Merging multiple omics datasets in silico: Statistical analyses and data interpretation

Research output: Chapter in Book/Report/Conference proceedingChapter

12 Citations (Scopus)

Abstract

By the combinations of high-throughput analytical technologies in the fields of transcriptomics, proteomics, and metabolomics, we are now able to gain comprehensive and quantitative snapshots of the intracellular processes. Dynamic intracellular activities and their regulations can be elucidated by systematic observation of these multi-omics data. On the other hand, careful statistical analysis is necessary for such integration, since each of the omics layers as well as the specific analytical methodologies harbor different levels of noise and variations. Moreover, interpretation of such multitude of data requires an intuitive pathway context. Here we describe such statistical methods for the integration and comparison of multiomics data, as well as the computational methods for pathway reconstruction, ID conversion, mapping, and visualization that play key roles for the efficient study of multi-omics information.

Original languageEnglish
Title of host publicationSystems Metabolic Engineering
Subtitle of host publicationMethods and Protocols
PublisherHumana Press Inc.
Pages459-470
Number of pages12
ISBN (Print)9781627032988
DOIs
Publication statusPublished - 2013

Publication series

NameMethods in Molecular Biology
Volume985
ISSN (Print)1064-3745

Keywords

  • Data normalization
  • Metabolome
  • Multi-omics analysis
  • Pathway reconstruction
  • Pathway visualization
  • Proteome
  • Transcriptome

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics

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    Arakawa, K., & Tomita, M. (2013). Merging multiple omics datasets in silico: Statistical analyses and data interpretation. In Systems Metabolic Engineering: Methods and Protocols (pp. 459-470). (Methods in Molecular Biology; Vol. 985). Humana Press Inc.. https://doi.org/10.1007/978-1-62703-299-5_23