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Bayesian Unsupervised Learning with Multiple Data Types

Author

Listed:
  • Agius Phaedra

    (MSKCC)

  • Ying Yiming

    (University of Bristol)

  • Campbell Colin

    (University of Bristol)

Abstract

We propose Bayesian generative models for unsupervised learning with two types of data and an assumed dependency of one type of data on the other. We consider two algorithmic approaches, based on a correspondence model, where latent variables are shared across datasets. These models indicate the appropriate number of clusters in addition to indicating relevant features in both types of data. We evaluate the model on artificially created data. We then apply the method to a breast cancer dataset consisting of gene expression and microRNA array data derived from the same patients. We assume partial dependence of gene expression on microRNA expression in this study. The method ranks genes within subtypes which have statistically significant abnormal expression and ranks associated abnormally expressing microRNA. We report a genetic signature for the basal-like subtype of breast cancer found across a number of previous gene expression array studies. Using the two algorithmic approaches we find that this signature also arises from clustering on the microRNA expression data and appears derivative from this data.

Suggested Citation

  • Agius Phaedra & Ying Yiming & Campbell Colin, 2009. "Bayesian Unsupervised Learning with Multiple Data Types," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-27, June.
    • Handle: RePEc:bpj:sagmbi:v:8:y:2009:i:1:n:27
    DOI: 10.2202/1544-6115.1441
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    References listed on IDEAS

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    1. Sohail F. Tavazoie & Claudio Alarcón & Thordur Oskarsson & David Padua & Qiongqing Wang & Paula D. Bos & William L. Gerald & Joan Massagué, 2008. "Endogenous human microRNAs that suppress breast cancer metastasis," Nature, Nature, vol. 451(7175), pages 147-152, January.
    2. Dobra, Adrian & Hans, Chris & Jones, Beatrix & Nevins, J.R.Joseph R. & Yao, Guang & West, Mike, 2004. "Sparse graphical models for exploring gene expression data," Journal of Multivariate Analysis, Elsevier, vol. 90(1), pages 196-212, July.
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