Finding Groups of Cross-Correlated Features in Bi-View Data
This addresses the need for exploratory analysis in scientific applications with multi-type data, such as genomics, by improving detection of stable feature associations, though it appears incremental compared to existing methods.
The paper tackles the problem of identifying groups of cross-correlated features in bi-view data by proposing a bimodule search procedure (BSP), which was best at recovering true bimodules with sufficient signal while limiting false discoveries. It applied BSP to eQTL analysis, identifying thousands of SNP-gene bimodules that revealed biologically meaningful genomic subnetworks.
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.