Transcriptomic and metabolomic data integration

Rachel Cavill*, Danyel Jennen, Jos Kleinjans, Jacco Briedé

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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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.
Original languageEnglish
Pages (from-to)891-901
JournalBriefings in Bioinformatics
Volume17
Issue number5
DOIs
Publication statusPublished - Sep 2016

Keywords

  • transcriptomics
  • metabolomics
  • data integration
  • study design

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