1. A computational framework for the analysis of peptide microarray antibody binding data with application to HIV vaccine profiling
- Author
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John R. Mascola, Raphael Gottardo, Richard A. Koup, Ellen Turk, Xiaoying Shen, Renan Sauteraud, Greg C. Imholte, David C. Montefiori, Bette T. Korber, Georgia D. Tomaras, and Robert T. Bailer
- Subjects
Normalization (statistics) ,HIV Antigens ,Computer science ,Immunology ,Protein Array Analysis ,Computational biology ,HIV Antibodies ,Bioinformatics ,computer.software_genre ,Article ,Epitopes ,Antibody Specificity ,Protein Interaction Mapping ,Humans ,Immunology and Allergy ,Profiling (information science) ,HIV vaccine ,Peptide sequence ,AIDS Vaccines ,Clinical Trials as Topic ,Antigen binding ,Visualization ,ROC Curve ,Data Interpretation, Statistical ,HIV-1 ,Immunologic Techniques ,Peptide microarray ,computer ,Epitope Mapping ,Data integration - Abstract
We present an integrated analytical method for analyzing peptide microarray antibody binding data, from normalization through subject-specific positivity calls and data integration and visualization. Current techniques for the normalization of such data sets do not account for non-specific binding activity. A novel normalization technique based on peptide sequence information quickly and effectively reduced systematic biases. We also employed a sliding mean window technique that borrows strength from peptides sharing similar sequences, resulting in reduced signal variability. A smoothed signal aided in the detection of weak antibody binding hotspots. A new principled FDR method of setting positivity thresholds struck a balance between sensitivity and specificity. In addition, we demonstrate the utility and importance of using baseline control measurements when making subject-specific positivity calls. Data sets from two human clinical trials of candidate HIV-1 vaccines were used to validate the effectiveness of our overall computational framework.
- Published
- 2013