Group sparse canonical correlation analysis for genomic data integration
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- 作者：Dongdong Lin (14) (15)
Jigang Zhang (15) (16)
Jingyao Li (14) (15)
Vince D Calhoun (17) (18)
Hong-Wen Deng (15) (16)
Yu-Ping Wang (14) (15) (16) - 关键词：Group sparse CCA ; Genomic data integration ; Feature selection ; SNP
- 刊名：BMC Bioinformatics
- 出版年：2013
- 出版时间：December 2013
- 年：2013
- 卷：14
- 期：1
- 全文大小：1005KB
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Jigang Zhang (15) (16)
Jingyao Li (14) (15)
Vince D Calhoun (17) (18)
Hong-Wen Deng (15) (16)
Yu-Ping Wang (14) (15) (16)
14. Biomedical Engineering Department, Tulane University, New Orleans, LA, USA
15. Center of Genomics and Bioinformatics, Tulane University, New Orleans, LA, USA
16. Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, LA, USA
17. The Mind Research Network, Albuquerque, NM, 87131, USA
18. Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, 87131, USA - ISSN：1471-2105
文摘
Background The emergence of high-throughput genomic datasets from different sources and platforms (e.g., gene expression, single nucleotide polymorphisms (SNP), and copy number variation (CNV)) has greatly enhanced our understandings of the interplay of these genomic factors as well as their influences on the complex diseases. It is challenging to explore the relationship between these different types of genomic data sets. In this paper, we focus on a multivariate statistical method, canonical correlation analysis (CCA) method for this problem. Conventional CCA method does not work effectively if the number of data samples is significantly less than that of biomarkers, which is a typical case for genomic data (e.g., SNPs). Sparse CCA (sCCA) methods were introduced to overcome such difficulty, mostly using penalizations with l-1 norm (CCA-l1) or the combination of l-1and l-2 norm (CCA-elastic net). However, they overlook the structural or group effect within genomic data in the analysis, which often exist and are important (e.g., SNPs spanning a gene interact and work together as a group). Results We propose a new group sparse CCA method (CCA-sparse group) along with an effective numerical algorithm to study the mutual relationship between two different types of genomic data (i.e., SNP and gene expression). We then extend the model to a more general formulation that can include the existing sCCA models. We apply the model to feature/variable selection from two data sets and compare our group sparse CCA method with existing sCCA methods on both simulation and two real datasets (human gliomas data and NCI60 data). We use a graphical representation of the samples with a pair of canonical variates to demonstrate the discriminating characteristic of the selected features. Pathway analysis is further performed for biological interpretation of those features. Conclusions The CCA-sparse group method incorporates group effects of features into the correlation analysis while performs individual feature selection simultaneously. It outperforms the two sCCA methods (CCA-l1 and CCA-group) by identifying the correlated features with more true positives while controlling total discordance at a lower level on the simulated data, even if the group effect does not exist or there are irrelevant features grouped with true correlated features. Compared with our proposed CCA-group sparse models, CCA-l1 tends to select less true correlated features while CCA-group inclines to select more redundant features.