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Abstract Details
Activity Number:
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72
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Type:
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Contributed
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Date/Time:
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Sunday, July 31, 2011 : 4:00 PM to 5:50 PM
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Sponsor:
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Biometrics Section
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Abstract - #303378 |
Title:
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Mixture Regression Analysis of *-Seq Data
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Author(s):
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Naim Rashid*+ and Wei Sun and Joseph G. Ibrahim
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Companies:
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The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill and The University of North Carolina
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Address:
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Biostatistics, Chapel Hill, NC, 27517, US
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Keywords:
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Mixture Regression ;
Discrete Time Series ;
ChIP-seq ;
Next Generation Sequencing ;
Negative Binomial ;
Penalized Regression
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Abstract:
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Next generation sequencing technologies are now routinely utilized for genome-wide investigation of various genomic activities, from the study of histone modifications to the detection of transcription factor binding sites. We refer to these collectively as *-seq experiments, which include Chromatin Immunoprecipitation (ChIP-seq), DNase hypersensitive site mapping (DNase-seq), and Formaldehyde-Assisted Isolation of Regulatory Elements (FAIRE-seq), among others. A common goal of the analysis of *-seq data is to identify genomic loci with enriched sequence reads. We discuss a general mixture regression approach for the analysis of *-seq data, where arbitrary sets of covariates are introduced to augment the identification of enrichment loci. We also introduce a new penalization procedure to efficiently identify covariates within each mixture component, and discuss approaches to deal with locally correlated responses in the mixture regression setting.
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Authors who are presenting talks have a * after their name.
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