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Adaptive clustering for time series: Application for identifying cell cycle expressed genes

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  • Douzal-Chouakria, Ahlame
  • Diallo, Alpha
  • Giroud, Françoise

Abstract

The biological problem of identifying the active genes during the cell division process is addressed. The cell division ensures the proliferation of cells, which is drastically aberrant in cancer cells. The studied genes are described by their expression profiles during the cell division cycle. Commonly, the identification process is a supervised approach based on an a priori set of reference genes, assumed as well-characterizing the cell cycle phases. Each studied gene is then classified by its peak similarity to one pre-specified reference gene. This classical approach suffers from two limitations. On the one hand, there is no consensus between biologists about the set of reference genes to consider for the identification process. On the other hand, the proximity measures used for genes expression profiles are unjustified and mainly based on the expression values regardless of the genes expression behavior. To identify genes expression profiles, a new adaptive clustering approach is proposed which consists of two main points. First, it allows in an unsupervised way the selection of a well-justified set of reference genes, to be compared with the pre-specified ones. Secondly, it enables the users to learn the appropriate proximity measure to use for genes expression data, a measure which will cover both proximity on values and on behavior. The adaptive clustering method is compared to a correlation-based approach through public and simulated genes expression data.

Suggested Citation

  • Douzal-Chouakria, Ahlame & Diallo, Alpha & Giroud, Françoise, 2009. "Adaptive clustering for time series: Application for identifying cell cycle expressed genes," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1414-1426, February.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:4:p:1414-1426
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    References listed on IDEAS

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    1. Park, Changyi & Koo, Ja-Yong & Kim, Sujong & Sohn, Insuk & Lee, Jae Won, 2008. "Classification of gene functions using support vector machine for time-course gene expression data," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2578-2587, January.
    2. Liu, Xueli & Lee, Sheng-Chien & Casella, George & Peter, Gary F., 2008. "Assessing agreement of clustering methods with gene expression microarray data," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5356-5366, August.
    3. Liang, Faming, 2007. "Use of SVD-based probit transformation in clustering gene expression profiles," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6355-6366, August.
    4. Caiado, Jorge & Crato, Nuno & Pena, Daniel, 2006. "A periodogram-based metric for time series classification," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2668-2684, June.
    5. Zhang, Chunming & Fu, Haoda & Jiang, Yuan & Yu, Tao, 2007. "High-dimensional pseudo-logistic regression and classification with applications to gene expression data," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 452-470, September.
    6. Scrucca, Luca, 2007. "Class prediction and gene selection for DNA microarrays using regularized sliced inverse regression," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 438-451, September.
    7. Luis Angel Garcia-Escudero & Alfonso Gordaliza, 2005. "A Proposal for Robust Curve Clustering," Journal of Classification, Springer;The Classification Society, vol. 22(2), pages 185-201, September.
    8. Serban, Nicoleta & Wasserman, Larry, 2005. "CATS: Clustering After Transformation and Smoothing," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 990-999, September.
    9. Kim, Yongdai & Kwon, Sunghoon & Heun Song, Seuck, 2006. "Multiclass sparse logistic regression for classification of multiple cancer types using gene expression data," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1643-1655, December.
    10. He, Yi & Pan, Wei & Lin, Jizhen, 2006. "Cluster analysis using multivariate normal mixture models to detect differential gene expression with microarray data," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 641-658, November.
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