Finding Significant Communities in Cross-Correlation Networks Derived from Multi-View Data

• Activity Number: 73 - SPEED: Statistical Computing and Statistics in Genomics Part 2
• Type: Contributed
• Date/Time: Sunday, August 7, 2022 : 5:05 PM to 5:50 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Miheer Ulhas Dewaskar*
• Companies: Duke University
• Keywords: Multi-view data ; eQTL analysis; Correlation networks; Community Detection; Multiple testing ; Exploratory analysis

Abstract

Multi-view data, obtained by taking multiple types of measurements of the same samples, is now common in many scientific disciplines like genomics, ecology, and climate science. An important exploratory problem in the analysis of such data is to identify interactions between features from the different measurement types. Assuming linear association, these interactions can be captured by a bipartite cross-correlation network, whose nodes are features from two distinct measurement types and edges represent feature pairs that are truly correlated. We will introduce the Bimodule Search Procedure (BSP), which performs iterative hypothesis-tests on the data to find communities of the bipartite cross-correlation network. BSP works directly with the data rather than only the sample correlation matrices, and it is thus able to borrow strength and account for correlations within features of the same measurement type. We show an application to eQTL-analysis: BSP is applied to a single-tissue data from the GTEx consortium to find numerous Gene-SNP interaction subnetworks.