title: Merging multiple omics datasets in silico: statistical analyses and data interpretation.
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.
authors: Institute for Advanced Biosciences, Keio University, Fujisawa, Kanagawa, Japan. gaou@sfc.keio.ac.jp
authors: Kazuharu
authors: K
authors: Arakawa
authors: Masaru
authors: M
authors: Tomita
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D004926:Escherichia coli / Q000235:genetics / Q000378:metabolism; D029968:Escherichia coli Proteins / Q000235:genetics / Q000378:metabolism; D015870:Gene Expression; D053858:Metabolic Networks and Pathways; D055442:Metabolome; D055432:Metabolomics; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis; D020543:Proteome / Q000235:genetics / Q000378:metabolism*; D059467:Transcriptome
chemical_list: D029968:Escherichia coli Proteins; D020543:Proteome