Skip to content

Advertisement

EURASIP Journal on Bioinformatics and Systems Biology

EURASIP Journal on Bioinformatics and Systems Biology Cover Image
Research Article
Open Access

Clustering Time-Series Gene Expression Data Using Smoothing Spline Derivatives

EURASIP Journal on Bioinformatics and Systems Biology20072007:70561

https://doi.org/10.1155/2007/70561

©  S. Déjean et al. 2007

Received: 14 December 2006

Accepted: 16 May 2007

Published: 18 June 2007

Abstract

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.

Keywords

Gene Expression DataMouse LiverHeuristic ApproachAbsolute LevelSmoothing Parameter

[123456789101112131415161718192021222324]

Authors’ Affiliations

(1)
Laboratoire de Statistique et Probabilités, UMR 5583, Université Paul Sabatier, Toulouse, France
(2)
Laboratoire de Pharmacologie et Toxicologie, UR 66, Institut National de la Recherche Agronomique (INRA), Toulouse, France

References

  1. Park T, Yi S-G, Lee S, et al.: Statistical tests for identifying differentially expressed genes in time-course microarray experiments. Bioinformatics 2003, 19(6):694-703. 10.1093/bioinformatics/btg068View ArticleGoogle Scholar
  2. Peddada SD, Lobenhofer EK, Li L, Afshari CA, Weinberg CR, Umbach DM: Gene selection and clustering for time-course and dose-response microarray experiments using order-restricted inference. Bioinformatics 2003, 19(7):834-841. 10.1093/bioinformatics/btg093View ArticleGoogle Scholar
  3. Storey JD, Xiao W, Leek JT, Tompkins RG, Davis RW: Significance analysis of time course microarray experiments. Proceedings of the National Academy of Sciences of the United States of America 2005, 102(36):12837-12842. 10.1073/pnas.0504609102View ArticleGoogle Scholar
  4. Tai YC, Speed TP: A multivariate empirical Bayes statistic for replicated microarray time course data. The Annals of Statistics 2006, 34(5):2387-2412. 10.1214/009053606000000759View ArticleMathSciNetMATHGoogle Scholar
  5. Ramoni MF, Sebastiani P, Kohane IS: Cluster analysis of gene expression dynamics. Proceedings of the National Academy of Sciences of the United States of America 2002, 99(14):9121-9126. 10.1073/pnas.132656399View ArticleMathSciNetMATHGoogle Scholar
  6. Ernst J, Nau GJ, Bar-Joseph Z: Clustering short time series gene expression data. Bioinformatics 2005, 21(1):i159-i168. 10.1093/bioinformatics/bti1022View ArticleGoogle Scholar
  7. Giurcǎneanu CD, Tǎbuş I, Astola J: Clustering time series gene expression data based on sum-of-exponentials fitting. EURASIP Journal on Applied Signal Processing 2005, 2005(8):1159-1173. 10.1155/ASP.2005.1159View ArticleGoogle Scholar
  8. Heard NA, Holmes CC, Stephens DA, Hand DJ, Dimopoulos G: Bayesian coclustering of Anopheles gene expression time series: study of immune defense response to multiple experimental challenges. Proceedings of the National Academy of Sciences of the United States of America 2005, 102(47):16939-16944. 10.1073/pnas.0408393102View ArticleGoogle Scholar
  9. Conesa A, Nueda MJ, Ferrer A, Talón M: maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments. Bioinformatics 2006, 22(9):1096-1102. 10.1093/bioinformatics/btl056View ArticleGoogle Scholar
  10. Letowski J, Brousseau R, Masson L: Designing better probes: effect of probe size, mismatch position and number on hybridization in DNA oligonucleotide microarrays. Journal of Microbiological Methods 2004, 57(2):269-278. 10.1016/j.mimet.2004.02.002View ArticleGoogle Scholar
  11. Ramsay J, Silverman B: Functional Data Analysis. 2nd edition. Springer, New York, NY, USA; 2005.Google Scholar
  12. Bar-Joseph Z, Gerber GK, Gifford DK, Jaakkola TS, Simon I: Continuous representations of time-series gene expression data. Journal of Computational Biology 2003, 10(3-4):341-356. 10.1089/10665270360688057View ArticleGoogle Scholar
  13. Bar-Joseph Z: Analyzing time series gene expression data. Bioinformatics 2004, 20(16):2493-2503. 10.1093/bioinformatics/bth283View ArticleGoogle Scholar
  14. Martin PGP, Lasserre F, Calleja C, et al.:Transcriptional modulations by RXR agonists are only partially subordinated to PPAR signaling and attest additional, organ-specific, molecular cross-talks. Gene Expression 2005, 12(3):177-192. 10.3727/000000005783992098View ArticleGoogle Scholar
  15. Martin PGP, Guillou H, Lasserre F, et al.:Novel aspects of PPAR -mediated regulation of lipid and xenobiotic metabolism revealed through a nutrigenomic study. Hepatology 2007, 45(3):767-777. 10.1002/hep.21510View ArticleGoogle Scholar
  16. INRArray: Laboratoire de Pharmacologie et Toxicologie, INRA.2005. [http://www.inra.fr/internet/Centres/toulouse/pharmacologie/lpt.htm]Google Scholar
  17. Silverman B: Some aspects of the spline smoothing approach to non-parametric regression curve fitting. Journal of the Royal Statistical Society: Series B 1985, 47(1):1-52.MathSciNetMATHGoogle Scholar
  18. Besse P, Cardot H, Ferraty F: Simultaneous non-parametric regressions of unbalanced longitudinal data. Computational Statistics & Data Analysis 1997, 24(3):255-270. 10.1016/S0167-9473(96)00067-9View ArticleMathSciNetMATHGoogle Scholar
  19. Seber GAF: Multivariate Observations. John Wiley & Sons, New York, NY, USA; 1984.View ArticleMATHGoogle Scholar
  20. Yeung KY, Ruzzo WL: Principal component analysis for clustering gene expression data. Bioinformatics 2001, 17(9):763-774. 10.1093/bioinformatics/17.9.763View ArticleGoogle Scholar
  21. Chipman H, Hastie TJ, Tibshirani T: Clustering microarray data. In Statistical Analysis of Gene Expression Microarray Data. Edited by: Speed T. Chapmann & Hall/CRC Press, Boca Raton, Fla, USA; 2003:159-200.Google Scholar
  22. Kersten S, Seydoux J, Peters JM, Gonzalez FJ, Desvergne B, Wahli W:Peroxisome proliferator-activated receptor mediates the adaptive response to fasting. Journal of Clinical Investigation 1999, 103(11):1489-1498. 10.1172/JCI6223View ArticleGoogle Scholar
  23. Mandard S, Müller M, Kersten S:Peroxisome proliferator-activated receptor target genes. Cellular and Molecular Life Sciences 2004, 61(4):393-416. 10.1007/s00018-003-3216-3View ArticleGoogle Scholar
  24. Bauer M, Hamm AC, Bonaus M, et al.: Starvation response in mouse liver shows strong correlation with life-span-prolonging processes. Physiological Genomics 2004, 17(2):230-244. 10.1152/physiolgenomics.00203.2003View ArticleGoogle Scholar

Copyright

© 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.

Advertisement