Supersparse principal component analyses for highthroughput genomic data
 Donghwan Lee^{1},
 Woojoo Lee^{2},
 Youngjo Lee^{1} and
 Yudi Pawitan^{2}Email author
DOI: 10.1186/1471210511296
© Lee et al; licensee BioMed Central Ltd. 2010
Received: 22 February 2010
Accepted: 2 June 2010
Published: 2 June 2010
Abstract
Background
Principal component analysis (PCA) has gained popularity as a method for the analysis of highdimensional genomic data. However, it is often difficult to interpret the results because the principal components are linear combinations of all variables, and the coefficients (loadings) are typically nonzero. These nonzero values also reflect poor estimation of the true vector loadings; for example, for gene expression data, biologically we expect only a portion of the genes to be expressed in any tissue, and an even smaller fraction to be involved in a particular process. Sparse PCA methods have recently been introduced for reducing the number of nonzero coefficients, but these existing methods are not satisfactory for highdimensional data applications because they still give too many nonzero coefficients.
Results
Here we propose a new PCA method that uses two innovations to produce an extremely sparse loading vector: (i) a randomeffect model on the loadings that leads to an unbounded penalty at the origin and (ii) shrinkage of the singular values obtained from the singular value decomposition of the data matrix. We develop a stable computing algorithm by modifying nonlinear iterative partial least square (NIPALS) algorithm, and illustrate the method with an analysis of the NCI cancer dataset that contains 21,225 genes.
Conclusions
The new method has better performance than several existing methods, particularly in the estimation of the loading vectors.
Background
Principal component analysis (PCA) or its equivalent singularvalue decomposition (SVD) is widely used for the analysis of highdimensional data. For such gene expression data with an enormous number of variables, PCA is a useful technique for visualization, analyses and interpretation [1–4].
Lower dimensional views of data made possible, via the PCA, often give a global picture of gene regulation that would reveal more clearly, for example, a group of genes with similar or related molecular functions or cellular states, or samples of similar or connected phenotypes, etc. PCA results might be used for clustering, but bear in mind that PCA is not simply a clustering method, as it has distinct analytical properties and utilities from the clustering methods. Simple interpretation and subsequent usage of PCA results often depends on the ability to identify subsets with nonzero loadings, but this effort is hampered by the fact that the standard PCA yields nonzero loadings on all variables. If the lowdimensional projections are relatively simple, many loadings are not statistically significant, so the nonzero values reflect the high variance of the standard method. In this paper our focus on the PCA methodology is constrained to produce sparse loadings.
where D is n × p matrix with (i, i )th element d_{ i }; the columns of Z = UD = XV are the principal component scores, and the columns of the p × p matrix V are the corresponding loadings. The vector v_{ k }in (1) is the kth column of V.
Each principal component in (2) is a linear combination of p variables, where the loadings are typically nonzero so that PCA results are often difficult to interpret. To get sparse loadings, [5] proposed to use L_{1}penalty, which corresponds to the leastabsolute shrinkage and selection operator (LASSO; [6]). [7] proposed to use the socalled elasticnet (EN) penalty. However, LASSO and EN may not be satisfactory either, because it can still gives too many nonzero coefficients. [8] proposed the smoothlyclipped absolute deviation (SCAD) penalty for oracle variable selection. Recently, in regression setting, [9] proposed a new randomeffect model using a gamma scale mixture, which gives various types of penalty, including the normaltype (bellshaped for ridge penalty), cuspedtype (LASSO and SCADtype), and a new (singular) unbounded penalty at the origin. [9] showed that the new unbounded penalty can yield very sparse estimates that are better than LASSO both in prediction and sparsity.
In this paper we use the randomeffect model approach of [9] for sparse PCA (SPCA); the model gives unbounded gains for zero loadings at the origin, so it forces many estimated coefficients to zero. We improve the estimation further by shrinking the singular values from the SVD of the data; the resulting procedure is called supersparse PCA (SSPCA). We provide some simulation studies that indicate that these SPCA methods perform better than existing ones, and illustrate their use using a cancer geneexpression dataset with 21,225 genes. We also show how to modify the ordinary NIPALS algorithm [10] to implement these methods computationally.
Results
Numerical studies
where , Σ_{22} = ϕI_{p4}and J_{ k } is the k × k matrix of ones. Here we consider cases (n, p) = (80, 20) for n > p and (n, p) = (50, 200) for n <p. Based on 100 simulated data, we compare our new sparse PCA method using the hlikelihood (HL; See Methodology section) with the LASSO and EN penalties for both SPCA and SSPCA methods. We also tried the SCAD method but the results are very similar to LASSO, so we do not report results for SCAD.
When v_{1} = , dist (v_{1}, ) = 0.
Simulation results: estimation
SPCA  

