Background: Gene-set analysis evaluates the expression of biological pathways, or a priori defined gene sets, rather than that of individual genes, in association with a binary phenotype, and is of great biologic interest in many DNA microarray studies. Gene Set Enrichment Analysis (GSEA) has been applied widely as a tool for gene-set analyses. We describe here some critical problems with GSEA and propose an alternative method by extending the individual-gene analysis method, Significance Analysis of Microarray (SAM), to gene-set analyses (SAM-GS).

Background: Canadian funding agencies are no longer content to support research that solely advances scientific knowledge, and key directives are now in place to promote research transfer to policy-and decision-makers. Therefore, it is necessary to improve our understanding of how researchers are trained and supported to facilitate knowledge translation activities. In this study, we investigated differences in health researcher characteristics and knowledge translation activities.

Gene-set analysis aims to identify differentially expressed gene sets (pathways) by a phenotype in DNA microarray studies. We review here important methodological aspects of gene-set analysis and illustrate them with varying performance of several methods proposed in the literature. We emphasize the importance of distinguishing between 'self-contained' versus 'competitive' methods, following Goeman and Bühlmann. We also discuss reducing a gene set to its subset, consisting of 'core members' that chiefly contribute to the statistical significance of the differential expression of the initial gene set by phenotype. Significance analysis of microarray for gene-set reduction (SAM-GSR) can be used for an analytical reduction of gene sets to their core subsets. We apply SAM-GSR on a microarray dataset for identifying biological gene sets (pathways) whose gene expressions are associated with p53 mutation in cancer cell lines. Codes to implement SAM-GSR in the statistical package R can be downloaded from http://www.ualberta.ca/~yyasui/homepage.html.

Background: Multiple data-analytic methods have been proposed for evaluating gene-expression levels in specific biological pathways, assessing differential expression associated with a binary phenotype. Following Goeman and Bühlmann's recent review, we compared statistical performance of three methods, namely Global Test, ANCOVA Global Test, and SAM-GS, that test "self-contained null hypotheses" Via. subject sampling. The three methods were compared based on a simulation experiment and analyses of three real-world microarray datasets.

Comparative analyses of safety/tolerability data from a typical phase III randomized clinical trial generate multiple p-values associated with adverse experiences (AEs) across several body systems. A common approach is to 'flag' any AE with a p-value less than or equal to 0.05, ignoring the multiplicity problem. Despite the fact that this approach can result in excessive false discoveries (false positives), many researchers avoid a multiplicity adjustment to curtail the risk of missing true safety signals. We propose a new flagging mechanism that significantly lowers the false discovery rate (FDR) without materially compromising the power for detecting true signals, relative to the common no-adjustment approach. Our simple two-step procedure is an enhancement of the Mehrotra-Heyse-Tukey approach that leverages the natural grouping of AEs by body systems. We use simulations to show that, on the basis of FDR and power, our procedure is an attractive alternative to the following: (i) the no-adjustment approach; (ii) a one-step FDR approach that ignores the grouping of AEs by body systems; and (iii) a recently proposed two-step FDR approach for much larger-scale settings such as genome-wide association studies. We use three clinical trial examples for illustration.