Dataset Information
IntegrOmics: an R package to unravel relationships between two omics datasets.
Ontology highlight
ABSTRACT: 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.auSupplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Le Cao KA
PROVIDER: S-EPMC2781751 | biostudies-literature | 2009 Nov
REPOSITORIES: biostudies-literature
Publications
integrOmics: an R package to unravel relationships between two omics datasets.
Bioinformatics (Oxford, England) 20090825 21
<h4>Motivation</h4>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 va ...[more]