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Abstract Details
Activity Number:
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617
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Type:
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Contributed
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Date/Time:
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Thursday, August 4, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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Abstract - #301769 |
Title:
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Title: Adjusting for Multiple Testing Dependence via Supervised SVD
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Author(s):
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Yunting Sun*+ and Nancy Zhang and Art Owen
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Companies:
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Stanford University and Stanford University and Stanford University
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Address:
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, , ,
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Keywords:
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Latent Factor ;
Outlier detection ;
Sparse Penalized Regression ;
Singular Value Decomposition ;
Thresholding
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Abstract:
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Most statistical methods for performing multiple testing rely on independence or some form of weak dependence among the data corresponding to the variables being tested. However, high dimensional studies rarely involve the analysis of independent variables with independent samples because of the presence of latent factors that comes from batch effect and population stratification. A latent factor not orthogonal to the primary predictors can lead to spurious association. We propose a method to tackle this issue by exploiting the sparsity of signals and low dimensionality of latent factors. Simulation studies show that our method has better power than existing methods such as SVA and EIGENSTRAT under most circumstances. Applying our method on Agemap mice gene expression data reveals some interesting relationship backed by and also contributing to the existing literature.
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