Group sparse canonical correlation analysis for genomic data integration

BMC Bioinformatics

Volumn 14, Issue 1, 2013, Pages

  Lin, Dongdong ^a,b   Zhang, Jigang ^a,b   Li, Jingyao ^a,b   Calhoun, Vince D ^c,d   Deng, Hong Wen ^a,b   Wang, Yu Ping ^a,b  

^a TULANE UNIVERSITY   (United States)

^b TULANE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c MIND RESEARCH NETWORK   (United States)

^d MSC01 1070   (United States)

Author keywords

Feature selection; Genomic data integration; Group sparse CCA; SNP

Indexed keywords

BIOLOGICAL INTERPRETATION; CANONICAL CORRELATION ANALYSIS; COPY NUMBER VARIATIONS; GRAPHICAL REPRESENTATIONS; GROUP SPARSE; MULTIVARIATE STATISTICAL METHOD; SINGLE-NUCLEOTIDE POLYMORPHISMS; SNP;

ALGORITHMS; COMPUTER SIMULATION; DATA INTEGRATION; FEATURE EXTRACTION; GENE EXPRESSION;

NUMERICAL METHODS;

GENETIC MARKER;

ALGORITHM; COPY NUMBER VARIATION; GENETIC ASSOCIATION; GENETIC MARKER; GENETICS; GENOMICS; HUMAN; HUMAN GENOME; PROCEDURES; SINGLE NUCLEOTIDE POLYMORPHISM; SYSTEM ANALYSIS; ARTICLE; METHODOLOGY;

ALGORITHMS; DNA COPY NUMBER VARIATIONS; GENETIC MARKERS; GENOME, HUMAN; GENOME-WIDE ASSOCIATION STUDY; GENOMICS; HUMANS; POLYMORPHISM, SINGLE NUCLEOTIDE; SYSTEMS INTEGRATION;

EID: 84881181566     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-14-245     Document Type: Article

References (49)

