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

Kaur, S; Peters, TJ; Yang, P; Luu, LDW; Vuong, J; Krycer, JR; O'Donoghue, SI

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

Kaur, S Peters, TJ Yang, P Luu, LDW Vuong, J Krycer, JR O'Donoghue, SI

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Publisher:: NATURE PUBLISHING GROUP
Publication Type:: Journal Article
Citation:: NPJ Syst Biol Appl, 2020, 6, (1), pp. 22
Issue Date:: 2020-07-16

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Field	Value	Language
dc.contributor.author	Kaur, S
dc.contributor.author	Peters, TJ
dc.contributor.author	Yang, P
dc.contributor.author	Luu, LDW
dc.contributor.author	Vuong, J
dc.contributor.author	Krycer, JR
dc.contributor.author	O'Donoghue, SI
dc.date.accessioned	2022-10-31T06:22:24Z
dc.date.available	2020-06-18
dc.date.available	2022-10-31T06:22:24Z
dc.date.issued	2020-07-16
dc.identifier.citation	NPJ Syst Biol Appl, 2020, 6, (1), pp. 22
dc.identifier.issn	2056-7189
dc.identifier.issn	2056-7189
dc.identifier.uri	http://hdl.handle.net/10453/163077
dc.description.abstract	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.
dc.format	Electronic
dc.language	eng
dc.publisher	NATURE PUBLISHING GROUP
dc.relation.ispartof	NPJ Syst Biol Appl
dc.relation.isbasedon	10.1038/s41540-020-0141-0
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.mesh	Algorithms
dc.subject.mesh	Computational Biology
dc.subject.mesh	Computer Simulation
dc.subject.mesh	Data Interpretation, Statistical
dc.subject.mesh	Proteomics
dc.subject.mesh	Software
dc.subject.mesh	Spatio-Temporal Analysis
dc.subject.mesh	Data Interpretation, Statistical
dc.subject.mesh	Proteomics
dc.subject.mesh	Computational Biology
dc.subject.mesh	Algorithms
dc.subject.mesh	Computer Simulation
dc.subject.mesh	Software
dc.subject.mesh	Spatio-Temporal Analysis
dc.title	Temporal ordering of omics and multiomic events inferred from time-series data.
dc.type	Journal Article
utslib.citation.volume	6
utslib.location.activity	England
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
utslib.copyright.status	open_access	*
dc.date.updated	2022-10-31T06:22:17Z
pubs.issue	1
pubs.publication-status	Published online
pubs.volume	6
utslib.citation.issue	1

Abstract:

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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http://hdl.handle.net/10453/163077