Abstract
BackgroundIn the context of systems biology, few sparse approaches have been proposed so far to integrate several data sets. It is however an important and fundamental issue that will be widely encountered in post genomic studies, when simultaneously analyzing transcriptomics, proteomics and metabolomics data using different platforms, so as to understand the mutual interactions between the different data sets. In this high dimensional setting, variable selection is crucial to give interpretable results. We focus on a sparse Partial Least Squares approach (sPLS) to handle two-block data sets, where the relationship between the two types of variables is known to be symmetric. Sparse PLS has been developed either for a regression or a canonical correlation framework and includes a built-in procedure to select variables while integrating data. To illustrate the canonical mode approach, we analyzed the NCI60 data sets, where two different platforms (cDNA and Affymetrix chips) were used to study the transcriptome of sixty cancer cell lines.ResultsWe compare the results obtained with two other sparse or related canonical correlation approaches: CCA with Elastic Net penalization (CCA-EN) and Co-Inertia Analysis (CIA). The latter does not include a built-in procedure for variable selection and requires a two-step analysis. We stress the lack of statistical criteria to evaluate canonical correlation methods, which makes biological interpretation absolutely necessary to compare the different gene selections. We also propose comprehensive graphical representations of both samples and variables to facilitate the interpretation of the results.ConclusionsPLS and CCA-EN selected highly relevant genes and complementary findings from the two data sets, which enabled a detailed understanding of the molecular characteristics of several groups of cell lines. These two approaches were found to bring similar results, although they highlighted the same phenomenons with a different priority. They outperformed CIA that tended to select redundant information.
Highlights
In the context of systems biology, few sparse approaches have been proposed so far to integrate several data sets
The analysis of the NCI60 data sets with characteristics of the data (CCA) with Elastic Net penalization (CCA-EN), Co-Inertia Analysis (CIA) and sparse Partial Least Squares approach (sPLS) evidenced the main differences between these methods
The loadings or weight vectors obtained were not orthogonal, in contrary to CCA-EN and sPLS. This resulted in some redundancy in the gene selections, which may be a limitation for the biological interpretation, as it led to less specific results
Summary
In the context of systems biology, few sparse approaches have been proposed so far to integrate several data sets It is an important and fundamental issue that will be widely encountered in post genomic studies, when simultaneously analyzing transcriptomics, proteomics and metabolomics data using different platforms, so as to understand the mutual interactions between the different data sets. The application of linear multivariate models such as Partial Least Squares regression (PLS, [1]) and Canonical Correlation Analysis (CCA, [2]), are often limited by the size of the data set (ill-posed problems, CCA), the noisy and the multicollinearity characteristics of the data (CCA), and the lack of interpretability (PLS) These approaches still remain extremely interesting for integrating data sets. This computational and exploratory approach is extremely popular thanks to its efficiency
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