n  p 

 PCA  HL  LASSO  EN 
80  20  2.0  0.1  0.054 (0.010)  0.023 (0.011)  0.022 (0.010)  0.025 (0.013) 
0.5  0.1  0.109 (0.021)  0.045 (0.021)  0.051 (0.022)  0.055 (0.029)  
50  200  2.0  0.1  0.223 (0.022)  0.029 (0.014)  0.035 (0.015)  0.056 (0.028) 
0.5  0.1  0.424 (0.041)  0.062 (0.032)  0.080 (0.033)  0.122 (0.058)  
PCA*  HL  SSPCA LASSO  EN  
80  20  2.0  0.1  0.055 (0.010)  0.020 (0.009)  0.021 (0.010)  0.022 (0.010) 
0.5  0.1  0.113 (0.020)  0.042 (0.020)  0.050 (0.023)  0.050 (0.026)  
50  200  2.0  0.1  0.218 (0.025)  0.026 (0.013)  0.032 (0.014)  0.055 (0.030) 
0.5  0.1  0.993 (0.010)  0.063 (0.030)  0.083 (0.044)  0.866 (0.000) 
Simulation results: model selection
SPCA  SSPCA  

n  p 

 PCA  HL  LASSO  EN  PCA*  HL  LASSO  EN 
80  20  2.0  0.1  0  72  12  64  0  95  14  99 
0/16  16/16  14/16  16/16  0/16  16/16  15/16  16/16  
0/4  0/4  0/4  0/4  0/4  0/4  0/4  0/4  
0.5  0.1  0  77  1  56  0  100  43  99  
0/16  16/16  12/16  16/16  0/16  16/16  15/16  16/16  
0/4  0/4  0/4  0/4  0/4  0/4  0/4  0/4  
50  200  2.0  0.1  0  73  0  88  0  100  27  87 
0/196  196/196  184.5/196  196/196  0/196  196/196  194/196  196/196  
0/4  0/4  0/4  0/4  0/4  0/4  0/4  0/4  
0.5  0.1  0  79  0  70  0  97  84  0  
0/196  196/196  185.5/196  196/196  0/196  196/196  196/196  196/196  
0/4  0/4  0/4  0/4  0/4  0/4  0/4  3/4 
Simulation results: prediction
SSPCA  

n  p 

 PCA  HL  LASSO  EN 
80  20  2.0  0.1  7.979 (0.831)  7.998 (0.837)  7.996 (0.842)  7.970 (1.116) 
0.5  0.1  2.050 (0.213)  2.057 (0.222)  2.055 (0.225)  2.088 (0.283)  
50  200  2.0  0.1  7.907 (1.633)  8.242 (1.599)  8.242 (1.601)  8.149 (1.386) 
0.5  0.1  1.769 (0.362)  2.143 (0.418)  2.140 (0.414)  2.071 (0.349)  
SSPCA  
PCA*  HL  LASSO  EN  
80  20  2.0  0.1  7.954 (1.125)  7.999 (0.849)  7.997 (0.850)  7.978 (1.115) 
0.5  0.1  2.062 (0.292)  2.057 (0.226)  2.057 (0.225)  2.088 (0.280)  
50  200  2.0  0.1  7.564 (1.718)  8.243 (1.593)  8.243 (1.597)  7.928 (1.755) 
0.5  0.1  0.242 (0.075)  2.137 (0.452)  2.149 (0.424)  0.503 (0.316) 
Analysis of NCI data
In the analysis of microarray data it is often of interest to coregulated genes, since they will point to some common involvement in molecular functions or biological processes or cellular states. PCA is a useful tool for such analyses [1–4]; since interpretation depends on comparing the relative sizes of the loading vectors, the sparse loadings in SPCA are much easier to interpret than ordinary PCA. Furthermore, the previous section also shows that SPCA has better estimation characteristics than the ordinary PCA. For illustrations we consider the socalled NCI60 microarray data downloaded from the CellMiner program package, National Cancer Institute http://discover.nci.nih.gov/cellminer/. Only n = 59 of the 60 human cancer cell lines were used in the analysis, as one of the cell lines had missing microarray information. The cell lines consist of 9 different cancers and were used by the Developmental Therapeutics Program of the U.S. National Cancer Institute to screen > 100,000 compounds and natural products. The number of genes is p = 21,225.
Analyses of NCI data: number of zero loadings
PCA  SPCA  SSPCA  

