Department of Biomathematics, University of Thessaly School of Medicine, Larissa, Greece.
Comput Methods Programs Biomed. 2012 Dec;108(3):1243-6. doi: 10.1016/j.cmpb.2012.08.001. Epub 2012 Sep 5.
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.
现介绍一种全面的软件，可用于对以发现为导向的排名数据集（例如源自微阵列或其他高通量技术的数据集）进行荟萃分析，并可用于测试生物变量（基因表达、microRNA、蛋白质组学或其他高维数据）之间的研究异质性。该软件可以识别具有非常高平均排名的生物探针（例如，一致过表达的基因）或具有非常低平均排名的生物探针（例如，一致低表达的基因）。该程序测试每个探针的平均排名和研究特定排名的研究间异质性。此外，它还可以对具有相似平均排名的探针进行异质性分析。该程序允许进行无权重和权重分析。统计推断基于蒙特卡罗置换检验。
