Abstract
BackgroundImmunosignaturing is a new peptide microarray based technology for profiling of humoral immune responses. Despite new challenges, immunosignaturing gives us the opportunity to explore new and fundamentally different research questions. In addition to classifying samples based on disease status, the complex patterns and latent factors underlying immunosignatures, which we attempt to model, may have a diverse range of applications.MethodsWe investigate the utility of a number of statistical methods to determine model performance and address challenges inherent in analyzing immunosignatures. Some of these methods include exploratory and confirmatory factor analyses, classical significance testing, structural equation and mixture modeling.ResultsWe demonstrate an ability to classify samples based on disease status and show that immunosignaturing is a very promising technology for screening and presymptomatic screening of disease. In addition, we are able to model complex patterns and latent factors underlying immunosignatures. These latent factors may serve as biomarkers for disease and may play a key role in a bioinformatic method for antibody discovery.ConclusionBased on this research, we lay out an analytic framework illustrating how immunosignatures may be useful as a general method for screening and presymptomatic screening of disease as well as antibody discovery.
Highlights
Immunosignaturing is a new peptide microarray based technology for profiling of humoral immune responses
In this paper we present a range of statistical methods, their use and demonstrate what type of information they can provide researchers in immunosignaturing studies
549 339301 49844 statistics of an immunosignature provide a significant amount of insight into the underlying immune response during disease states
Summary
Immunosignaturing is a new peptide microarray based technology for profiling of humoral immune responses. In addition to classifying samples based on disease status, the complex patterns and latent factors underlying immunosignatures, which we attempt to model, may have a diverse range of applications. The human immune system is a rich source of information about the health and disease status of an individual [1,2,3,4]. Immunosignaturing is a new technology that may be useful to decode the vast amounts of health information contained in the immune system. An immunosignature is a pattern containing multiplexed signals from chronic or recently matured antibodies. These signals come from a sufficiently diverse set of peptide targets on a microarray. Previous reports [1,2,3] used frequentist statistics (ANOVA or t-test) and cluster analysis (hierarchical clustering and Principal Components) to
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