TY - JOUR
T1 - Subject classification obtained by cluster analysis and principal component analysis applied to flow cytometric data
AU - Lugli, Enrico
AU - Pinti, Marcello
AU - Nasi, Milena
AU - Troiano, Leonarda
AU - Ferraresi, Roberta
AU - Mussi, Chiara
AU - Salvioli, Gianfranco
AU - Patsekin, Valeri
AU - Robinson, J. Paul
AU - Durante, Caterina
AU - Cocchi, Marina
AU - Cossarizza, Andrea
PY - 2007/5/1
Y1 - 2007/5/1
N2 - 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.
AB - 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.
KW - Cluster analysis
KW - Data analysis
KW - Polychromatic flow cytometry
KW - Principal component analysis
KW - Subject classification
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U2 - 10.1002/cyto.a.20387
DO - 10.1002/cyto.a.20387
M3 - Article
C2 - 17352421
AN - SCOPUS:34248139722
VL - 71
SP - 334
EP - 344
JO - Cytometry. Part A : the journal of the International Society for Analytical Cytology
JF - Cytometry. Part A : the journal of the International Society for Analytical Cytology
SN - 1552-4922
IS - 5
ER -