BACKGROUND: Sets of genes that are known to be associated with each other can be 
used to interpret microarray data. This gene set approach to microarray data 
analysis can illustrate patterns of gene expression which may be more 
informative than analyzing the expression of individual genes. Various 
statistical approaches exist for the analysis of gene sets. There are three main 
classes of these methods: over-representation analysis, functional class 
scoring, and pathway topology based methods.
METHODS: We propose weighted hypergeometric and weighted chi-squared methods in 
order to assign a rank to the degree to which each gene participates in the 
enrichment. Each gene is assigned a weight determined by the absolute value of 
its log fold change, which is then raised to a certain power. The power value 
can be adjusted as needed. Datasets from the Gene Expression Omnibus are used to 
test the method. The significantly enriched pathways are validated through 
searching the literature in order to determine their relevance to the dataset.
RESULTS: Although these methods detect fewer significantly enriched pathways, 
they can potentially produce more relevant results. Furthermore, we compare the 
results of different enrichment methods on a set of microarray studies all 
containing data from various rodent neuropathic pain models.
DISCUSSION: Our method is able to produce more consistent results than other 
methods when evaluated on similar datasets. It can also potentially detect 
relevant pathways that are not identified by the standard methods. However, the 
lack of biological ground truth makes validating the method difficult.