Comparison of Penalty Functions for Sparse Canonical Correlation Analysis.
Comparison of Penalty Functions for Sparse Canonical Correlation Analysis.
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DOI：
10.1016/j.csda.2011.07.012
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发表时间：
2012-02-01
影响因子：
1.8
通讯作者：
Fridley, Brooke L.
中科院分区：
文献类型：
--
作者：
Chalise, Prabhakar;Fridley, Brooke L.
典型相关分析（CCA）是一种广泛使用的多变量方法，用于评估两组变量之间的关联。然而，当变量的数量远远超过受试者的数量时，如在大规模基因组研究的情况下，传统的CCA方法是不合适的。此外，当变量高度相关时，样本协方差矩阵变得不稳定或不确定。为了克服这两个问题，稀疏典型相关分析（SCCA）的多个数据集已经提出了使用Lasso类型的惩罚。然而，这些方法不具有对解的稀疏性的直接控制。还建议使用贝叶斯信息准则（BIC）的附加步骤来进一步过滤掉不重要的特征。在本文中，比较了四个惩罚函数（拉索，弹性网络，SCAD和硬阈值）SCCA与BIC过滤步骤和没有进行了使用真实的和模拟的基因型和mRNA表达数据。本研究表明，SCAD惩罚与BIC过滤器将是一个较好的惩罚函数的SCCA应用到基因组数据。
Canonical correlation analysis (CCA) is a widely used multivariate method for assessing the association between two sets of variables. However, when the number of variables far exceeds the number of subjects, such in the case of large-scale genomic studies, the traditional CCA method is not appropriate. In addition, when the variables are highly correlated the sample covariance matrices become unstable or undefined. To overcome these two issues, sparse canonical correlation analysis (SCCA) for multiple data sets has been proposed using a Lasso type of penalty. However, these methods do not have direct control over sparsity of solution. An additional step that uses Bayesian Information Criterion (BIC) has also been suggested to further filter out unimportant features. In this paper, a comparison of four penalty functions (Lasso, Elastic-net, SCAD and Hard-threshold) for SCCA with and without the BIC filtering step have been carried out using both real and simulated genotypic and mRNA expression data. This study indicates that the SCAD penalty with BIC filter would be a preferable penalty function for application of SCCA to genomic data.
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影响因子：
2.1
作者：
Gauderman, W. James;Murcray, Cassandra;Conti, David V.
通讯作者：
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影响因子：
2.5
作者：
HOERL, AE;KENNARD, RW
通讯作者：
KENNARD, RW
DOI：
10.2202/1544-6115.1470
发表时间：
2009-01-01
影响因子：
0.9
作者：
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通讯作者：
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作者：
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影响因子：
3.7
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