Open Access Open Badges Methodology article

A flexible framework for sparse simultaneous component based data integration

Katrijn Van Deun1*, Tom F Wilderjans2, Robert A van den Berg13, Anestis Antoniadis4 and Iven Van Mechelen1

Author Affiliations

1 Center for Computational Systems Biology SymBioSys, Katholieke Universiteit Leuven, 3000 Leuven, Belgium

2 Department of Psychology, Katholieke Universiteit Leuven, 3000 Leuven, Belgium

3 Present address: GlaxoSmithKline Biologicals, 1330 Rixensart, Belgium

4 Laboratoire Jean Kuntzmann, Université Joseph Fourier, 38041 Grenoble, France

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BMC Bioinformatics 2011, 12:448  doi:10.1186/1471-2105-12-448

Published: 15 November 2011


1 Background

High throughput data are complex and methods that reveal structure underlying the data are most useful. Principal component analysis, frequently implemented as a singular value decomposition, is a popular technique in this respect. Nowadays often the challenge is to reveal structure in several sources of information (e.g., transcriptomics, proteomics) that are available for the same biological entities under study. Simultaneous component methods are most promising in this respect. However, the interpretation of the principal and simultaneous components is often daunting because contributions of each of the biomolecules (transcripts, proteins) have to be taken into account.

2 Results

We propose a sparse simultaneous component method that makes many of the parameters redundant by shrinking them to zero. It includes principal component analysis, sparse principal component analysis, and ordinary simultaneous component analysis as special cases. Several penalties can be tuned that account in different ways for the block structure present in the integrated data. This yields known sparse approaches as the lasso, the ridge penalty, the elastic net, the group lasso, sparse group lasso, and elitist lasso. In addition, the algorithmic results can be easily transposed to the context of regression. Metabolomics data obtained with two measurement platforms for the same set of Escherichia coli samples are used to illustrate the proposed methodology and the properties of different penalties with respect to sparseness across and within data blocks.

3 Conclusion

Sparse simultaneous component analysis is a useful method for data integration: First, simultaneous analyses of multiple blocks offer advantages over sequential and separate analyses and second, interpretation of the results is highly facilitated by their sparseness. The approach offered is flexible and allows to take the block structure in different ways into account. As such, structures can be found that are exclusively tied to one data platform (group lasso approach) as well as structures that involve all data platforms (Elitist lasso approach).

4 Availability

The additional file contains a MATLAB implementation of the sparse simultaneous component method.