HL  LASSO  HL  LASSO  
214/21225  7966/21225  650/21225  19965/21225  1144/21225 
(1.01)  (37.53)  (3.06)  (94.06)  (5.39) 
To select the number of principal components, we use a permutation approach as follows. First, we randomly permute the expression values within each sample (row) of X to create permuted data X_{ perm }. Then PCA is performed on X_{ perm }to get the singular values . We perform P = 1000 permutations, from which we can compute the pvalues of the observed d_{ k }'s. The number of principal components, k_{0}, is such that the pvalue of d_{ k }'s is less than 0.001 when k ≤ k_{0}.
Analysis of NCI data: number of zero loadings
Principal component scores  Z _{1}  Z _{2}  Z _{3}  Z _{4}  Z _{5}  Z _{6}  Z _{7}  Z _{8} 

PCA  
Number of nonzero loadings  21011  20385  19226  21099  20948  20817  20945  20997 
Adjusted Variance (%)  12.3  10.2  6.6  4.1  3.6  3.2  2.9  2.6 
Cumulative adjusted Variance (%)  12.3  22.5  29.1  33.2  36.8  40.0  42.9  45.5 
SPCA  HL  
Number of nonzero loadings  13259  4086  15362  13547  13946  10445  9890  10958 
Adjusted Variance (%)  20.6  13.4  11.5  6.4  6.1  4.9  4.0  4.1 
Cumulative adjusted Variance (%)  20.6  34.0  45.5  51.9  58.0  62.9  66.9  71.0 
SSPCA  HL  
Number of nonzero loadings  1260  681  375  290  47  58  33  3434 
Adjusted Variance (%)  22.3  8.7  6.1  6.5  1.3  0.4  0.0  1.6 
Cumulative adjusted Variance (%)  22.3  31.0  37.1  43.6  44.9  45.3  45.3  46.9 
Gene Ontology analysis
Number  GO ID  GO Term  Pvalue(1)  Pvalue(2)  Pvalue(3) 

