Transcriptomic and metabolomic data integration
- 14 October 2015
- journal article
- review article
- Published by Oxford University Press (OUP) in Briefings in Bioinformatics
- Vol. 17 (5), 891-901
- https://doi.org/10.1093/bib/bbv090
Abstract
Many studies now produce parallel data sets from different omics technologies; however, the task of interpreting the acquired data in an integrated fashion is not trivial. This review covers those methods that have been used over the past decade to statistically integrate and interpret metabolomics and transcriptomic data sets. It defines four categories of approaches, correlation-based integration, concatenation-based integration, multivariate-based integration and pathway-based integration, into which all existing statistical methods fit. It also explores the choices in study design for generating samples for analysis by these omics technologies and the impact that these technical decisions have on the subsequent data analysis options.Keywords
This publication has 64 references indexed in Scilit:
- Consensus-Phenotype Integration of Transcriptomic and Metabolomic Data Implies a Role for Metabolism in the Chemosensitivity of Tumour CellsPLoS Computational Biology, 2011
- Messy biology and the origins of evolutionary innovationsNature Chemical Biology, 2010
- Tackling the widespread and critical impact of batch effects in high-throughput dataNature Reviews Genetics, 2010
- Microarrays in the clinicNature Biotechnology, 2010
- The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive modelsNature Biotechnology, 2010
- The metabolomics standards initiative (MSI)Metabolomics, 2007
- The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurementsNature Biotechnology, 2006
- Discordant Protein and mRNA Expression in Lung AdenocarcinomasMolecular & Cellular Proteomics, 2002
- Minimum information about a microarray experiment (MIAME)—toward standards for microarray dataNature Genetics, 2001
- Correlation between Protein and mRNA Abundance in YeastMolecular and Cellular Biology, 1999