integrOmics: an R package to unravel relationships between two omics datasets

By Kim-Anh Lê Cao, Ignacio González, Sébastien Déjean. Bioinform., 2009

421 citations recorded by the source corpus.

Motivation: With the availability of many ‘omics’ data, such as transcriptomics, proteomics or metabolomics, the integrative or joint analysis of multiple datasets from different technology platforms is becoming crucial to unravel the relationships between different biological functional levels. However, the development of such an analysis is a major computational and technical challenge as most approaches suffer from high data dimensionality. New methodologies need to be developed and validated. Results: integrOmics efficiently performs integrative analyses of two types of ‘omics’ variables that are measured on the same samples. It includes a regularized version of canonical correlation analysis to enlighten correlations between two datasets, and a sparse version of partial least squares (PLS) regression that includes simultaneous variable selection in both datasets. The usefulness of both approaches has been demonstrated previously and successfully applied in various integrative studies. Availability: integrOmics is freely available from http://CRAN.R-project.org/ or from the web site companion (http://math.univ-toulouse.fr/biostat) that provides full documentation and tutorials. Contact: k.lecao@uq.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/af91d424fbc6062befa9362fb4b812e57ee6708a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

A tutorial review: Metabolomics and partial least squares-discriminant analysis--a marriage of convenience or a shotgun wedding. (2015, methodology): “The increasing interest in PLS-DA, especially in the field of metabolomics, is mainly due to its availability in most of the common statistical software packages such as: R, S-Plus, SAS, SPSS, MATLAB [7–13].”
Canonical Correlation Analysis based on Hilbert-Schmidt Independence Criterion and Centered Kernel Target Alignment (2013, background): “To date, CCA has a wide range of applications including, for example, functional data analysis (Leurgans et al., 1993) and bioinformatics (Cao et al., 2009).”
Methods for the integration of multi-omics data: mathematical aspects (2016, methodology): “Conversely from the methods above, Integromics [3], MCIA [20] and the approach of Liu et al.”
From big data analysis to personalized medicine for all: challenges and opportunities (2015, methodology): “Both mixOmics and integrOmics are publically available R packages for utilizing sparse methods on omics data [77, 78].”