1  GO:0048856  anatomical structure development  1.6e10  1.5e09  4.5e07 
2  GO:0009653  anatomical structure morphogenesis  2.9e10  4.8e06  
3  GO:0008283  cell proliferation  1.3e09  
4  GO:0050793  regulation of developmental process  1.7e09  9.4e06  
5  GO:0032502  developmental process  3.8e09  8.1e08  4.9e06 
6  GO:0042127  regulation of cell proliferation  5.8e08  3.9e06  
7  GO:0048513  organ development  6.6e08  
8  GO:0048869  cellular developmental process  1e07  
9  GO:0048731  system development  1.1e07  3.6e07  5.3e06 
10  GO:0007155  cell adhesion  1.3e07  7.6e07  
11  GO:0022610  biological adhesion  1.3e07  7.6e07  
12  GO:0051093  negative regulation of developmental process  1.9e06  
13  GO:0048519  negative regulation of biological process  2.8e06  
14  GO:0048523  negative regulation of cellular process  3.4e06  
15  GO:0009605  response to external stimulus  2.8e07  
16  GO:0043065  positive regulation of apoptosis  7.4e06  
17  GO:0043068  positive regulation of programmed cell death  8.6e06  
18  GO:0042981  regulation of apoptosis  9.6e06  
19  GO:0032501  multicellular organismal process  1.3e06  
20  GO:0007275  multicellular organismal development  4.3e06 
Comparative GO analyses from the ordinary PCA are given in the Additional file 1. We use the same number of topranking nonzero loadings as for the SSPCA, which are 1,260, 681 and 375 for the first 3 principal components, respectively. Out of these, the number of overlapping probes between the SSPCA and PCA are 462, 194 and 60. These overlaps are substantially more (up to 8 times more) than expected under random rearrangement. However, there is sufficiently large number of distinct probes in the two methods, so the GO analyses could be different. The Pvalues from the SSPCAbased GO analyses are more significant than those from the ordinary PCA; this may be due to better estimation of the loadings, so that the SSPCA has better power than the ordinary PCA in revealing biologicallyimportant grouping of genes.
Discussions and Conclusions
PCA is one of the most important tools in multivariate statistics, where it has been used, for example, in data reduction or visualization of highdimensional data. The emergence of ultrahigh dimensional data such as in genomics, involving 10,000s of variables but with only a few samples has brought new opportunities for PCA applications. However, there are new challenges also, particularly on the interpretation of results. If we treat PCA quantities such as the loading vectors as parameter estimates, the largepsmalln applications typically produce very noisy estimates. This is obvious since the loading vectors are a statistic derived from the sample covariance matrix, and the latter is not well estimated.
It is well known that improved estimation can come by imposing constraints, and in this case sparsity constraint is natural. As PCA scores capture some underlying biological processes, we do not expect every gene in the genome to be involved. Out of possibly 30,000 genes we can expect only a small fraction, probably less than 1,000, to be involved in a cellular process. Hence sparsity constraint can help in reducing the number of loading parameters to estimate.
Imposing statistical constraints can be achieved by applying a penalty approach as used by the ridge regression or the LASSO methods [6]. In this paper we have investigated a randomeffect model approach using a gamma scale mixture, which leads to a class of penalties that includes the ridge and LASSO penalties as special cases. One significant property is that it can produce unbounded penalties on the origin, which leads to stronger constraints and more sparse estimates. From our results it seems clear that the penalty approach alone is not able to yield sufficiently sparse PCA for highdimensional genomic data. Additionally we also need the shrinkage on the singular values of the data matrix. In simulation studies we show that the proposed methods outperform existing methods both in estimation and model selection. Hence we believe that the new SPCA methods are promising tools for highdimensional data analyses.
For future works, it will be of interest to apply supersparse technique in this paper to locallylinear methods of dimensionality reduction (e.g. [13]]), partialleast squares (PLS) regression and classification methods (e.g. [14]), or other highthroughput data analysis method where dimensionality reduction is used (e.g. [15]).
Methodology
NIPALS algorithm for PCA
Standard algorithms for SVD (e.g. [16]) give the PCA loadings, but if p is large and we only want to obtain a few singular vectors, the computation to obtain the whole set of singular vectors may be impractical. Furthermore, with these algorithms it is not obvious how to impose sparsity on the loadings. [10] described a NIPALS algorithm that works like a power method ([17], p.523) for obtaining the largest eigenvalue of a matrix and its associated eigenvector. The NIPALS algorithm computes only a singular vector at a time, so it is efficient if we only want to extract a few singular vectors. Also the steps are recognizable in regression terms, so the algorithm is immediately amenable to randomeffect modification as needed to obtain the various SPCA methods proposed in this paper.
 1.
Find
 2.
Normalize
 3.
Find z _{1}: z _{1} ← X ^{ T } v _{1}
 4.
Repeat steps 1 to 3 until convergence.
To obtain the secondlargest singular value, first compute residual , then apply the NIPALS algorithm above by replacing X by X_{2}.
Sparse PCA via randomeffect models
where p_{ λ }(·) is a penalty function. For example, p_{ λ }(v_{1j}) = λv_{1j} gives LASSO, gives ridge, and gives EN, where λ, λ_{1} and λ_{2} are tuning parameters. For the prediction the ridgetype penalty is effective and for sparse estimation the LASSOtype penalty is recommended, so that EN [19] has been recommended as a compromise between the ridge and LASSO methods. [7] proposed to use EN for sparse (SPCA), but it gives less sparse estimates than LASSO.
where solves dh/du = 0.
using and λ = ϕ/θ. In randomeffect model approach, the penalty function p_{ λ }(v_{1j}) stems from a probabilistic model . As noted previously the proposed penalty p_{ λ }(v_{1j}) is nonconvex. However, by expressing the model for p_{ λ }(v_{1j}) hierarchically as (i) v_{1j}u_{ j }is normal and (ii) u_{ j }is gamma, both models can be fitted by convex GLM optimizations. Thus, the proposed IWLS algorithm overcomes the difficulties of a nonconvex optimization by solving twointerlinked convex optimizations [22].
The derivatives of the penalty functions.
Types 


