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Activity Number: 663 - Regression, Clustering and Gene Set Methods in Genomics
Type: Contributed
Date/Time: Thursday, August 1, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #301701 Presentation
Title: Covariance Thresholding to Detect Differentially Co-Expressed Gene Sets
Author(s): Hokeun Sun* and Mingyu Oh and Kipoong Kim
Companies: Pusan National University and Pusan National University and Pusan National University
Keywords: Gene set analysis; co-expressed genes; covariance estimation; hard thresholding

Analysis of genes within a biological group such as a gene regulatory network and a signaling pathway aims to identify differentially expressed or differentially co-expressed genes between two experimental conditions, which is often called gene set analysis. In the last few decades, various statistical and computational methods have been proposed to improve statistical power of gene set analysis. In recent years, much attention has been paid to differentially co-expressed genes since they can be potentially disease-related genes without significant difference in average expression levels between two conditions. In this article, we propose new statistical method to identify differentially co-expressed genes in gene set analysis. The proposed method first estimates co-expression levels of paired genes using covariance regularization by thresholding, and then significance of difference in covariance estimation between two conditions is evaluated. We demonstrated that the proposed method is more powerful than the existing main-stream methods used in gene set analysis through extensive simulation studies. Also, we applied it to gene expression data from breast cancer studies.

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

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