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 [6]. Hence, the focus shifted to pathways as groups of functionally related genes [4], 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 [5]. 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.