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Activity Number: 527 - Contributed Poster Presentations: Section on Statistics in Genomics and Genetics
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #307054
Title: Integrating Gene Regulatory Pathways into Differential Network Analysis of Gene Expression Data
Author(s): Tyler Grimes* and Somnath Datta
Companies: and University of Florida
Keywords: RNA-seq; Reactome; graphical models; co-expression; permutation testing

The advent of next-generation sequencing has introduced new opportunities in analyzing gene expression data. The analysis of gene-gene association networks provides insight into gene regulatory networks. Comparing networks from different populations can reveal differences the connections between genes. Pathologies often arise from aberrations in these gene-gene interactions. Exposing these network irregularities provides a new avenue for understanding and treating diseases. A general framework for integrating known gene regulatory pathways into a differential network analysis is proposed. The framework allows for any gene-gene association measure to be used, and inference is carried out through permutation testing. A simulation study investigates the performance in identifying differential connected genes when incorporating known pathways and compares the general framework to four state-of-the-art methods. Two RNA-seq dataset are analyzed to illustrate the use of this framework in practice. In both examples, the analysis reveals genes and pathways that are known to be biologically significant along with potentially novel findings that may be used to motivate future research.

Authors who are presenting talks have a * after their name.

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