integrOmics: an R package to unravel relationships between two omics datasets. 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.
Keywords for this software
References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Hadrien Lorenzo, Jérôme Saracco, Rodolphe Thiébaut: Supervised Learning for Multi-Block Incomplete Data (2019) arXiv
- Jain, Yashita; Ding, Shanshan; Qiu, Jing: Sliced inverse regression for integrative multi-omics data analysis (2019)
- Jalal K. Siddiqui, Elizabeth Baskin, Mingrui Liu, Carmen Z. Cantemir-Stone, Bofei Zhang, Russell Bonneville, Joseph P. McElroy, Kevin R. Coombes, Ewy A. Mathé: IntLIM: Integration using Linear Models of metabolomics and gene expression data (2018) arXiv
- Cao, Kim-Anh Lê; González, Ignacio; Déjean, Sébastien: Integromics: an R package to unravel relationships between two omics datasets (2009) ioport