Correlation Engine 2.0
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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.


Kazuharu Arakawa, Masaru Tomita. Merging multiple omics datasets in silico: statistical analyses and data interpretation. Methods in molecular biology (Clifton, N.J.). 2013;985:459-70

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PMID: 23417818

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