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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.

In order to discover potential markers of prognosis in colorectal cancer (CRC) we have determined gene expression profiles, using cDNA microarrays in CRC samples obtained from 19 patients in Dukes stages C and D, with favorable clinical course (Dukes C patients, survival >5 years after surgery, group A, n=7) or unfavorable clinical course (Dukes stage C and D patients, survival <5 years after surgery, group B, n=12). Gene expression was measured in RNA from each tumor, using a pool of equal amounts of RNA from all tumors as a reference. To identify and rank differentially expressed genes we used three different analytical methods: (i) Significance Analysis of Microarrays (SAM), (ii) Cox's Proportional Hazard Model, and (iii) Trend Filter (a mathematical method for the assessment of numerical trends). The level of expression of a gene in an individual tumor was regarded as of interest when that gene was identified as differentially expressed by at least two of these three methods. By these stringent criteria we identified eight genes (ITGB2, MRPS11, NPR1, TXNL2, PHF10, PRSS8, KCNK3, JAK3) that were correlated with prolonged survival after surgery. Pathway analysis showed that patients with favorable prognosis had several activated metabolic pathways (carbon metabolism, transcription, amino acid and nitrogen metabolism, signaling and fibroblast growth factor receptor pathways). To further validate individual gene expression findings, the RNA level of each gene identified as a marker with microarrays was measured by real-time RT-PCR in CRC samples from an independent group of 55 patients. In this set of patients the Cox Proportional Hazard Model analysis demonstrated a significant association between increased patient survival and low expression of ITGB2 (p = 0.011) and NPR1 (p = 0.023) genes.

Motivation: Microarray studies permit to quantify expression levels on a global scale by measuring transcript abundance of thousands of genes simultaneously. A difficulty when analysing expression measures is how to model variability for the whole set of genes. It is usually unrealistic to assume a common variance for each gene. Several approaches to model gene-specific variances are proposed. We take advantage of calibration experiments, in which the probes hybridized on the two channels come from the same population (self-self experiment). In this case it is possible to estimate the gene-specific variance, to be incorporated in comparative experiments on the same tissue, cellular line or species., Results: We present two approaches to introduce prior information on gene-specific variability from a calibration experiment: an empirical Bayes model and a full Bayesian hierarchical model. We apply the methods in the analysis of human lipopolysaccharide-stimulated leukocyte experiments., Availability: The calculations are implemented in WinBugs. The codes are available on request from the authors.