Finding groups of cross-correlated features in bi-view data

Data sets in which measurements of two (or more) types are obtained from a common set of samples arise in many scientific applications. A common problem in the exploratory analysis of such data is to identify groups of features of different data types that are strongly associated. A bimodule is a pair (A, B) of feature sets from two data types such that the aggregate cross-correlation between the features in A and those in B is large. A bimodule (A, B) is stable if A coincides with the set of features that have significant aggregate correlation with the features in B, and vice-versa. In this paper we propose and investigate an iterative testing-based procedure (BSP) to identify stable bimodules in bi-view data. We carry out a thorough simulation study to assess the performance of BSP, and present an extended application to the problem of expression quantitative trait loci (eQTL) analysis using recent data from the GTEx project. In addition, we apply BSP to climatology data to identify regions in North America where annual temperature variation affects precipitation.