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Abstract Details
Activity Number:
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649
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 4, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract - #303070 |
Title:
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Joint and Individual Variation Explained (JIVE) for Integrated Analysis of Multiple Datatypes
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Author(s):
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Eric Frazer Lock*+
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Companies:
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The University of North Carolina at Chapel Hill
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Address:
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107 West Main St., Carrboro, NC, 27510,
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Keywords:
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Multi-block data ;
Data integration ;
Principal Component Analysis
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Abstract:
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Research in several fields now requires the analysis of "multi- block" data, in which multiple high-dimensional and fundamentally disparate datatypes are available for a common set of objects. In this paper we introduce Joint and Individual Variation Explained (JIVE), a general method for the integrated analysis of multi-block data. The method decomposes a multi-block dataset into a sum of three terms: a low-rank approximation capturing joint variation between datatypes, a low-rank approximation for structured variation individual to each datatype, and residual noise. This decomposition can be used to quantify the amount of joint variation between datatypes, visually explore joint and individual structure, and reduce the dimensionality of the data. The proposed method represents an extension of Principal Component Analysis (PCA) and has clear advantages over popular two-block methods such as Canonical Correlation Analysis (CCA) and Partial Least Squares (PLS). We apply JIVE to data from The Cancer Genome Atlas (TCGA), where multiple genomic technologies are available for a common set of Glioblastoma Multiforme tumor samples.
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