Archive for the ‘Metabolomics’ Category

Visualising associations between paired `omics’ data sets

Saturday, November 17th, 2012

Visualising associations between paired `omics’ data sets by Ignacio González, Kim-Anh Lê Cao, Melissa J Davis and Sébastien Déjean.

Abstract:

Background

Each omics platform is now able to generate a large amount of data. Genomics, proteomics, metabolomics, interactomics are compiled at an ever increasing pace and now form a core part of the fundamental systems biology framework. Recently, several integrative approaches have been proposed to extract meaningful information. However, these approaches lack of visualisation outputs to fully unravel the complex associations between different biological entities.

Results

The multivariate statistical approaches ‘regularized Canonical Correlation Analysis’ and ‘sparse Partial Least Squares regression’ were recently developed to integrate two types of highly dimensional ‘omics’ data and to select relevant information. Using the results of these methods, we propose to revisit few graphical outputs to better understand the relationships between two ‘omics’ data and to better visualise the correlation structure between the different biological entities. These graphical outputs include Correlation Circle plots, Relevance Networks and Clustered Image Maps. We demonstrate the usefulness of such graphical outputs on several biological data sets and further assess their biological relevance using gene ontology analysis.

Conclusions

Such graphical outputs are undoubtedly useful to aid the interpretation of these promising integrative analysis tools and will certainly help in addressing fundamental biological questions and understanding systems as a whole.

Availability

The graphical tools described in this paper are implemented in the freely available R package mixOmics and in its associated web application.

Just in case you are looking for something a little more challenging this weekend than political feeds on Twitter. 😉

Is “higher dimensional” data everywhere? Just more obvious in the biological sciences?

If so, there are lessons here for manipulation/visualization of higher dimensional data in other areas as well.