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Clustering Time-Series Gene Expression Data Using Smoothing Spline Derivatives
Microarray data acquired during time-course experiments allow the temporal variations in gene expression to be monitored. An original postprandial fasting experiment was conducted in the mouse and the expression of 200 genes was monitored with a dedicated macroarray at 11 time points between 0 and 7...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer
2007
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3171348/ https://www.ncbi.nlm.nih.gov/pubmed/17713590 http://dx.doi.org/10.1155/2007/70561 |
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author | Déjean, S Martin, PGP Baccini, A Besse, P |
author_facet | Déjean, S Martin, PGP Baccini, A Besse, P |
author_sort | Déjean, S |
collection | PubMed |
description | Microarray data acquired during time-course experiments allow the temporal variations in gene expression to be monitored. An original postprandial fasting experiment was conducted in the mouse and the expression of 200 genes was monitored with a dedicated macroarray at 11 time points between 0 and 72 hours of fasting. The aim of this study was to provide a relevant clustering of gene expression temporal profiles. This was achieved by focusing on the shapes of the curves rather than on the absolute level of expression. Actually, we combined spline smoothing and first derivative computation with hierarchical and partitioning clustering. A heuristic approach was proposed to tune the spline smoothing parameter using both statistical and biological considerations. Clusters are illustrated a posteriori through principal component analysis and heatmap visualization. Most results were found to be in agreement with the literature on the effects of fasting on the mouse liver and provide promising directions for future biological investigations. |
format | Online Article Text |
id | pubmed-3171348 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2007 |
publisher | Springer |
record_format | MEDLINE/PubMed |
spelling | pubmed-31713482011-09-13 Clustering Time-Series Gene Expression Data Using Smoothing Spline Derivatives Déjean, S Martin, PGP Baccini, A Besse, P EURASIP J Bioinform Syst Biol Research Article Microarray data acquired during time-course experiments allow the temporal variations in gene expression to be monitored. An original postprandial fasting experiment was conducted in the mouse and the expression of 200 genes was monitored with a dedicated macroarray at 11 time points between 0 and 72 hours of fasting. The aim of this study was to provide a relevant clustering of gene expression temporal profiles. This was achieved by focusing on the shapes of the curves rather than on the absolute level of expression. Actually, we combined spline smoothing and first derivative computation with hierarchical and partitioning clustering. A heuristic approach was proposed to tune the spline smoothing parameter using both statistical and biological considerations. Clusters are illustrated a posteriori through principal component analysis and heatmap visualization. Most results were found to be in agreement with the literature on the effects of fasting on the mouse liver and provide promising directions for future biological investigations. Springer 2007-06-18 /pmc/articles/PMC3171348/ /pubmed/17713590 http://dx.doi.org/10.1155/2007/70561 Text en Copyright © 2007 S. Déjean et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Déjean, S Martin, PGP Baccini, A Besse, P Clustering Time-Series Gene Expression Data Using Smoothing Spline Derivatives |
title | Clustering Time-Series Gene Expression Data Using Smoothing Spline Derivatives |
title_full | Clustering Time-Series Gene Expression Data Using Smoothing Spline Derivatives |
title_fullStr | Clustering Time-Series Gene Expression Data Using Smoothing Spline Derivatives |
title_full_unstemmed | Clustering Time-Series Gene Expression Data Using Smoothing Spline Derivatives |
title_short | Clustering Time-Series Gene Expression Data Using Smoothing Spline Derivatives |
title_sort | clustering time-series gene expression data using smoothing spline derivatives |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3171348/ https://www.ncbi.nlm.nih.gov/pubmed/17713590 http://dx.doi.org/10.1155/2007/70561 |
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