GroupTest: Multiple Testing Procedure for Grouped Hypotheses

In the modern Big Data analysis, testing multiple hypotheses simultaneously has been an important tool in analyzing data arising from scientific studies, such as genetics, astronomy, social sciences and many others. The hypotheses can often be grouped together according to the nature of the scientific investigations. For instance, genes can be grouped according to gene pathways; the nearby pixels in an image can be grouped together. The sparsity appears in both betweenand within-group levels. Namely, only a small number of groups are significant and a small number of hypotheses within a significant group are significant. To fully incorporate the group information, Liu, Sarkar, and Zhao (2015) proposed the BSG model, described in Section 2. They have further provided a methodology for testing these grouped hypothesis, controlling the total posterior false discovery rate and within-group false discover rate. The GroupTest package provides the implementation of all the procedures under the R programming environment. The usage of GroupTest is illustrated in this paper using a simulated data set and a real data from Liu et al. (2015).