Information Bottleneck for Pathway-Centric Gene Expression Analysis
While DNA microarrays enable us to conveniently measure expression profiles in the scope of thousands of genes, the subsequent association studies typically suffer from a tremendous imbalance between number of variables (genes) and observations (subjects). Even more so, each gene is heavily perturbed by noise which prevents any meaningful analysis on the single-gene level . Hence, the focus shifted to pathways as groups of functionally related genes , in the hope that aggregation potentiates the underlying signal. Technically, this leads to a problem of feature extraction which was previously tackled by principal component analysis . We reformulate the task using an extension of the Meta-Gaussian Information Bottleneck method as a means to compress a gene set while preserving information about a relevance variable. This opens up new possibilities, enabling us to make use of clinical side information in order to uncover hidden characteristics in the data.
KeywordsCopula Model Gaussian Copula Irrelevance Variable Gaussian Random Vector Univariate Margin
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