res 7:51–61
Radulovic D, Jelveh S, Ryu S et al (2004) Informatics platform for global proteomic profiling and biomarker discovery using liquid chromatography-tandem mass spectrometry. Mol Cell Proteomics 3:984–997
Bellew M, Coram M, Fitzgibbon M et al (2006) A suite of algorithms for the comprehensive analysis of complex protein mixtures using high-resolution LC-MS. Bioinformatics 22:1902–1909
Cox J, Mann M (2008) MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol 26:1367–1372
Eng JK, McCormack AL, Yates JR 3rd (1994) An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J Am Soc Mass Spectrom 5:976–989
Perkins DN, Pappin DJ, Creasy DM et al (1999) Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20:3551–3567
Reidegeld KA, Eisenacher M, Kohl M et al (2008) An easy-to-use Decoy Database Builder software tool, implementing different decoy strategies for false discovery rate calculation in automated MS/MS protein identifications. Proteomics 8:1129–1137
Keller A, Nesvizhskii AI, Kolker E et al (2002) Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal Chem 74:5383–5392
Nesvizhskii AI, Keller A, Kolker E et al (2003) A statistical model for identifying proteins by tandem mass spectrometry. Anal Chem 75:4646–4658
Nesvizhskii AI, Aebersold R (2005) Interpretation of shotgun proteomic data: the protein inference problem. Mol Cell Proteomics 4:1419–1440
Liu H, Sadygov RG, Yates JR 3rd (2004) A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal Chem 76:4193–4201
Old WM, Meyer-Arendt K, Aveline-Wolf L et al (2005) Comparison of label-free methods for quantifying human proteins by shotgun proteomics. Mol Cell Proteomics 4:1487–1502
Cairns DA, Barrett JH, Billingham LJ et al (2009) Sample size determination in clinical proteomic profiling experiments using mass spectrometry for class comparison. Proteomics 9:74–86
Dudoit S, Shaffer JP, Boldrick JC (2003) Multiple hypothesis testing in microarray experiments. Stat Sci 18:71–103
Dudoit S, Yang YH, Callow MJ et al (2002) Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Stat Sin 12: 111–139
Jung K, Poschmann G, Podwojski K et al (2009) Adjusted confidence intervals for the expression change of proteins observed in 2-dimensional difference gel electrophoresis. J Proteomics Bioinform 2:78–87
Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B 57:289–300
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Podwojski, K., Stephan, C., Eisenacher, M. (2012). Important Issues in Planning a Proteomics Experiment: Statistical Considerations of Quantitative Proteomic Data. In: Marcus, K. (eds) Quantitative Methods in Proteomics. Methods in Molecular Biology, vol 893. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-61779-885-6_1
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DOI: https://doi.org/10.1007/978-1-61779-885-6_1
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