1. Hamid JS, et al. Data integration in genetics and genomics: methods and challenges. Proteomics Hum Genomics 2009, 2009.
2. Le Cao KA, et al. Sparse canonical methods for biological data integration: application to a cross-platform study. Bmc Bioinform 2009, 10:34.
3. Wiley HS. Integrating multiple types of data for signaling research: challenges and opportunities. Sci Signal 2011, 4(160):pe9. 10.1126/scisignal.2001826, 21325205.
4. Le Cao KA, et al. A sparse PLS for variable selection when integrating omics data. Stat Appl Genet Mol Biol 2008, 7:35.
5. Hotelling H. Relations between two sets of variates. Biometrika 1936, 28:321-377.
6. Wegelin JA. A Survey of Partial Least Squares(PLS) Methods, with Emphasis on the Two-Block Case. Technical Report 371: Department of Statistics 2000, Seattle: University of Washington.
7. Parkhomenko E, Tritchler D, Beyene J. Sparse Canonical Correlation Analysis with Application to Genomic Data Integration. Stat Appl Genet Mol Biol 2009, 8(1):1-34.
8. Lee W, et al. Sparse Canonical Covariance Analysis for High-throughput Data. Stat Appl Genet Mol Biol 2011, 10(1):1-24.
9. Naylor MG, et al. Using Canonical Correlation Analysis to Discover Genetic Regulatory Variants. PLoS One 2010, 5(5).
10. Soneson C, et al. Integrative analysis of gene expression and copy number alterations using canonical correlation analysis. Bmc Bioinformatics 2010, 11:191. 10.1186/1471-2105-11-191, 2873536, 20398334.
11. Sui J, et al. A CCA + ICA based model for multi-task brain imaging data fusion and its application to schizophrenia. Neuroimage 2010, 51(1):123-134. 10.1016/j.neuroimage.2010.01.069, 2847043, 20114081.
12. Wright J, et al. Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 2009, 31(2):210-227.
13. Wu TT, et al. Genome-wide association analysis by lasso penalized logistic regression. Bioinformatics 2009, 25(6):714-721. 10.1093/bioinformatics/btp041, 2732298, 19176549.
14. Zou H, Hastie T, Tibshirani R. Sparse principal component analysis. J Comput Graph Stat 2006, 15(2):265-286.
15. Waaijenborg S, Hamer PCVDW, Zwinderman AH. Quantifying the association between gene expressions and DNA-markers by penalized canonical correlation analysis. Stat Appl Genet Mol Biol 2008, 7(1).
16. Witten DM, Tibshirani R, Hastie T. A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics 2009, 10(3):515-534. 10.1093/biostatistics/kxp008, 2697346, 19377034.
17. Witten DM, Tibshirani RJ. Extensions of Sparse Canonical Correlation Analysis with Applications to Genomic Data. Stat Appl Genet Mol Biol 2009, 8(1). Article 28.
18. Chalise P, Fridley BL. Comparison of penalty functions for sparse canonical correlation analysis. Comput Stat Data Anal 2012, 56(2):245-254. 10.1016/j.csda.2011.07.012, 3185379, 21984855.
19. Tyekucheva S, et al. Integrating diverse genomic data using gene sets. Genome Biol 2011, 12(10):R105. 10.1186/gb-2011-12-10-r105, 3333775, 22018358.
20. Yuan M, Lin Y. Model selection and estimation in regression with grouped variables. J R Stat Soc Ser B-Methodological 2006, 68(Part 1):49-67.
21. Meier L, Svd G, Buhlmann P. The group lasso for logistic regression. J R Stat Soc Ser B-Methodological 2008, 70(Part 1):53-71.
22. Puig A, Wiesel A, Hero A. A multidimensional shrinkagethresholding operator 2009, 113-116. SSP'09.IEEE/SP 15th Workshop on Statistical Signal Processing.
23. Simon N, Tibshirani R. Standarization and the group lasso penalty. Stat Sin 2012, 22:983-1001.
24. Simon N, et al. A sparse group lasso. J Comput Graph Stat 2013, 22(2):231-245.
25. Huang JZ, Zhang T. The Benefit of Group Sparsity. Annals of Statistics 2010, 38(4):1978-2004.
26. Friedman J, Hastie T, Tibshirani R. A note on the group Lasso and a sparse group Lasso. 2010, Available: http://arxiv.org/pdf/1001.0736.
27. Zhou H, et al. Association screening of common and rare genetic variants by penalized regression. Bioinformatics 2010, 26(19):2375-2382. 10.1093/bioinformatics/btq448, 3025646, 20693321.
28. Chen X, Liu H. An efficient optimization algorithm for structured sparse CCA, with applications to eQTL Mapping. Stat Biosci 2012, 4:3-26.
29. Chen J, et al. Structure-constrained sparse canonical correlation analysis with an application to microbiome data analysis. Biostatistics 2013, 14(2):244-258. 10.1093/biostatistics/kxs038, 23074263.
30. Shen HP, Huang JHZ. Sparse principal component analysis via regularized low rank matrix approximation. J Multivar Anal 2008, 99(6):1015-1034.
31. Yan JJ, et al. Sparse 2-D canonical correlation analysis via low rank matrix approximation for feature extraction. Ieee Signal Process Letters 2012, 19(1):51-54.
32. Kotliarov Y, et al. High-resolution global genomic survey of 178 gliomas reveals novel regions of copy number alteration and allelic imbalances. Cancer Res 2006, 66(19):9428-9436. 10.1158/0008-5472.CAN-06-1691, 17018597.
33. Scherf U, et al. A gene expression database for the molecular pharmacology of cancer. Nat Genet 2000, 24(3):236-244. 10.1038/73439, 10700175.
34. Culhane AC, Perriere G, Higgins DG. Cross-platform comparison and visualisation of gene expression data using co-inertia analysis. Bmc Bioinformatics 2003, 4:59. 10.1186/1471-2105-4-59, 317282, 14633289.
35. Wei F, Zhu H. Group coordinate descent algorithms for nonconvex penalized regression. Comput Stat Data Anal 2012, 56:316-326.
36. Ma S, et al. Integrative analysis of multiple cancer prognosis studies with gene expression measurements. Stat Med 2011, 30(28):3361-3371. 10.1002/sim.4337, 3399910, 22105693.
37. Waaijenborg S, Zwinderman AH. Correlating multiple SNPs and multiple disease phenotypes: penalized non-linear canonical correlation analysis. Bioinformatics 2009, 25(21):2764-2771. 10.1093/bioinformatics/btp491, 19689958.
38. Su Z, Marchini J, Donnelly P. HAPGEN2: simulation of multiple disease SNPs. Bioinformatics 2011, 27(16):2304-2305. 10.1093/bioinformatics/btr341, 3150040, 21653516.
39. Boudreau NJ, Jones PL. Extracellular matrix and integrin signalling: the shape of things to come. Biochem J 1999, 339(Pt 3):481-488. 1220180, 10215583.
40. Moissoglu K, Schwartz MA. Integrin signalling in directed cell migration. Biology of the Cell 2006, 98(9):547-555. 10.1042/BC20060025, 16907663.
41. Giancotti FG, Ruoslahti E. Integrin Signaling. Science 1999, 285(1028).
42. Springer TA. Traffic signals on endothelium for lymphocyte recirculation and leukocyte emigration. Annu Rev Physiol 1995, 57:827-872. 10.1146/annurev.ph.57.030195.004143, 7778885.
43. Giese A, Westphal M. Glioma invasion in the central nervous system. Neurosurgery 1996, 39(2):235-250. discussion 250-2, 10.1097/00006123-199608000-00001, 8832660.
44. Boone B, et al. EGFR in melanoma: clinical significance and potential therapeutic target. J Cutan Pathol 2011, 38(6):492-502. 10.1111/j.1600-0560.2011.01673.x, 21352258.
45. Avery-Kiejda KA, et al. P53 in human melanoma fails to regulate target genes associated with apoptosis and the cell cycle and may contribute to proliferation. Bmc Cancer 2011, 11:203. 10.1186/1471-2407-11-203, 3120805, 21615965.
46. Hess AR, et al. Phosphoinositide 3-kinase regulates membrane Type 1-matrix metalloproteinase (MMP) and MMP-2 activity during melanoma cell vasculogenic mimicry. Cancer Res 2003, 63(16):4757-4762.
47. Wang X, et al. Epithelial tight junctional changes in colorectal cancer tissues. Sci World J 2011, 11:826-841.
48. Silver M, et al. Identification of gene pathways implicated in Alzheimer's disease using longitudinal imaging phenotypes with sparse regression. Neuroimage 2012, 63:1681-1694. 10.1016/j.neuroimage.2012.08.002, 3549495, 22982105.
49. Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw 2010, 33(1):1-22. 2929880, 20808728.