Visualising associations between paired ‘omics’ data sets View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2012-12

AUTHORS

Ignacio González, Kim-Anh Lê Cao, Melissa J Davis, 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. More... »

PAGES

19

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1756-0381-5-19

DOI

http://dx.doi.org/10.1186/1756-0381-5-19

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1043908874

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/23148523


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