Visualising associations between paired ‘omics’ data sets

Abstract

BackgroundEach 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.ResultsThe 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.ConclusionsSuch 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.AvailabilityThe graphical tools described in this paper are implemented in the freely available package and in its associated web application.

Small RNA Transcriptome of the Oral Microbiome during Periodontitis Progression

“…Association of expression profiles of sRNAs with expression profiles of mRNAs during periodontitis progression was performed using multivariate statistical analysis and visualization tools implemented in the R package "mixOmics." A detailed description of the methods used here can be found in a study by Gonzalez et al (32). Transcript hits were normalized by frequencies obtained in the metagenome before mixOmics analysis.…”

Sensory Impact of Extended Maceration and Regulated Deficit Irrigation on Washington State Cabernet Sauvignon Wines

“…Confidence ellipses indicating 95% confidence intervals were based on the multivariate distribution of the Hotelling's test for p < 0.05 and were constructed using SensoMineR panellipse function on R (Husson et al 2005). Two-way canonical correlation analysis with clustered image maps to relate sensory and chemical data was obtained using the mix0mics package of R software (González et al 2012).…”

“…The relationship between the significant chemical features of the wines and their sensory properties was examined by canonical correlation analysis (Figure 2). In addition to an agglomerative hierarchical cluster analysis of the chemical and sensory data sets, the correlation coefficients between any given pair of variables in the two retained dimensions are also provided (González et al 2012). Shared cluster membership indicates the strength of the relationship between the canonical variates for both the chemical and sensory variables.…”

Gene selection heuristic algorithm for nutrigenomics studies

“…So far, these datasets were analyzed by RCCA or sparse partial least squares (sPLS) (11,12,16,31). In our case studies (see section 5), we re-analyzed the data with RCCA or sPLS too, evidencing gene/metabolite correlations for one half or one third of the physiological or clinical measurements (NutriBov, 3 out of 6 metabolites; NutriMous, 7/21; LiverTox, 7/14) and at best, half of the interesting gene changes (NutriBov, 151/293; NutriMous, 27/120; LiverTox, 1,032/3,116).…”