title: Subject classification obtained by cluster analysis and principal component analysis applied to flow cytometric data.
abstract: BACKGROUND
  Polychromatic flow cytometry (PFC) allows the simultaneous determination of multiple antigens in the same cell, resulting in the generation of a high number of subsets. As a consequence, data analysis is the main difficulty with this technology. Here we show the use of cluster analysis (CA) and principal component analyses (PCA) to simplify multicolor data visualization and to allow subjects' classification.
  
  
  METHODS
  By eight-colour cytofluorimetric analysis, we investigated the T cell compartment in donors of different age (young, middle-aged, and centenarians). T cell subsets were identified by combining positive and negative expression of antigens. The resulting data set was organized into a matrix and subjected to CA and PCA.
  
  
  RESULTS
  CA clustered people of different ages on the basis of cytofluorimetric profile. PCA of the cellular subsets identified centenarians within a different cluster from young donors, while middle-aged donors were scattered between these groups. These approaches identified T cell phenotypes that changed with increasing age. In young donors, memory T cell subsets tended to be CD127+ and CD95- whereas CD127-, CD95+ phenotypes were found at higher frequencies in people with advanced age.
  
  
  CONCLUSIONS
  Our data suggest the use of bioinformatic approaches to analyze large data-sets generated by PFC and to obtain the rapid identification of key populations that best characterize a group of subjects.
authors.affiliation: Department of Biomedical Sciences, Chair of Immunology, University of Modena and Reggio Emilia, via Campi 287, 41100 Modena, Italy.
authors.forename: Enrico
authors.initials: E
authors.lastname: Lugli
authors.forename: Marcello
authors.initials: M
authors.lastname: Pinti
authors.forename: Milena
authors.initials: M
authors.lastname: Nasi
authors.forename: Leonarda
authors.initials: L
authors.lastname: Troiano
authors.forename: Roberta
authors.initials: R
authors.lastname: Ferraresi
authors.forename: Chiara
authors.initials: C
authors.lastname: Mussi
authors.forename: Gianfranco
authors.initials: G
authors.lastname: Salvioli
authors.forename: Valeri
authors.initials: V
authors.lastname: Patsekin
authors.forename: J Paul
authors.initials: JP
authors.lastname: Robinson
authors.forename: Caterina
authors.initials: C
authors.lastname: Durante
authors.forename: Marina
authors.initials: M
authors.lastname: Cocchi
authors.forename: Andrea
authors.initials: A
authors.lastname: Cossarizza
mesh_terms: D051997:ADP-ribosyl Cyclase 1 / Q000032:analysis; D000328:Adult; D000369:Aged, 80 and over; D000375:Aging / Q000276:immunology; D015496:CD4-Positive T-Lymphocytes / Q000166:cytology / Q000378:metabolism; D018414:CD8-Positive T-Lymphocytes / Q000166:cytology / Q000378:metabolism; D002965:Classification; D016000:Cluster Analysis; D005260:Female; D005434:Flow Cytometry / Q000379:methods*; D006801:Humans; D007156:Immunologic Memory; D053699:Interleukin-7 Receptor alpha Subunit / Q000032:analysis; D008297:Male; D008875:Middle Aged; D025341:Principal Component Analysis; D019014:fas Receptor / Q000032:analysis
chemical_list: D053699:Interleukin-7 Receptor alpha Subunit; D019014:fas Receptor; D051997:ADP-ribosyl Cyclase 1