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About the Authors:
Florian Rohart
Roles Conceptualization, Formal analysis, Methodology, Software, Visualization, Writing - original draft, Writing - review & editing
Current address: Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
Affiliation: The University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland, Australia
ORCID http://orcid.org/0000-0002-9588-8000
Benoît Gautier
Roles Methodology, Software, Visualization
Affiliation: The University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland, Australia
Amrit Singh
Roles Investigation, Methodology, Visualization
Affiliations Prevention of Organ Failure (PROOF) Centre of Excellence, Vancouver, British Columbia, Canada, Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
Kim-Anh Lê Cao
Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Software, Supervision, Visualization, Writing - original draft, Writing - review & editing
* E-mail: [email protected]
Affiliations The University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland, Australia, Melbourne Integrative Genomics and School of Mathematics and Statistics, University of Melbourne, Melbourne, Victoria, Australia
ORCID http://orcid.org/0000-0003-3923-1116Abstract
The advent of high throughput technologies has led to a wealth of publicly available ‘omics data coming from different sources, such as transcriptomics, proteomics, metabolomics. Combining such large-scale biological data sets can lead to the discovery of important biological insights, provided that relevant information can be extracted in a holistic manner. Current statistical approaches have been focusing on identifying small subsets of molecules (a ‘molecular signature’) to explain or predict biological conditions, but mainly for a single type of ‘omics. In addition, commonly used methods are univariate and consider each biological feature independently. We introduce mixOmics, an R package dedicated to the multivariate analysis of biological data sets with a specific focus on data exploration, dimension reduction and visualisation. By adopting a systems biology approach, the toolkit provides a wide range of methods that statistically integrate several data sets at once to probe relationships between heterogeneous ‘omics data sets. Our recent methods extend Projection to Latent Structure (PLS) models for discriminant analysis, for data integration across multiple ‘omics data or across independent studies, and for the identification of molecular signatures. We illustrate our latest mixOmics integrative frameworks for the multivariate analyses of ‘omics data available from the package.
Citation: Rohart F, Gautier B, Singh A, Lê Cao K-A (2017) mixOmics: An R package for ‘omics feature...