Merging multiple omics datasets in silico

statistical analyses and data interpretation.

Research output: Contribution to journalArticle

11 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 multi-omics 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
Pages (from-to)459-470
Number of pages12
JournalMethods in molecular biology (Clifton, N.J.)
Volume985
DOIs
Publication statusPublished - 2013

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Statistical Data Interpretation
Computer Simulation
Metabolomics
Proteomics
Noise
Observation
Technology
Datasets

ASJC Scopus subject areas

  • Medicine(all)

Cite this

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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 multi-omics 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.",
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