title: Multivariate statistical analysis of large-scale IgE antibody measurements reveals allergen extract relationships in sensitized individuals.
abstract: BACKGROUND
  Many allergenic sources are reportedly cross-reactive because of protein structural similarities. Although several aggregations are well characterized, no holistic mapping of IgE reactivity has hitherto been reported.
  
  
  OBJECTIVE
  The aim of this study was to disclose relevant associations within a large set of allergen preparations, as revealed by specific IgE antibody levels in blood sera of multireactive human donors.
  
  
  METHODS
  A dataset of recorded IgE antibody serum concentrations of 1011 nonidentifiable multireactive individuals (devoid of clinical records) to 89 allergen extracts was compiled for in silico analysis. Various algorithms were used to identify specific multivariate dependencies between the IgE antibody levels.
  
  
  RESULTS
  Exhaustive cluster analysis demonstrates that IgE antibody responses to the 89 extracts can be aggregated into 12 stable formations. These clusters hold both well-known relationships, unexpected patterns, and unknown patterns, the latter categories being exemplified by the coclustering of wasp and certain seafood and a clear differentiation among pollen allergens.
  
  
  CONCLUSIONS
  Identified relationships within several well-known groups of cross-reactive allergen extracts confirm the applicability of dedicated multivariate data analysis within the allergology field. Moreover, some of the unexpected IgE reactivity associations in sensitized human subjects might help in identifying new relationships with potential importance to allergy.
  
  
  CONCLUSIONS
  Although clinical implications from this study should be validated in subsequent investigations with documentation on symptoms included, we believe this seminal approach is a key step toward the development of new analysis tools for interpretation of allergy data generated by using high-throughput recording systems.
authors.affiliation: Division of Toxicology, National Food Administration, Uppsala, Sweden.
authors.forename: Daniel
authors.initials: D
authors.lastname: Soeria-Atmadja
authors.forename: Annica
authors.initials: A
authors.lastname: Onell
authors.forename: Anita
authors.initials: A
authors.lastname: Kober
authors.forename: Per
authors.initials: P
authors.lastname: Matsson
authors.forename: Mats G
authors.initials: MG
authors.lastname: Gustafsson
authors.forename: Ulf
authors.initials: U
authors.lastname: Hammerling
mesh_terms: D000465:Algorithms; D000485:Allergens / Q000276:immunology*; D000818:Animals; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods / Q000706:statistics & numerical data*; D003429:Cross Reactions; D006801:Humans; D007114:Immunization; D007073:Immunoglobulin E / Q000032:analysis* / Q000096:biosynthesis / Q000097:blood; D015999:Multivariate Analysis; D010363:Pattern Recognition, Automated / Q000706:statistics & numerical data; D010936:Plant Extracts / Q000276:immunology; D014020:Tissue Extracts / Q000276:immunology