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Data sets in which measurements of two (or more) types are obtained from acommon set of samples arise in many scientific applications. A common problemin the exploratory analysis of such data is to identify groups of features ofdifferent 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-correlationbetween the features in A and those in B is large. A bimodule (A, B) is stableif A coincides with the set of features that have significant aggregatecorrelation with the features in B, and vice-versa. In this paper we proposeand investigate an iterative testing-based procedure (BSP) to identify stablebimodules in bi-view data. We carry out a thorough simulation study to assessthe performance of BSP, and present an extended application to the problem ofexpression quantitative trait loci (eQTL) analysis using recent data from theGTEx project. In addition, we apply BSP to climatology data to identify regionsin North America where annual temperature variation affects precipitation.

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