This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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603
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Type:
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Invited
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Date/Time:
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Thursday, August 5, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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WNAR
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Abstract - #306089 |
Title:
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Integrative Clustering of Multiple Genomic Data Types Using a Regularized Joint Latent Variable Model
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Author(s):
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Ronglai Shen*+ and Adam B. Olshen and Sijian Wang
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Companies:
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Memorial Sloan-Kettering Cancer Center and University of California, San Francisco and University of Wisconsin-Madison
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Address:
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307 East 63rd Street, Third Floor, New York, NY, 10065,
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Keywords:
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Integrative Clustering ;
Multiple genomic data types ;
Joint latent variable model ;
penalized likelihood approach ;
EM algorithm
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Abstract:
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The NCI/NHGRI initiated Cancer Genome Atlas (TCGA) project is a coordinated effort to catalogue the entire spectrum of genomic, epigenomic and transcriptomic alterations in the cancer genome. The TCGA network is generating unprecedented multidimensional data using the latest array and sequencing technologies. We propose a joint latent variable model for integrating different "omic" data types in the same sets of tumors for the purpose of subtype analysis. The main concept is to formulate tumor subtypes as a set of latent variables underpinning the common source of variation across data types; and schematically, inducing a simultaneous dimension reduction for multiple high-dimensional correlated data sets. An independent error term is added to account for the within-data type variance-covariance structures. A regularized optimization scheme is carried out in the EM framework.
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