GSMA: Gene Set Matrix Analysis, An Automated Method for Rapid Hypothesis Testing of Gene Expression Data

Background: Microarray technology has become highly valuable for identifying complex global changes in gene expression patterns. The assignment of functional information to these complex patterns remains a challenging task in effectively interpreting data and correlating results from across experiments, projects and laboratories. Methods which allow the rapid and robust evaluation of multiple functional hypotheses increase the power of individual researchers to data mine gene expression data more effi ciently. Results: We have developed (gene set matrix analysis) GSMA as a useful method for the rapid testing of group-wise up- or downregulation of gene expression simultaneously for multiple lists of genes (gene sets) against entire distributions of gene expression changes (datasets) for single or multiple experiments. The utility of GSMA lies in its fl exibility to rapidly poll gene sets related by known biological function or as designated solely by the end-user against large numbers of datasets simultaneously. Conclusions: GSMA provides a simple and straightforward method for hypothesis testing in which genes are tested by groups across multiple datasets for patterns of expression enrichment. Background functional meaning to patterns of statistically signifi cant changes in gene expression is a common goal in the interpretation of microarray data. Until recently most conventional approaches have restricted their focus to only those genes which have satisfi ed multiple different criteria including size of fold change, signifi cant p-value (often accompanied by additional requirements related to passing tests correcting for multiple comparisons), and certain minimum baseline levels of expression on at least one side of the comparison. This approach was reasonable during the early developmental period of microarrays when uncertainty as to the reliability of gene expression measurements naturally led to a conservative bias in the interpretation of microarray data in an effort to reduce, as much as possible, the inclusion of artifactual noise in analyses. Unfortunately, the tradeoff in reducing Type 1 error (false positives) was almost certainly at the expense of increasing Type II error (false negatives) but since these were essentially unknown, the problem tended to be ignored at that time. The issue has become more acute as technical improvements in microarray technology and the extent and depth of microarray studies have expanded at accelerated rates. The loss of vital information because of restrictive signifi cance levels is less tolerable and, as others have argued, can result in the failure to defi ne small but coordinated changes in gene expression which clearly, in the aggregate, distinguish biological phenotypes [1]. Traditional methods of assigning function to gene lists have focused primarily in looking for enrichment within a group of genes on the basis of some functional category, for example, for gene ontologies (GenMapp, David/Ease) or pathways (KEGG, BioCarta). These methods use some simple statistic (e.g. Fisher’s exact test) to generate an estimate of probability that the genes are enriched relative to all genes for that category and corrected for the frequency of representation for the genes of that category on the microarray platform being used. These methods are vulnerable to small changes in the