Application of three-way principal component analysis to the evaluation of two-dimensional maps in proteomics

Emilio Marengo; Riccardo Leardi; Elisa Robotti; Pier Giorgio Righetti; Francesca Antonucci; Daniela Cecconi

doi:10.1021/pr030002t

Application of three-way principal component analysis to the evaluation of two-dimensional maps in proteomics

Emilio Marengo, Riccardo Leardi, Elisa Robotti, Pier Giorgio Righetti, Francesca Antonucci, Daniela Cecconi

Dipartimento di Scienze e Innovazione Tecnologica

Risultato della ricerca: Contributo su rivista › Articolo in rivista › peer review

Abstract

Three-way PCA has been applied to proteomic pattern images to identify the classes of samples present in the dataset. The developed method has been applied to two different datasets: a rat sera dataset, constituted by five samples of healthy Wistar rat sera and five samples of nicotine-treated Wistar rat sera; a human lymph-node dataset constituted by four healthy lymph-nodes and four lymph-nodes affected by a non-Hodgkin's lymphoma. The method proved to be successful in the identification of the classes of samples present in both of the groups of 2D-PAGE images, and it allowed us to identify the regions of the two-dimensional maps responsible for the differences occurring between the classes for both rat sera and human lymph-nodes datasets.

Lingua originale	Inglese
pagine (da-a)	351-360
Numero di pagine	10
Rivista	Journal of Proteome Research
Volume	2
Numero di pubblicazione	4
DOI	https://doi.org/10.1021/pr030002t
Stato di pubblicazione	Pubblicato - lug 2003

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title = "Application of three-way principal component analysis to the evaluation of two-dimensional maps in proteomics",

abstract = "Three-way PCA has been applied to proteomic pattern images to identify the classes of samples present in the dataset. The developed method has been applied to two different datasets: a rat sera dataset, constituted by five samples of healthy Wistar rat sera and five samples of nicotine-treated Wistar rat sera; a human lymph-node dataset constituted by four healthy lymph-nodes and four lymph-nodes affected by a non-Hodgkin's lymphoma. The method proved to be successful in the identification of the classes of samples present in both of the groups of 2D-PAGE images, and it allowed us to identify the regions of the two-dimensional maps responsible for the differences occurring between the classes for both rat sera and human lymph-nodes datasets.",

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author = "Emilio Marengo and Riccardo Leardi and Elisa Robotti and Righetti, {Pier Giorgio} and Francesca Antonucci and Daniela Cecconi",

year = "2003",

month = jul,

doi = "10.1021/pr030002t",

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T1 - Application of three-way principal component analysis to the evaluation of two-dimensional maps in proteomics

AU - Marengo, Emilio

AU - Leardi, Riccardo

AU - Robotti, Elisa

AU - Righetti, Pier Giorgio

AU - Antonucci, Francesca

AU - Cecconi, Daniela

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AB - Three-way PCA has been applied to proteomic pattern images to identify the classes of samples present in the dataset. The developed method has been applied to two different datasets: a rat sera dataset, constituted by five samples of healthy Wistar rat sera and five samples of nicotine-treated Wistar rat sera; a human lymph-node dataset constituted by four healthy lymph-nodes and four lymph-nodes affected by a non-Hodgkin's lymphoma. The method proved to be successful in the identification of the classes of samples present in both of the groups of 2D-PAGE images, and it allowed us to identify the regions of the two-dimensional maps responsible for the differences occurring between the classes for both rat sera and human lymph-nodes datasets.

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Application of three-way principal component analysis to the evaluation of two-dimensional maps in proteomics

Abstract

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