A computational framework for the analysis of peptide microarray antibody binding data with application to HIV vaccine profiling

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• 作者：Greg C. Imholte ; Renan Sauteraud ; Bette Korber ; Robert T. Bailer ; Ellen T. Turk ; Xiaoying Shen ; Georgia D. Tomaras ; John R. Mascola ; Richard A. Koup ; David C. Montefiori ; Raphael Gottardo
• 关键词：Peptide microarrays ; Antibodies ; Normalization ; Positivity calls ; Software ; Visualization
• 刊名：Journal of Immunological Methods
• 出版年：2013
• 出版时间：30 September, 2013
• 年：2013
• 卷：395
• 期：1-2
• 页码：1-13
• 全文大小：3936 K

文摘

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.