dorsal/arxiv
View SchemaSpectral analysis of gene expression profiles using gene networks
| Authors | Franck Rapaport, Andrei Zinovyev, Marie Dutreix, Emmanuel Barillot, Jean-Philippe Vert |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0603030 |
| URL | https://arxiv.org/abs/q-bio/0603030 |
Abstract
Microarrays have become extremely useful for analysing genetic phenomena, but establishing a relation between microarray analysis results (typically a list of genes) and their biological significance is often difficult. Currently, the standard approach is to map a posteriori the results onto gene networks to elucidate the functions perturbed at the level of pathways. However, integrating a priori knowledge of the gene networks could help in the statistical analysis of gene expression data and in their biological interpretation. Here we propose a method to integrate a priori the knowledge of a gene network in the analysis of gene expression data. The approach is based on the spectral decomposition of gene expression profiles with respect to the eigenfunctions of the graph, resulting in an attenuation of the high-frequency components of the expression profiles with respect to the topology of the graph. We show how to derive unsupervised and supervised classification algorithms of expression profiles, resulting in classifiers with biological relevance. We applied the method to the analysis of a set of expression profiles from irradiated and non-irradiated yeast strains. It performed at least as well as the usual classification but provides much more biologically relevant results and allows a direct biological interpretation.
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"abstract": "Microarrays have become extremely useful for analysing genetic phenomena, but\nestablishing a relation between microarray analysis results (typically a list\nof genes) and their biological significance is often difficult. Currently, the\nstandard approach is to map a posteriori the results onto gene networks to\nelucidate the functions perturbed at the level of pathways. However,\nintegrating a priori knowledge of the gene networks could help in the\nstatistical analysis of gene expression data and in their biological\ninterpretation. Here we propose a method to integrate a priori the knowledge of\na gene network in the analysis of gene expression data. The approach is based\non the spectral decomposition of gene expression profiles with respect to the\neigenfunctions of the graph, resulting in an attenuation of the high-frequency\ncomponents of the expression profiles with respect to the topology of the\ngraph. We show how to derive unsupervised and supervised classification\nalgorithms of expression profiles, resulting in classifiers with biological\nrelevance. We applied the method to the analysis of a set of expression\nprofiles from irradiated and non-irradiated yeast strains. It performed at\nleast as well as the usual classification but provides much more biologically\nrelevant results and allows a direct biological interpretation.",
"arxiv_id": "q-bio/0603030",
"authors": [
"Franck Rapaport",
"Andrei Zinovyev",
"Marie Dutreix",
"Emmanuel Barillot",
"Jean-Philippe Vert"
],
"categories": [
"q-bio.QM"
],
"title": "Spectral analysis of gene expression profiles using gene networks",
"url": "https://arxiv.org/abs/q-bio/0603030"
},
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