Analyzing Peptide Microarray Data with the R pepStat Package.
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.
Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, M2-C200, Seattle, WA, 98109, USA.
Gregory
G
Imholte
Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, M2-C200, Seattle, WA, 98109, USA.
Renan
R
Sauteraud
Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, M2-C200, Seattle, WA, 98109, USA. rgottard@fhcrc.org.
Raphael
R
Gottardo
D000906:Antibodies / Q000276:immunology; D002986:Clinical Trials as Topic; D006801:Humans; D010455:Peptides / Q000276:immunology / Q000378:metabolism*; D040081:Protein Array Analysis / Q000379:methods*; D013223:Statistics as Topic / Q000379:methods*; D014612:Vaccines / Q000276:immunology
D000906:Antibodies; D010455:Peptides; D014612:Vaccines
Background correction; Baseline correction; Data visualization; Decision rule; False discovery rate; Normalization; Sliding mean; Smoothing