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Activity Number:
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306
- SPEED: SPAAC SESSION II
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Type:
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Topic-Contributed
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Date/Time:
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Wednesday, August 11, 2021 : 3:30 PM to 5:20 PM
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Sponsor:
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Section on Statistics in Genomics and Genetics
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Abstract #318320
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Title:
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Network-Based Differential Co-Expression Analysis Using Breast Cancer Data Set
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Author(s):
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Yonghui Ni* and Prabhakar Chalise and Jianghua He
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Companies:
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University of Kansas-Medical Center,Department of Biostatistics & Data Science and University of Kansas Medical Center and University of Kansas Medical Center
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Keywords:
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differential co-expression;
differential network
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Abstract:
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Identification of genes associated with the biological mechanism of disease is very important. Traditional differential expression analyses methods consider one gene at a time independently. Network based differential co-expression analysis has emerged as powerful approach to reveal dependency structure of genes by identifying the coordinated and co-regulated expression between biological states of disease. Such methods aim to identify a differential co-expression network that characterizes association change across conditions. One such strategy is by exploring the network global/local topological changes to identify differentially connected genes that may indicate the differences in the underlying cellular activity. In this study, we implement and compare four network-based methods (DiffK, INDEED, DINGO, DEDN) using gene expression data from breast cancer study . Genes found in KEGG breast cancer pathways were used in the analysis. The identified genes were further studied to explore their association with survival of the subjects using cox regression analysis. Our study shows that network-based methods are better able to identify important disease associated genes.
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Authors who are presenting talks have a * after their name.
