Abstract
This chapter aims to provide statistical methods for analyzing protein microarray data. It uses a publicly available protein array dataset and emphasizes practical applications in statistics using R, a statistical software. A wide range of statistical methods will be demonstrated, including descriptive statistics, hypothesis testing, false discovery rate, receiver operating characteristic curve, correlation, visualization, and power analysis. The R code used to perform the statistical analyses will be provided.
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References
Elston RC, Johnson WD (1987) Essentials of biostatistics. F.A. Davis Company, Philadelphia, PA
Díez P, González-González M, Lourido L, Dégano RM, Ibarrola N, Casado-Vela J, LaBaer J, Fuentes M (2015) NAPPA as a real new method for protein microarray generation. Microarrays 4(2):214–227
Protein Microarray Database (2020) PMDA156-PMDA160. http://www.proteinmicroarray.cn/. Accessed 8 Sept 2020
R core team (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org/. Accessed 8 Sept 2020
Tukey JW (1977) Exploratory data analysis, vol 2. Addison-Wesley, Boston, pp 131–160
Bonferroni C (1936) Teoria statistica delle classi e calcolo delle probabilita. Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commericiali di Firenze 8:3–62
Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B Methodol 57(1):289–300
Storey JD (2002) A direct approach to false discovery rates. J R Stat Soc B Stat Methodol 64(3):479–498
Storey JD (2003) The positive false discovery rate: a Bayesian interpretation and the q-value. Ann Stat 31(6):2013–2035
Storey JD, Bass AJ, Dabney A, Robinson D (2020) q-value: Q-value estimation for false discovery rate control. R package version 2.20.0. http://github.com/jdstorey/qvalue. Accessed 8 Sept 2020
Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Müller M (2011) pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 12(1):1–8
Liu P, Hwang JG (2007) Quick calculation for sample size while controlling false discovery rate with application to microarray analysis. Bioinformatics 23(6):739–746
Orr M, Liu P (2009) Sample size estimation while controlling false discovery rate for microarray experiments using the size. fdr package. R Journal 1(1):47–53
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Chung, Y. (2021). Statistical Methods for Analysis of Protein Microarray Data Using R. In: Barderas, R., LaBaer, J., Srivastava, S. (eds) Protein Microarrays for Disease Analysis. Methods in Molecular Biology, vol 2344. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1562-1_18
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DOI: https://doi.org/10.1007/978-1-0716-1562-1_18
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