Bayesian hierarchical modeling for subject-level response classification in peptide microarray immunoassays

• Published in: Biometrics Vol. 72; no. 4; pp. 1206 - 1215
• Main Authors: Imholte, Gregory, Gottardo, Raphael
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.12.2016; Wiley-Blackwell
• Subjects: Adaptive control; AIDS Vaccines - immunology; Antibodies; Antibodies - analysis; Antibody response; Bayes Theorem; Bayesian analysis; Bayesian hierarchical model; BIOMETRIC METHODOLOGY; biometry; blood serum; Classification; Computer Simulation; data collection; False Positive Reactions; Humans; Immune system; Immunoassay; Immunoassay - statistics & numerical data; immunoassays; Immunogenicity; Mapping; microarray technology; Mixture modeling; model validation; Models, Statistical; Outliers (statistics); pathogens; Peptide microarray; Peptides; probability; Probes; Protein Array Analysis; Proteins; Randomized Controlled Trials as Topic; ROC Curve; Screens; Sensitivity; Tiling; Vaccines; Viruses; Bayesian hierarchical model; Mixture modeling; Classification; Peptide microarray
• ISSN: 0006-341X; 1541-0420; 1541-0420
• DOI: 10.1111/biom.12523

The peptide microarray immunoassay simultaneously screens sample serum against thousands of peptides, determining the presence of antibodies bound to array probes. Peptide microarrays tiling immunogenic regions of pathogens (e.g., envelope proteins of a virus) are an important high throughput tool for querying and mapping antibody binding. Because of the assay's many steps, from probe synthesis to incubation, peptide microarray data can be noisy with extreme outliers. In addition, subjects may produce different antibody profiles in response to an identical vaccine stimulus or infection, due to variability among subjects' immune systems. We present a robust Bayesian hierarchical model for peptide microarray experiments, pepBayes, to estimate the probability of antibody response for each subject/peptide combination. Heavy-tailed error distributions accommodate outliers and extreme responses, and tailored random effect terms automatically incorporate technical effects prevalent in the assay. We apply our model to two vaccine trial data sets to demonstrate model performance. Our approach enjoys high sensitivity and specificity when detecting vaccine induced antibody responses. A simulation study shows an adaptive thresholding classification method has appropriate false discovery rate control with high sensitivity, and receiver operating characteristics generated on vaccine trial data suggest that pepBayes clearly separates responses from non-responses.