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Activity Number:
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244
- Statistical methods for microbiome data analysis and beyond
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Type:
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Contributed
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Date/Time:
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Wednesday, August 11, 2021 : 10:00 AM to 11:50 AM
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Sponsor:
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Section on Statistics in Genomics and Genetics
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Abstract #317764
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Title:
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Fast Search Algorithms for Identifying Dynamic Gene Coexpression via Bayesian Variable Selection
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Author(s):
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Wenda Zhang* and Lianming Wang and Yen-Yi Ho and Daping Fan
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Companies:
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University of South Carolina and University of South Carolina and University of South Carolina and University of South Carolina School of Medicine
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Keywords:
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Bayesian variable selection;
Coexpression biomarker;
Dynamic gene coexpression;
High dimensional search space;
Liquid association;
Spike-and-slab prior
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Abstract:
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A wealth of gene expression data generated by high-throughput techniques provides exciting opportunities for studying genetic interactions systemically. Genetic interactions in a biological system are tightly regulated and are often highly dynamic. The interactions can change versatilely under various internal cellular signals or external stimuli. Previous studies have developed statistical methods to examine these dynamic changes in genetic interactions. However, a common challenge encountered in the existing approaches is the computational intensiveness due to the massive amount of gene combinations in a typical genomic dataset. To solve this problem, we propose fast algorithms via Bayesian variable selection framework with spike-and-slab priors in a sparse setting. The proposed algorithms can restrict the search space to subsets of promising gene combinations to accelerate the search process. Simulation studies are conducted to evaluate and compare our proposed approaches to existing exhaustive search heuristics. We also performed experimental data analysis to study gene coexpression changes associated with colorectal cancer recurrence-free survival.
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Authors who are presenting talks have a * after their name.
