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

Methods in Molecular Biology

Volumn 728, Issue , 2011, Pages 293-319

  Clough, Timothy ^c   Braun, Siegmund ^c   Fokin, Vladimir ^c   Ott, Ilka ^c   Ragg, Susanne ^c   Schadow, Gunther ^c   Vitek, Olga ^a,b  

^a PURDUE UNIVERSITY   (United States)

^b Purdue University   (United States)

^c NONE

Author keywords

Analysis of Variance; Blocking; Coronary artery disease; Gene set enrichment; LC MS; Protein quantification; Quantitative proteomics; Randomization; Replication; Sample size; Statistical design of experiments

Indexed keywords

PROTEOME;

ANALYSIS OF VARIANCE; ARTICLE; BLOOD; CASE CONTROL STUDY; CORONARY ARTERY DISEASE; FEMALE; HUMAN; LIQUID CHROMATOGRAPHY; MALE; MASS SPECTROMETRY; METABOLISM; METHODOLOGY; MIDDLE AGED; PROTEIN DATABASE; PROTEOMICS; SAMPLE SIZE; STAINING; STATISTICAL MODEL;

ANALYSIS OF VARIANCE; CASE-CONTROL STUDIES; CHROMATOGRAPHY, LIQUID; CORONARY ARTERY DISEASE; DATABASES, PROTEIN; FEMALE; HUMANS; MALE; MASS SPECTROMETRY; MIDDLE AGED; MODELS, STATISTICAL; PROTEOME; PROTEOMICS; SAMPLE SIZE; STAINING AND LABELING;

EID: 79960147500     PISSN: 10643745     EISSN: 19406029     Source Type: Book Series    
DOI: 10.1007/978-1-61779-068-3_20     Document Type: Chapter

References (25)

1. Aebersold R and Mann M (2003). Mass spectrometry-based proteomics. Nature, 422, 198–207.
2. Gstaiger M and Aebersold R (2009). Applying mass spectrometry-based proteomics to genetics, genomics and network biology. Nature Reviews Genetics, 10, 617–27.
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.
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.
5. R Development Core Team (2009). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
6. Gentleman R, Carey V, Huber W, et al., eds. (2005). Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Springer Verlag.
7. Ragg S, Fokin V, Podgorski K, et al. (2007). Proteomic profiling of plasma samples in coronary artery disease. Circulation, 116, II.575.
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.
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.
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.
11. Oberg AL and Vitek O (2009). Statistical design of quantitative mass spectrometry-based proteomic experiments. Journal of Proteome Research, 8, 2144–2156.
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.
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.
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.
15. Kutner M, Nachtsheim C, Neter J, et al. (2004). Applied Linear Statistical Models. McGraw-Hill/Irwin, New York, 5th edition.
16. Montgomery DC (2000). Design and Analysis of Experiments. John Wiley and Sons, New York, 5th edition.
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.
18. Dudoit S, Shaffer JP, and Boldrick JC (2003). Multiple hypothesis testing in microarray experiments. Statistical Science, 18, 71–103.
19. Ashburner M, Ball CA, Blake JA, et al. (2000). Gene Ontology: Tool for the unification of biology. Nature Genetics, 25, 25–29.
20. Kanehisa M and Goto S (2000). KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Research, 28, 27–30.
21. Ackermann M and Strimmer K (2009). A general modular framework for gene set enrichment analysis. BMC Bioinformatics, 10, 47.
22. Rao PV (1998). Statistical Research Methods in the Life Sciences. Brooks/Cole Publishing Company, Pacific Grove, CA.
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.
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.
25. Zuber V and Strimmer K (2009). Gene ranking and biomarker discovery under correlation. Bioinformatics, 25, 2700.