Temporal ordering of omics and multiomic events inferred from time-series data

Temporal changes in omics events can now be routinely measured; however, current analysis methods are often inadequate, especially for multiomics experiments. We report a novel analysis method that can infer event ordering at better temporal resolution than the experiment, and integrates omic events...

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Bibliographic Details
Published inNPJ systems biology and applications Vol. 6; no. 1; p. 22
Main Authors Kaur, Sandeep, Peters, Timothy J, Yang, Pengyi, Luu, Laurence Don Wai, Vuong, Jenny, Krycer, James R, O'Donoghue, Seán I
Format Journal Article
LanguageEnglish
Published England Nature Publishing Group 16.07.2020
Nature Publishing Group UK
Subjects
Algorithms
Brief Communication
Computational Biology - methods
Computer Simulation
Data Interpretation, Statistical
Proteomics - methods
Software
Spatio-Temporal Analysis
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Summary:Temporal changes in omics events can now be routinely measured; however, current analysis methods are often inadequate, especially for multiomics experiments. We report a novel analysis method that can infer event ordering at better temporal resolution than the experiment, and integrates omic events into two concise visualizations (event maps and sparklines). Testing our method gave results well-correlated with prior knowledge and indicated it streamlines analysis of time-series data.
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ISSN:2056-7189
2056-7189
DOI:10.1038/s41540-020-0141-0