As gene annotation databases continue to evolve and improve, it has become 
feasible to incorporate the functional and pathway information about genes, 
available in these databases into the analysis of gene expression data, for a 
better understanding of the underlying mechanisms. A few methods have been 
proposed in the literature to formally convert individual gene results into gene 
function results. In this paper, we will compare the various methods, propose 
and examine some new ones, and offer a structured approach to incorporating gene 
function or pathway information into the analysis of expression data. We study 
the performance of the various methods and also compare them on real data, using 
a case study from the toxicogenomics area. Our results show that the approaches 
based on gene function scores yield a different, and functionally more 
interpretable, array of genes than methods that rely solely on individual gene 
scores. They also suggest that functional class scoring methods appear to 
perform better and more consistently than overrepresentation analysis and 
distributional score methods.