What is significant? The Global Mean Rank Test may bring the answer

Comparison of the Global Mean Rank test with the established tests SAM and LIMMA. The Global Mean Rank test has consistently a low False Discovery Rate (FDR) and a high True Positive Rate (TPR). The performance of the Mean Rank Test is high and relatively robust against different types of data processing.

In the public perception, statistics is at best boring if not dubious. However, many modern scientific methods generate huge amounts of data. To find important information in these data, new statistical methods are needed. For instance, such methods can tell us which genes or proteins are important for a certain disease amongst the many thousands that form just background noise? The Global Mean Rank Test (“MeanRank” for short) is a statistical test that has been developed for such purposes. It is based on a very simple idea: If an element (e.g. a protein, a gene, etc.) turns up very prominently in several comparable experiments which study a certain phenomenon (e.g. a disease), it is probably “significant” for this phenomenon. The authors have implemented this idea in an efficient way, and they could show that MeanRank performs well with real world problems.

PLOS ONE: Identification of Significant Features by the Global Mean Rank Test.

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