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dc.contributor.authorLe Cao, K-A
dc.contributor.authorMartin, PGP
dc.contributor.authorRobert-Granie, C
dc.contributor.authorBesse, P
dc.date.accessioned2021-02-04T02:12:10Z
dc.date.available2021-02-04T02:12:10Z
dc.date.issued2009-01-26
dc.identifierpii: 1471-2105-10-34
dc.identifier.citationLe Cao, K. -A., Martin, P. G. P., Robert-Granie, C. & Besse, P. (2009). Sparse canonical methods for biological data integration: application to a cross-platform study. BMC BIOINFORMATICS, 10 (1), https://doi.org/10.1186/1471-2105-10-34.
dc.identifier.issn1471-2105
dc.identifier.urihttp://hdl.handle.net/11343/259606
dc.description.abstractBACKGROUND: 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.
dc.languageEnglish
dc.publisherBIOMED CENTRAL LTD
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleSparse canonical methods for biological data integration: application to a cross-platform study
dc.typeJournal Article
dc.identifier.doi10.1186/1471-2105-10-34
melbourne.affiliation.departmentSchool of Mathematics and Statistics
melbourne.affiliation.facultyScience
melbourne.source.titleBMC Bioinformatics
melbourne.source.volume10
melbourne.source.issue1
dc.rights.licenseCC BY
melbourne.elementsid1220039
melbourne.contributor.authorLe Cao, Kim-Anh
dc.identifier.eissn1471-2105
melbourne.accessrightsOpen Access


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