Abstract
A comprehensive software for performing meta-analysis of ranked discovery oriented datasets, such as those derived from microarrays or other high throughput technologies, and for testing between-study heterogeneity for biological variables (gene expression, microRNA, proteomic, or other high-dimensional data) is presented. The software can identify biological probes that have either very high average ranks (e.g. consistently over-expressed genes) or very low average ranks (e.g. consistently under-expressed genes). The program tests each probe's average rank and the between-study heterogeneity of the study-specific ranks. Furthermore, it performs heterogeneity analyses restricted to probes with similar average ranks. The program allows both unweighted and weighted analysis. Statistical inferences are based on Monte Carlo permutation tests.
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