  Datasets 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. This paper proposes an
iterative-testing based bimodule search procedure (BSP) to identify stable
bimodules.
  Compared to existing methods for detecting cross-correlated features, BSP was
the best at recovering true bimodules with sufficient signal, while limiting
the false discoveries. In addition, we applied BSP to the problem of expression
quantitative trait loci (eQTL) analysis using data from the GTEx consortium.
BSP identified several thousand SNP-gene bimodules. While many of the
individual SNP-gene pairs appearing in the discovered bimodules were identified
by standard eQTL methods, the discovered bimodules revealed genomic subnetworks
that appeared to be biologically meaningful and worthy of further scientific
investigation.
