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

By Florian Rohart, Benoît Gautier, Amrit Singh, Kim-Anh Lê Cao. bioRxiv, 2017

3,265 citations recorded by the source corpus.

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 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, 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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/13653c06f1cecd84cc692f6195c33cc9b849139c

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In Molecular Biology

Citations in the corpus

Orchestrating single-cell analysis with Bioconductor (2019, methodology): “Finally, several data integration methods developed for bulk assays are available in themixOmics [79] package and have shown good performance on single-cell data [80].”
Root functional traits explain root exudation rate and composition across a range of grassland species (2021, methodology): “Supervised sparse partial least-square discriminant analyses (sPLS-DAs) were conducted to obtain the metabolites that provide the greatest source of discrimination between functional groups using the mixomics package (Rohart et al., 2017).”
Grammar of protein domain architectures (2019, methodology): “We used the splsda method from the R package mixOmics (78) and performed feature selection after variable tuning.”
Multi-omics Data Integration, Interpretation, and Its Application (2020, background): “…to identify multi-omics signatures (putative biomarkers) that could distinguish the breast cancer subtypes, namely, Basal, HER2, and Luminal A. 51 Thus, mix-Omics can address both disease subtyping and biomarker prediction. moCluster. moCluster uses multi-table multivariate analysis approach…”
Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community (2019, background): “Name to ID conversion Open [41] BiofOmics • Transcriptomics • Proteomics • Metabolomics Biofilm - Experiment library - Data depository Open [113] BioCyc/MetaCyc • Genomics • Proteomics • Metabolomics Unspecified - Online encyclopedia of metabolism - Predicted metabolic pathways in sequenced genomes ”
DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays (2019, background): “Note that the design matrix is not restricted to 0 and 1 values only and a compromise between correlation and discrimination can also be modeled as described in Rohart et al. (2017).”
The Optimal Ridge Penalty for Real-world High-dimensional Data Can Be Zero or Negative due to the Implicit Ridge Regularization (2020, methodology): “We used the liver.toxicity dataset (Bushel et al., 2007) from the R package mixOmics (Rohart et al., 2017) as a motivational example to demonstrate the phenomenon.”
Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources (2020, methodology): “Examples of opensource and user-friendly workflows include MetaboAnalyst [2], XCMS [3], mixomics [4], miodin [5], and many others reviewed elsewhere [1].”
Machine Learning and Integrative Analysis of Biomedical Big Data (2019, methodology): “In [69], mixOmics R package incorporated L1-penalized embedded FS into various supervised omics-integration methods to enable molecular signature extraction.”