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

TLDR

High‑throughput technologies generate abundant transcriptomic, proteomic, and metabolomic data, and integrating these large‑scale datasets can uncover biological insights, yet existing methods largely identify small, univariate molecular signatures from single omics types. mixOmics is an R package designed for multivariate exploration, dimensionality reduction, and visualization of biological data sets. It implements a systems‑biology approach that statistically integrates multiple heterogeneous omics data sets simultaneously to probe inter‑dataset relationships. The package extends PLS models for discriminant analysis, multi‑omics or multi‑study integration, and molecular signature discovery, and demonstrates these integrative frameworks on available omics data.

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 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.