التفاصيل البيبلوغرافية
| العنوان: |
Sparse Canonical Covariance Analysis for High-throughput Data. |
| المؤلفون: |
Woojoo Lee, Donghwan Lee, Youngjo Lee, Pawitan, Yudi |
| المصدر: |
Statistical Applications in Genetics & Molecular Biology; 2011, Vol. 10 Issue 1, preceding p1-24, 26p, 10 Charts, 1 Graph |
| مصطلحات موضوعية: |
ANALYSIS of covariance, GENOMICS, BIOMETRY, LINEAR statistical models, BIOLOGICAL mathematical modeling |
| مستخلص: |
Canonical covariance analysis (CCA) has gained popularity as a method for the analysis of two sets of high-dimensional genomic data. However, it is often difficult to interpret the results because canonical vectors are linear combinations of all variables, and the coefficients are typically nonzero. Several sparse CCA methods have recently been proposed for reducing the number of nonzero coefficients, but these existing methods are not satisfactory because they still give too many nonzero coefficients. In this paper, we propose a new random-effect model approach for sparse CCA; the proposed algorithm can adapt arbitrary penalty functions to CCA without much computational demands. Through simulation studies, we compare various penalty functions in terms of the performance of correct model identification. We also develop an extension of sparse CCA to address more than two sets of variables on the same set of observations. We illustrate the method with an analysis of the NCI cancer dataset. [ABSTRACT FROM AUTHOR] |
|
Copyright of Statistical Applications in Genetics & Molecular Biology is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