LASSO  λ 
SCAD 

HL 

Other methods for sparse principal component analysis
for deriving the first sparse loading vector v_{1}. Given θ, this optimization problem becomes a naive elastic net problem for v_{1}. Given v_{1} , θ can updated from SVD of X^{ T }X v_{1}. These two steps are repeated until v_{1} converges. Following [25], (11) is different from our objective function (5) even when we use the same penalty function. In fact, (5) is very close to the objective function of [26], but we put the normalization constraint of the loading inside iterated procedure so that it could make a different result. In this paper, we used the function spca() in the Rpackage elasticnet for the EN method in the simulation studies.
Conditionnumber constraint for SPCA
where Λ = diag(l_{1}, ..., l_{ p }and for i = 1, ..., p is the eigenvalues of S_{ X }in nonincreasing order (l_{ 1 } ≥ … ≥ l_{ p }≥ 0). Let the p × 1 random vectors x_{1}, …, x_{ n }be rows of X that have zero mean vector and true covariance matrix Σ with the nonincreasing eigenvalues, λ_{1} ≥ ... ≥ λ_{ p }. When our goal is to estimate Σ, the sample covariance matrix S_{ X }can be used. Many applications require a covariance estimate that is not only invertible but also wellconditioned. An immediate problem arises when n <p, where the estimate S_{ X }is singular. Even when n > p, the eigenstructure tends to be systematically distorted unless p/n is small [27], resulting in illconditioned estimator for Σ.
where the eigenvalues . To estimate the shrinkage parameter κ_{max}, they proposed to use the Kfold cross validation.
where D* is n × p matrix with (i, i)th diagonal element . Thus, for conditionnumber constrained PCA we use X* instead of the original data matrix X. As the procedure yields extremely sparse loading vectors, we call it SSPCA, for supersparse PCA.
[29] considered the estimation of covariance matrix when p is not very large. However, for large p such as over 10,000 in gene expression data, it becomes computationally too intensive. Because the aim is to obtain a few singular vectors, not whole p singular vectors, when p > n in this paper we propose to apply the above algorithm to X^{ T }and the results are transformed back appropriately.
Modified NIPALS algorithm for SPCA and SSPCA
where is defined in (9). For SSPCA we also apply this modified step, but replace X by X* defined in (14).
Tuning parameter selection
where is the estimated loadings from the k th training sets (the whole data without the k th validation set) and S_{X[k]}is the sample variance based on the k th validation set. For the numerical studies in Section we use K = 5.
Declarations
Acknowledgements
This research is partially funded by a grant for the Swedish Science Foundation.
Authors’ Affiliations
References
 Alter O, Brown P, Botstein D: Singular value decomposition for genomewide expression data processing and modeling. Proceedings of the National Academy of Science 2000, 97: 10101–10106. 10.1073/pnas.97.18.10101View ArticleGoogle Scholar
 Kuruvilla F, Park P, Schreiber S: Vector algebra in the analysis of genomewide expression data. Genome Biol Epub 2002, 3(3):RESEARCH0011.1–11. 10.1186/gb200233research0011Google Scholar
 Sharov A, Dudekula D, Ko M: A webbased tool for principal component and significance analysis of microarray data. Bioinformatics 2005, 21(10):2548–9. 10.1093/bioinformatics/bti343View ArticlePubMedGoogle Scholar
 Scholz M, Selbig J: Visualization and analysis of molecular data. Methods Mol Biol 2005, 358: 87–104. full_textView ArticleGoogle Scholar
 Jolliffe I, Trendafilov N, Uddin M: A modified principal component technique base on the Lasso. Journal of Computational and Graphical Statistics 2003, 12: 531–547. 10.1198/1061860032148View ArticleGoogle Scholar
 Tibshirani R: Regression shrinkage and selection via the LASSO. Journal of the Royal Statistical Society, series B 1996, 58: 267–288.Google Scholar
 Zou H, Hastie T, Tibshirani R: Sparse principal components analysis. Journal of Computational and Graphical Statistics 2006, 15: 265–286. 10.1198/106186006X113430View ArticleGoogle Scholar
