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

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 largescale 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 system 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,

Germ‐free and microbiota‐associated mice yield small intestinal epithelial organoids with equivalent and robust transcriptome/proteome expression phenotypes

“…As a starting point, we performed an integrative analysis of the transcriptome and proteome data sets, using the DIABLO framework of the CRAN package mixOmics (Rohart, Gautier, Singh, & Lê Cao, ). The correlation between up‐ or downregulated proteins and their corresponding transcripts was high when comparing the organoid group to the MEF and m‐IC c12 groups ( R 2 = .719) (Figure S5c).…”

Endocrine disruption of gene expression and microRNA profiles in hippocampus and hypothalamus of California mice: Association of gene expression changes with behavioural outcomes

“…To determine how the phenotypic and molecular changes relate to each other, we used mixOmics analysis with r = 0.85 69 . California mice offspring exposed to LD BPA and GEN tended to show the most pronounced molecular and phenotypic effects that often mirrored each other.…”

“…We used the mixOmics r package 69 to correlate the behavioural and metabolic outcomes, mRNA expression profiles in the hippocampus and hypothalamus, and miR expression profiles in the hippocampus and hypothalamus. We conducted sparse discriminant analysis with partial least square regression with the function ‘block.splsda’.…”

The omics era: what can nuclear magnetic resonance tell us on metabolomics?

“…Despite its relative youth, in comparison to genomics and proteomics, metabolomics is now firmly established as a functional methodology to understanding the molecular complexity of living organisms. Currently, metabolomics research is applied in several fields, from environmental science (6,27), pharmacology (7,13,39) to computer science (35,37) and medicine (25,32) as depicted in Figure 2.…”