Using pathway signatures as means of identifying similarities among microarray experiments.

Widespread use of microarrays has generated large amounts of data, the interrogation of the public microarray repositories, identifying similarities between microarray experiments is now one of the major challenges. Approaches using defined group of genes, such as pathways and cellular networks (pathway analysis), have been proposed to improve the interpretation of microarray experiments. We propose a novel method to compare microarray experiments at the pathway level, this method consists of two steps: first, generate pathway signatures, a set of descriptors recapitulating the biologically meaningful pathways related to some clinical/biological variable of interest, second, use these signatures to interrogate microarray databases. We demonstrate that our approach provides more reliable results than with gene-based approaches. While gene-based approaches tend to suffer from bias generated by the analytical procedures employed, our pathway based method successfully groups together similar samples, independently of the experimental design. The results presented are potentially of great interest to improve the ability to query and compare experiments in public repositories of microarray data. As a matter of fact, this method can be used to retrieve data from public microarray databases and perform comparisons at the pathway level.

Eu.Gene Analyzer a tool for integrating gene expression data with pathway databases.

MOTIVATION: Eu.Gene Analyzer is an easy-to-use, stand-alone application that allows rapid and powerful microarray data analysis in the context of biological pathways. Its intuitive graphical user interface makes it an easy and flexible tool, even for the first-time user. Eu.Gene supports a variety of array platforms, organisms and pathway ontologies, transparently deals with multiple nomenclature systems and seamlessly integrates data from different sources. Two different statistical methods, the Fisher Exact Test and the Gene Set Enrichment Analysis (GSEA), are implemented to identify biological pathways transcriptionally affected under experimental conditions. A suite of tools is offered to define, visualize and share custom non-redundant pathway sets. In conclusion, Eu.Gene Analyzer is a new software application that takes advantage of information from multiple pathway databases to build a comprehensive interpretation of experimental results in a simple, intuitive environment.