 Fan J, Li R: Variable selection via nonconcave penalized likelihood and its oracle properties. Journal American Statistical Association 2001, 96: 1348–1360. 10.1198/016214501753382273View ArticleGoogle Scholar
 Lee Y, Oh H: A new randomeffect model for sparse variable selection. Submitted for publication
 Höskuldsson A: PLS regression methods. Journal of Chemometrics 1988, 2: 211–228. 10.1002/cem.1180020306View ArticleGoogle Scholar
 Johnstone I, Lu A: On consistency and sparsity for principal components analysis in high dimensions. Journal of American Statistical Association 2009, 104: 682–693. 10.1198/jasa.2009.0121View ArticleGoogle Scholar
 Consortium GO: Gene ontology: tool for the unification of biology. Nature Genetics 2000, 25: 25–29. 10.1038/75556View ArticleGoogle Scholar
 Roweis S, Saul L: Nonlinear dimensionality reduction by locally linear embedding. Science 2000, 290: 2323–2326. 10.1126/science.290.5500.2323View ArticlePubMedGoogle Scholar
 Boulesteix A: PLS Dimension Reduction for Classification with Microarray Data. Statistical Applications in Genetics and Molecular Biology 2004, 3: 33. 10.2202/15446115.1075View ArticleGoogle Scholar
 Nueda M, Conesa A, Westerhuis J, Hoefsloot H, Smilde A, Talon M, Ferrer A: Discovering gene expression patterns in time course microarray experiments by ANOVASCA. Bioinformatics 2007, 23: 1792–1800. 10.1093/bioinformatics/btm251View ArticlePubMedGoogle Scholar
 Golub G, Reinsch C: Singular value decomposition and least squares solutions. In Handbook for Automatic Computation II: Linear Algebra. Edited by: Householder A, Bauer F. New York: SpringerVerlag; 1971.Google Scholar
 Horn R, Johnson C: Matrix analysis. Cambridge: Cambridge university press; 1985.View ArticleGoogle Scholar
 Salim A, Pawitan Y, Bond K: Modelling association between two irregularly observed spatiotemporal processes by using maximum covariance analysis. Applied statistics 2005, 54: 555–573.Google Scholar
 Zou H, Hastie T: Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society, series B 2005, 67: 301–320. 10.1111/j.14679868.2005.00503.xView ArticleGoogle Scholar
 Lee Y, Nelder J: Double hierarchical generalized linear models (with discussion). Applied Statistics 2006, 55: 139–185.Google Scholar
 Lee Y, Nelder J: Hierarchical generalized linear models (with discussion). Journal of the Royal Statistical Society, series B 1996, 58: 619–678.Google Scholar
 Lee Y, Nelder J, Pawitan Y: Matrix analysisGeneralized Linear Models With Random Effects: Unified Analysis via HLikelihood. London: Chapman and Hall; 2006.View ArticleGoogle Scholar
 Efron B, Morris C: Data analysis using Stein's estimator and its generalizations. Journal of American Statistical Association 1975, 70: 311–319. 10.2307/2285814View ArticleGoogle Scholar
 Fan J: Comments on "Wavelets in statistics: A review" by A. Antoniadis. Journal of Italian Statistical Association 1997, 6: 131–138. 10.1007/BF03178906View ArticleGoogle Scholar
 Witten D, Tibshirani R, Haste T: A penalized matrix decomposition, with application to sparse principal components and canonical correlation analysis. Biostatistics 2009, 10: 515–534. 10.1093/biostatistics/kxp008View ArticlePubMedPubMed CentralGoogle Scholar
 Shen H, Huang J: Sparse principal component analysis via regularized low rank matrix approximation. Journal of Multivariate Analysis 2008, 99: 1015–1034. 10.1016/j.jmva.2007.06.007View ArticleGoogle Scholar
 Dempster A: Covariance selection. Biometrics 1972, 28: 157–175. 10.2307/2528966View ArticleGoogle Scholar
 Ledoit O, Wolf M: A wellconditioned estimator for largedimensional covariance matrices. Journal of Multivariate Analysis 2004, 88: 365–411. 10.1016/S0047259X(03)000964View ArticleGoogle Scholar
 Won J, Lim J, Kim S, Rajaratnam B: Maximum likelihood covariance estimation with a conditionnumber constraint. Submitted for publication
 Parkomenko E, Tritchler D, Beyene J: Sparse canonical correlation analysis with application to genomic data integration. Statistical Applications in Genetics and Molecular Biology 2009., 8:Google Scholar
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