mixOmics: An R package for ‘omics feature selection and multiple data integration
Translational Research Institute · The University of Queensland · +4 more institutions
Abstract
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…
Citation impact
- FWCI
- 80.85
- Percentile
- 100%
- References
- 48
Authors
4- FRFlorian Rohart
Translational Research Institute, The University of Queensland
- BGBenoît Gautier
Translational Research Institute, The University of Queensland
- ASAmrit Singh
University of British Columbia, Prevention of Organ Failure
- KLKim‐Anh Lê CaoCorresponding
Translational Research Institute, The University of Queensland, The University of Melbourne, Melbourne Genomics Health Alliance
Topics & keywords
- Univariate
- Omics
- Computer science
- Feature selection
- Identification (biology)
- Data integration
- Biological data
- Data mining
- Reduced inequalities