Journal article

mixOmics: An R package for 'omics feature selection and multiple data integration

Florian Rohart, Benoit Gautier, Amrit Singh, Kim-Anh Le Cao

PLOS COMPUTATIONAL BIOLOGY | PUBLIC LIBRARY SCIENCE | Published : 2017

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 packag..

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University of Melbourne Researchers

Grants

Awarded by National Health and Medical Research Council (NHMRC) Career Development fellowship


Funding Acknowledgements

FR was supported, in part, by the Australian Cancer Research Foundation (ACRF) for the Diamantina Individualised Oncology Care Centre at The University of Queensland Diamantina Institute. KALC was supported, in part, by the National Health and Medical Research Council (NHMRC) Career Development fellowship (APP1087415). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.