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Immune repertoire fingerprinting by principal component analysis reveals shared features in subject groups with common exposures

dc.contributor.authorSevy, Alexander M.
dc.contributor.authorSoto, Cinque
dc.contributor.authorBombardi, Robin G.
dc.contributor.authorMeiler, Jens
dc.contributor.authorCrowe, James E., Jr.
dc.identifier.citationSevy, A. M., Soto, C., Bombardi, R. G., Meiler, J., & Crowe, J. E., Jr (2019). Immune repertoire fingerprinting by principal component analysis reveals shared features in subject groups with common exposures. BMC bioinformatics, 20(1), 629.
dc.description.abstractBackground: Advances in next-generation sequencing (NGS) of antibody repertoires have led to an explosion in B cell receptor sequence data from donors with many different disease states. These data have the potential to detect patterns of immune response across populations. However, to this point it has been difficult to interpret such patterns of immune response between disease states in the absence of functional data. There is a need for a robust method that can be used to distinguish general patterns of immune responses at the antibody repertoire level. Results: We developed a method for reducing the complexity of antibody repertoire datasets using principal component analysis (PCA) and refer to our method as "repertoire fingerprinting." We reduce the high dimensional space of an antibody repertoire to just two principal components that explain the majority of variation in those repertoires. We show that repertoires from individuals with a common experience or disease state can be clustered by their repertoire fingerprints to identify common antibody responses. Conclusions: Our repertoire fingerprinting method for distinguishing immune repertoires has implications for characterizing an individual disease state. Methods to distinguish disease states based on pattern recognition in the adaptive immune response could be used to develop biomarkers with diagnostic or prognostic utility in patient care. Extending our analysis to larger cohorts of patients in the future should permit us to define more precisely those characteristics of the immune response that result from natural infection or autoimmunity.en_US
dc.description.sponsorshipThis work was supported by the National Institutes of Health [U19 AI117905 to JEC and JM] and a grant from the Human Vaccines Project, Inc. [to JEC]. The funding bodies did not play any role in the design of the study or collection, analysis, or interpretation of data or in writing the manuscript.en_US
dc.publisherBMC Bioinformaticsen_US
dc.rightsCopyright © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.
dc.subjectImmune repertoire analysisen_US
dc.subjectPrincipal component analysisen_US
dc.subjectAntibody sequencingen_US
dc.subjectRepertoire dissimilarity indexen_US
dc.titleImmune repertoire fingerprinting by principal component analysis reveals shared features in subject groups with common exposuresen_US

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