Abstract
For gene expression data we propose a hierarchical Bayesian method of analysis using latent variables, wherein we have combined normalization and classification in a single framework. The uncertainty associated with classification for each gene can also be estimated based on the posterior distributions of the latent variables applied. The proposed models are implemented using the MCMC algorithm.
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Bhattacharjee, M., Pritchard, C., Sillanpää, M.J., Arjas, E. (2004). Bayesian Characterization of Natural Variation in Gene Expression. In: Johnson, K.F., Lin, S.M. (eds) Methods of Microarray Data Analysis III. Springer, Boston, MA. https://doi.org/10.1007/0-306-48354-8_11
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DOI: https://doi.org/10.1007/0-306-48354-8_11
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4020-7582-7
Online ISBN: 978-0-306-48354-7
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