Sparse canonical methods for biological data integration: application to a cross-platform study

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• 作者：Kim-Anh Lê Cao (1) (2)
  Pascal GP Martin (3)
  Christèle Robert-Granié (1)
  Philippe Besse (2)
  
• 刊名：BMC Bioinformatics
• 出版年：2009
• 出版时间：December 2009
• 年：2009
• 卷：10
• 期：1
• 全文大小：2164KB
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• 作者单位：Kim-Anh Lê Cao (1) (2)
  Pascal GP Martin (3)
  Christèle Robert-Granié (1)
  Philippe Besse (2)
  
  1. Station d'Amélioration Génétique des Animaux UR 631, Institut National de la Recherche Agronomique, F-31326, Castanet, France
  2. Institut de Mathéematiques, Université de Toulouse et CNRS (UMR 5219), F-31062, Toulouse, France
  3. Laboratoire de Pharmacologie et Toxicologie UR 66, Institut National de la Recherche Agronomique, F-31931, Toulouse, France
  
• ISSN：1471-2105

文摘

Background In 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. Results We 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. Conclusion sPLS 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.