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Clustering Time-Series Gene Expression Data Using Smoothing Spline Derivatives

EURASIP Journal on Bioinformatics and Systems Biology20072007:70561

Received: 14 December 2006

Accepted: 16 May 2007

Published: 18 June 2007


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.


Gene Expression DataMouse LiverHeuristic ApproachAbsolute LevelSmoothing Parameter


Authors’ Affiliations

Laboratoire de Statistique et Probabilités, UMR 5583, Université Paul Sabatier, Toulouse, France
Laboratoire de Pharmacologie et Toxicologie, UR 66, Institut National de la Recherche Agronomique (INRA), Toulouse, France


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© S. Déjean et al. 2007

This article is published under license to BioMed Central Ltd. 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.