A Weighted Principal Component Analysis and Its Application to Gene Expression Data

• Tipo: Artigo em Revista Científica Internacional
• Data: 2011

Autores

Joaquim Pinto P da Costa (Autor) FCUP; Hugo Alonso (Autor) FEUP; Luis Roque (Autor) Outra

Revista

Título: IEEE/ACM Transactions on Computational Biology and Bioinformatics

Vol. 8

Páginas: 246-252

ISSN: 1545-5963

Editora: IEEE

Indexação

ISI Web of Knowledge - 29 Citações

Scopus - 47 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

ID Authenticus: P-002-XVZ

DOI: 10.1109/tcbb.2009.61

Abstract (EN): In this work, we introduce in the first part new developments in Principal Component Analysis (PCA) and in the second part a new method to select variables (genes in our application). Our focus is on problems where the values taken by each variable do not all have the same importance and where the data may be contaminated with noise and contain outliers, as is the case with microarray data. The usual PCA is not appropriate to deal with this kind of problems. In this context, we propose the use of a new correlation coefficient as an alternative to Pearson's. This leads to a so-called weighted PCA (WPCA). In order to illustrate the features of our WPCA and compare it with the usual PCA, we consider the problem of analyzing gene expression data sets. In the second part of this work, we propose a new PCA-based algorithm to iteratively select the most important genes in a microarray data set. We show that this algorithm produces better results when our WPCA is used instead of the usual PCA. Furthermore, by using Support Vector Machines, we show that it can compete with the Significance Analysis of Microarrays algorithm.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 7