Journal Article

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

Kim-Anh Lê Cao, Ignacio González and Sébastien Déjean

in Bioinformatics

Volume 25, issue 21, pages 2855-2856
Published in print November 2009 | ISSN: 1367-4803
Published online August 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp515

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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.

Journal Article.  1224 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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