Abstract Details
Activity Number:
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297
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 11, 2015 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract #316988
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Title:
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Bayesian Clustering of Multi-Source Data
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Author(s):
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Eric Lock*
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Companies:
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University of Minnesota
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Keywords:
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Bayesian ;
Clustering ;
Multi-source ;
Data integration ;
Dirichlet mixtures
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Abstract:
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In biomedical research a growing number of platforms and technologies are used to measure diverse but related information. The task of clustering a set of objects based on multiple sources of data arises in several applications. Most current approaches to multi-source clustering either independently determine a clustering for each data source, or determine a single joint clustering based on all data sources. We propose an integrative statistical model that permits a different clustering for each data source. These source-specific clusterings adhere loosely to an overall consensus clustering, and hence they are not independent. We describe a computationally scalable Bayesian framework for simultaneous estimation of both the consensus clustering and the source-specific clusterings. We demonstrate that this flexible approach is more robust than joint clustering of all data sources, and is more powerful than clustering each data source independently. Furthermore, We present an application to subtype identification of breast cancer tumor samples using publicly available data from The Cancer Genome Atlas.
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Authors who are presenting talks have a * after their name.
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