Abstract
In this chapter we demonstrate the use of R Bioconductor packages pepStat and Pviz on a set of paired peptide microarrays generated from vaccine trial data. Data import, background correction, normalization, and summarization techniques are presented. We introduce a sliding mean method for amplifying signal and reducing noise in the data, and show the value of gathering paired samples from subjects. Useful visual summaries are presented, and we introduce a simple method for setting a decision rule for subject/peptide responses that can be used with a set of control peptides or placebo subjects.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
R Core Team (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, http://www.R-project.org
Gentleman R, Carey VJ, Bates DM et al (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5:R80
Gottardo R, Imholte G, Sauteraud R et al. (2014) pepStat: statistical analysis of peptide microarrays. R package version 1.1.0
Rerks-Ngarm S, Pitisuttithum P, Nitayaphan S et al (2009) Vaccination with ALVAC and AIDSVAX to prevent HIV-1 infection in Thailand. N Engl J Med 361(23):2209–2220
Gottardo R et al (2013) Plasma IgG to linear epitopes in the V2 and V3 regions of HIV-1 gp120 correlate with a reduced risk of infection in the RV144 vaccine efficacy trial. PLoS One 8:e75665
Molecular Devices (2014) GenePix Pro. http://mdc.custhelp.com/app/answers/detail/a_id/18792/~/genepix%C2%AE-pro-7-microarray-acquisition-%26-analysis-software-download-page
Ritchie M, Silver J, Oshlack A et al (2007) A comparison of background correction methods for two-colour microarrays. Bioinformatics 23(20):2700–2707
Lawrence M, Huber W, Pagès H et al (2013) Software for computing and annotating genomic ranges. PLoS Comput Biol 9(8):e1003118. doi:10.1371/journal.pcbi.1003118
Hellberg S, Sjöström M, Skagerberg B et al (1987) Peptide quantitative structure-activity relationships, a multivariate approach. J Med Chem 30(7):1126–1135
Imholte G, Sauteraud R, Korber B et al (2013) A computational framework for the analysis of peptide microarray antibody binding data with application to HIV vaccine profiling. J Immunol Methods 395(1–2):1–13
Nahtman T, Jernberg A, Mahdavifar S et al (2007) Validation of peptide epitope microarray experiments and extraction of quality data. J Immunol Methods 328:1–13
Bolstad B, Irizarry R, Astrand M et al (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19:185
Sauteraud R, Jiang M, Gottardo R. Pviz (2014) peptide annotation and data visualization using Gviz. R package version 1.1.0
Hahne F, Durinck S, Ivanek R et al. Gviz (2012) plotting data and annotation information along genomic coordinates. R package version 1.8.0
Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B 57(1):289–300
RStudio and Inc (2014) shiny: Web application framework for R. R package version 0.10.2.1. http://CRAN.R-project.org/package=shiny
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media New York
About this protocol
Cite this protocol
Imholte, G., Sauteraud, R., Gottardo, R. (2016). Analyzing Peptide Microarray Data with the R pepStat Package. In: Cretich, M., Chiari, M. (eds) Peptide Microarrays. Methods in Molecular Biology, vol 1352. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3037-1_10
Download citation
DOI: https://doi.org/10.1007/978-1-4939-3037-1_10
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-3036-4
Online ISBN: 978-1-4939-3037-1
eBook Packages: Springer Protocols