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Activity Number:
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56
- Bayesian Analysis of Functional and Structured Data
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Type:
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Contributed
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Date/Time:
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Monday, August 3, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #312962
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Title:
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Identification of Differentially Methylated Regions with Bayesian Functional Data Analysis
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Author(s):
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Duchwan Ryu* and Sanjib Basu and Shrabanti Chowdhury and Suvo Chatterjee
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Companies:
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Northern Illinois University and University of Illinois At Chicago and Icahn school of Medicine at Mount Sinai and National Institute of Child Health and Development (NICHD)/ National Institutes of Health
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Keywords:
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Bayesian Functional Data Analysis;
Bayesian Smoothing Splines;
DNA Methylation;
Dynamically Weighted Particle Filter
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Abstract:
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Bayesian functional data analysis provides flexible statistical inferences even for a large volume of data, considerable measurement errors and missing observations. Regarding a very long sequence of data as segments with manageable size of sequence, the neighboring segments can be dependent and demanding computation is indispensable. We model the dependency of neighboring segments with a transition model, utilize the dynamically weighted particle filter to estimate the methylation patterns at each segment, and apply Bayes factor to examine the group effect. The proposed methodology is examined through simulation studies and is applied it to identify differentially methylated regions in DNA from lung adenocarcinoma patients.
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Authors who are presenting talks have a * after their name.
