The latent process decomposition of cDNA microarray data sets

Article English OPEN
Rogers, S. ; Girolami, M. ; Campbell, C. ; Breitling, R. (2005)
  • Publisher: Institute of Electrical and Electronics Engineers
  • Related identifiers: doi: 10.1109/TCBB.2005.29
  • Subject: QA76
    acm: ComputingMethodologies_PATTERNRECOGNITION

We present a new computational technique (a software implementation, data sets, and supplementary information are available at which enables the probabilistic analysis of cDNA microarray data and we demonstrate its effectiveness in identifying features of biomedical importance. A hierarchical Bayesian model, called latent process decomposition (LPD), is introduced in which each sample in the data set is represented as a combinatorial mixture over a finite set of latent processes, which are expected to correspond to biological processes. Parameters in the model are estimated using efficient variational methods. This type of probabilistic model is most appropriate for the interpretation of measurement data generated by cDNA microarray technology. For determining informative substructure in such data sets, the proposed model has several important advantages over the standard use of dendrograms. First, the ability to objectively assess the optimal number of sample clusters. Second, the ability to represent samples and gene expression levels using a common set of latent variables (dendrograms cluster samples and gene expression values separately which amounts to two distinct reduced space representations). Third, in contrast to standard cluster models, observations are not assigned to a single cluster and, thus, for example, gene expression levels are modeled via combinations of the latent processes identified by the algorithm. We show this new method compares favorably with alternative cluster analysis methods. To illustrate its potential, we apply the proposed technique to several microarray data sets for cancer. For these data sets it successfully decomposes the data into known subtypes and indicates possible further taxonomic subdivision in addition to highlighting, in a wholly unsupervised manner, the importance of certain genes which are known to be medically significant. To illustrate its wider applicability, we also illustrate its performance on a microarray data set for yeast.
  • References (12)
    12 references, page 1 of 2

    [1] A.A. Alizadeh et al., “Distinct Types of Diffuse Large B-Cell Lymphoma Identified by Gene Expression Profiling,” Nature, vol. 403, no. 3, pp. 503-511, Feb. 2000.

    [2] D.M. Blei, A.Y. Ng, and M.I. Jordan, “Latent Dirichlet Allocation,” J. Machine Learning Research, vol. 3, pp. 993-1022, 2003.

    [3] C.C. Chang et al., “Connective Tissue Growth Factor and Its Role in Lung Adenocarcinoma Invasion and Metastasis,” J. Nat'l Cancer Inst., vol. 96, pp. 344-345, 2004.

    [4] S.M. Dhanasekaran et al., “Delineation of Prognostic Biomarkers in Prostate Cancer,” Nature, vol. 412, pp. 822-826, 2001.

    [5] R.G. Fahmy et al., “Transcription Factor EGR-1 Supports FGFDependent Angiogenesis during Neovascularization and Tumor Growth,” Nature Medicine, vol. 9, pp. 1026-1032, 2003.

    [6] E. Garber et al., “Diversity of Gene Expression in Adenocarcinoma of the Lung,” Proc. Nat'l Academy of Sciences of the USA, vol. 98, no. 24, pp. 12784-12789, 2001.

    [7] A.P. Gasch et al., “Genomic Expression Program in the Response of Yeast Cells to Environmental Changes,” Molecular Biology of the Cell, vol. 11, pp. 4241-4257, 2000.

    [8] L.C. Lazzeroni and A. Owen, “Plaid Models for Expression Data,” Statistica Sinica, vol. 12, pp. 61-86, 2002.

    [9] G.J. McLachlan, R.W. Bean, and D. Peel, “A Mixture Model-Based Approach to the Clustering of Microarray Expression Data,” Bioinformatics, vol. 18, no. 3, pp. 413-422, 2002.

    [10] E. Segal, A. Battle, and D. Koller, “Decomposing Gene Expression into Cellular Processes,” Proc. Eighth Pacific Symp. Biocomputing (PSB), pp. 89-100, 2003.

  • Metrics
    No metrics available
Share - Bookmark