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Gene Set Enrichment Analysis (GSEA) and its variations aim to discovercollections of genes that show moderate but coordinated differences inexpression. However, such techniques may be ineffective if many individualgenes in a phenotype-related gene set have weak discriminative power. Apotential solution is to search for combinations of genes that are highlydifferentiating even when individual genes are not. Although such techniqueshave been developed, these approaches have not been used with GSEA to anysignificant degree because of the large number of potential gene combinationsand the heterogeneity of measures that assess the differentiation provided bygene groups of different sizes.  To integrate the search for differentiating gene combinations and GSEA, wepropose a general framework with two key components: (A) a procedure thatreduces the number of scores to be handled by GSEA to the number of genes bysummarizing the scores of the gene combinations involving a particular gene ina single score, and (B) a procedure to integrate the heterogeneous scores fromcombinations of different sizes and from different gene combination measures bymapping the scores to p-values. Experiments on four gene expression data setsdemonstrate that the integration of GSEA and gene combination search canenhance the power of traditional GSEA by discovering gene sets that includegenes with weak individual differentiation but strong joint discriminativepower. Also, gene sets discovered by the integrative framework share severalcommon biological processes and improve the consistency of the results amongthree lung cancer data sets.

Integration of Differential Gene-combination Search and Gene Set Enrichment Analysis: A General Approach