Paper ID sheet
- TITLE: Geometric optimization methods for the analysis of gene expression data
- AUTHORS: M. Journée, A. E. Teschendorff, P.-A. Absil, S. Tavaré, R. Sepulchre
- ABSTRACT:
DNA microarrays provide such a huge amount of data that unsupervised methods are required to reduce the dimension of the data set and to extract meaningul biological information. This work shows that Independent Component Analysis (ICA) is a promising approach for the analysis of genome-wide transcriptomic data. The paper first presents an overview of the most popular algorithms to perform ICA. These algorithms are then applied on a microarray breatst-cancer data set. Some issues about the application of ICA and the evaluation of biological relevance of the results are discussed. This study indicates that ICA significantly outperforms Principal Component Analysis (PCA).
- KEY WORDS:
- STATUS: Principal Manifolds for Data Visualization and Dimension Reduction, Lecture Notes in Computational Science and Engineering, Springer Berlin Heidelberg, Volume 58, page 271--292 - 2007.
BibTeX citation:
@InCollection{JTATS07,
author = "Journ\'ee, Michel and Teschendorff, Andrew and Absil, Pierre-Antoine and Tavar\'e, Simon and Sepulchre, Rodolphe",
title = "Geometric Optimization Methods for the Analysis of Gene Expression Data",
booktitle = "Principal Manifolds for Data Visualization and Dimension Reduction",
chapter = "12",
series = "Lecture Notes in Computational Science and Engineering",
volume = "58",
pages = "271--292",
year = "2007",
editor = "Alexander N. Gorban, Bal\'azs K\'egl, Donald C. Wunsch and Andrei Y. Zinovyev",
publisher = "Springer Berlin Heidelberg",
url = "http://www.montefiore.ulg.ac.be/services/stochastic/pubs/2007/JTATS07"
}
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