POE: Statistical Methods for Qualitative Analysis of Gene Expression. In many gene expression studies, the goals include discovery of novel biological classes and identification of genes whose expression can reliably be associated with these classes. Here we present a statistical analysis approach to facilitate both of these goals. The key idea is to model gene expression using latent categories that can be interpreted as a gene being turned “on“ or “off“ compared to a baseline level of expression. This three-way categorization is used for defining a reference in the unsupervised setting, for removing noise prior to clustering, for defining molecular subclasses in a way that is portable across platforms, and for defining easily interpretable probability-based distance measures for visualization, mining, and clustering.

References in zbMATH (referenced in 2 articles )

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  1. De Bin, Riccardo; Risso, Davide: A novel approach to the clustering of microarray data via nonparametric density estimation (2011) ioport
  2. Parmigiani, Giovanni (ed.); Garrett, Elizabeth S. (ed.); Irizarry, Rafael A. (ed.); Zeger, Scott L. (ed.): The analysis of gene expression data. Methods and software (2003)