Subject classification obtained by cluster analysis and principal component analysis applied to flow cytometric data
| Autor: | Milena Nasi, Enrico Lugli, Marcello Pinti, Valeri Patsekin, Roberta Ferraresi, Caterina Durante, J. Paul Robinson, Chiara Mussi, Gianfranco Salvioli, Andrea Cossarizza, Leonarda Troiano, Marina Cocchi |
|---|---|
| Rok vydání: | 2007 |
| Předmět: |
Adult
CD4-Positive T-Lymphocytes Male Aging Histology T cell Computational biology CD8-Positive T-Lymphocytes Biology Bioinformatics Pathology and Forensic Medicine Flow cytometry Interleukin-7 Receptor alpha Subunit Text mining Antigen medicine Cluster Analysis Humans fas Receptor Interleukin-7 receptor Aged 80 and over Principal Component Analysis medicine.diagnostic_test business.industry Cell Biology Middle Aged Classification Flow Cytometry ADP-ribosyl Cyclase 1 Data set medicine.anatomical_structure Principal component analysis Female business Immunologic Memory Memory T cell |
| Zdroj: | Cytometry Part A. :334-344 |
| ISSN: | 1552-4930 1552-4922 |
| DOI: | 10.1002/cyto.a.20387 |
| Popis: | 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. © 2007 International Society for Analytical Cytology |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Plný text