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

ABSTRACT:

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 b

SUBMITTER: Le Cao KA 

PROVIDER: S-EPMC2640358 | biostudies-literature | 2009 Jan

REPOSITORIES: biostudies-literature