1687-4153-2007-70561 1687-4153 Research Article <p>Clustering Time-Series Gene Expression Data Using Smoothing Spline Derivatives</p> DéjeanSsebastien.dejean@math.ups-tlse.fr MartinPGPpascal.martin@toulouse.inra.fr BacciniAalain.baccini@math.ups-tlse.fr BessePphilippe.besse@math.ups-tlse.fr

Laboratoire de Statistique et Probabilités, UMR 5583, Université Paul Sabatier, Toulouse Cedex 9 31062, France

Laboratoire de Pharmacologie et Toxicologie, UR 66, Institut National de la Recherche Agronomique (INRA), 180 Chemin de Tournefeuille, BP 3, Toulouse Cedex 9 31931, France

 EURASIP Journal on Bioinformatics and Systems Biology 1687-4153 2007 2007 1 70561 http://bsb.eurasipjournals.com/content/2007/1/70561 10.1155/2007/70561 1412200663200716520071862007 2007S. Déjean et al.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.

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.

[123456789101112131415161718192021222324]

 <p>Statistical tests for identifying differentially expressed genes in time-course microarray experiments</p>ParkTYiS-GLeeSBioinformatics200319669470310.1093/bioinformatics/btg06812691981<p>Gene selection and clustering for time-course and dose-response microarray experiments using order-restricted inference</p>PeddadaSDLobenhoferEKLiLAfshariCAWeinbergCRUmbachDMBioinformatics200319783484110.1093/bioinformatics/btg09312724293<p>Significance analysis of time course microarray experiments</p>StoreyJDXiaoWLeekJTTompkinsRGDavisRWProceedings of the National Academy of Sciences of the United States of America200510236128371284210.1073/pnas.0504609102120169716141318<p>A multivariate empirical Bayes statistic for replicated microarray time course data</p>TaiYCSpeedTPThe Annals of Statistics20063452387241210.1214/009053606000000759<p>Cluster analysis of gene expression dynamics</p>RamoniMFSebastianiPKohaneISProceedings of the National Academy of Sciences of the United States of America200299149121912610.1073/pnas.13265639912310412082179<p>Clustering short time series gene expression data</p>ErnstJNauGJBar-JosephZBioinformatics2005211i159i16810.1093/bioinformatics/bti102215961453<p>Clustering time series gene expression data based on sum-of-exponentials fitting</p>GiurcǎneanuCDTǎbuşIAstolaJEURASIP Journal on Applied Signal Processing2005200581159117310.1155/ASP.2005.1159<p>Bayesian coclustering of Anopheles gene expression time series: study of immune defense response to multiple experimental challenges</p>HeardNAHolmesCCStephensDAHandDJDimopoulosGProceedings of the National Academy of Sciences of the United States of America200510247169391694410.1073/pnas.0408393102128796116287981<p>maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments</p>ConesaANuedaMJFerrerATalónMBioinformatics20062291096110210.1093/bioinformatics/btl05616481333<p>Designing better probes: effect of probe size, mismatch position and number on hybridization in DNA oligonucleotide microarrays</p>LetowskiJBrousseauRMassonLJournal of Microbiological Methods200457226927810.1016/j.mimet.2004.02.00215063067RamsayJSilvermanBFunctional Data AnalysisSpringer, New York, NY, USA22005<p>Continuous representations of time-series gene expression data</p>Bar-JosephZGerberGKGiffordDKJaakkolaTSSimonIJournal of Computational Biology2003103-434135610.1089/1066527036068805712935332<p>Analyzing time series gene expression data</p>Bar-JosephZBioinformatics200420162493250310.1093/bioinformatics/bth28315130923<p>Transcriptional modulations by RXR agonists are only partially subordinated to PPAR<inline-formula><graphic file="1687-4153-2007-70561-i1.gif"/></inline-formula> signaling and attest additional, organ-specific, molecular cross-talks</p>MartinPGPLasserreFCallejaCGene Expression200512317719210.3727/00000000578399209816128002<p>Novel aspects of PPAR<inline-formula><graphic file="1687-4153-2007-70561-i2.gif"/></inline-formula>-mediated regulation of lipid and xenobiotic metabolism revealed through a nutrigenomic study</p>MartinPGPGuillouHLasserreFHepatology200745376777710.1002/hep.2151017326203<p>Laboratoire de Pharmacologie et Toxicologie, INRA</p>INRArray2005http://www.inra.fr/internet/Centres/toulouse/pharmacologie/lpt.htm<p>Some aspects of the spline smoothing approach to non-parametric regression curve fitting</p>SilvermanBJournal of the Royal Statistical Society: Series B1985471152<p>Simultaneous non-parametric regressions of unbalanced longitudinal data</p>BessePCardotHFerratyFComputational Statistics & Data Analysis199724325527010.1016/S0167-9473(96)00067-921757463SeberGAFMultivariate ObservationsJohn Wiley & Sons, New York, NY, USA1984<p>Principal component analysis for clustering gene expression data</p>YeungKYRuzzoWLBioinformatics200117976377410.1093/bioinformatics/17.9.76311590094<p>Clustering microarray data</p>ChipmanHHastieTJTibshiraniTStatistical Analysis of Gene Expression Microarray DataChapmann & Hall/CRC Press, Boca Raton, Fla, USASpeed T2003159200<p>Peroxisome proliferator-activated receptor <inline-formula><graphic file="1687-4153-2007-70561-i3.gif"/></inline-formula> mediates the adaptive response to fasting</p>KerstenSSeydouxJPetersJMGonzalezFJDesvergneBWahliWJournal of Clinical Investigation1999103111489149810.1172/JCI622340837210359558<p>Peroxisome proliferator-activated receptor <inline-formula><graphic file="1687-4153-2007-70561-i4.gif"/></inline-formula> target genes</p>MandardSMüllerMKerstenSCellular and Molecular Life Sciences200461439341610.1007/s00018-003-3216-314999402<p>Starvation response in mouse liver shows strong correlation with life-span-prolonging processes</p>BauerMHammACBonausMPhysiological Genomics200417223024410.1152/physiolgenomics.00203.200314762175