Visualising associations between paired 'omics' data sets

Gonzalez, Ignacio, Le Cao, Kim-Anh, Davis, Melissa J. and Dejean, Sebastien (2012) Visualising associations between paired 'omics' data sets. BioData Mining, 5 1: 1-23. doi:10.1186/1756-0381-5-19

Author Gonzalez, Ignacio
Le Cao, Kim-Anh
Davis, Melissa J.
Dejean, Sebastien
Title Visualising associations between paired 'omics' data sets
Journal name BioData Mining   Check publisher's open access policy
ISSN 1756-0381
Publication date 2012-11-13
Sub-type Article (original research)
DOI 10.1186/1756-0381-5-19
Open Access Status DOI
Volume 5
Issue 1
Start page 1
End page 23
Total pages 23
Place of publication London, United Kingdom
Publisher BioMed Central
Language eng
Formatted 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.

Keyword Canonical correlation-analysis
Gene-expression data
Molecular pharmacology
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ
Additional Notes Article # 19

Document type: Journal Article
Sub-type: Article (original research)
Collection: Institute for Molecular Bioscience - Publications
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Citation counts: TR Web of Science Citation Count  Cited 24 times in Thomson Reuters Web of Science Article | Citations
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