title: Lessons learned in the analysis of high-dimensional data in vaccinomics.
abstract: The field of vaccinology is increasingly moving toward the generation, analysis, and modeling of extremely large and complex high-dimensional datasets. We have used data such as these in the development and advancement of the field of vaccinomics to enable prediction of vaccine responses and to develop new vaccine candidates. However, the application of systems biology to what has been termed "big data," or "high-dimensional data," is not without significant challenges-chief among them a paucity of gold standard analysis and modeling paradigms with which to interpret the data. In this article, we relate some of the lessons we have learned over the last decade of working with high-dimensional, high-throughput data as applied to the field of vaccinomics. The value of such efforts, however, is ultimately to better understand the immune mechanisms by which protective and non-protective responses to vaccines are generated, and to use this information to support a personalized vaccinology approach in creating better, and safer, vaccines for the public health.
authors.affiliation: Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA; Mayo Clinic Vaccine Research Group, Mayo Clinic, Rochester, MN, USA.
authors.forename: Ann L
authors.initials: AL
authors.lastname: Oberg
authors.affiliation: Tandy School of Computer Science, Department of Mathematics, University of Tulsa, Tulsa, OK, USA.
authors.forename: Brett A
authors.initials: BA
authors.lastname: McKinney
authors.affiliation: Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA; Mayo Clinic Vaccine Research Group, Mayo Clinic, Rochester, MN, USA.
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Schaid
authors.affiliation: UNM Health Sciences Library & Informatics Center, Division of Nephrology, University of New Mexico, Albuquerque, NM, USA.
authors.forename: V Shane
authors.initials: VS
authors.lastname: Pankratz
authors.affiliation: Mayo Clinic Vaccine Research Group, Mayo Clinic, Rochester, MN, USA.
authors.forename: Richard B
authors.initials: RB
authors.lastname: Kennedy
authors.affiliation: Mayo Clinic Vaccine Research Group, Mayo Clinic, Rochester, MN, USA. Electronic address: poland.gregory@mayo.edu.
authors.forename: Gregory A
authors.initials: GA
authors.lastname: Poland
mesh_terms: D000068598:Data Accuracy; D003627:Data Interpretation, Statistical; D057225:Data Mining; D066264:Datasets as Topic; D020869:Gene Expression Profiling; D023281:Genomics / Q000379:methods; D006801:Humans; D049490:Systems Biology / Q000379:methods*; D014612:Vaccines / Q000276:immunology*
chemical_list: D014612:Vaccines
keywords: Data interpretation, statistical; Immunogenetics; Systems biology; Vaccination; Vaccines