Combined static and dynamic analysis for determining the quality of time-series expression profiles

Itamar Simon, Zahava Siegfried, Jason Ernst, Ziv Bar-Joseph*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


Expression profiling of time-series experiments is widely used to study biological systems. However, determining the quality of the resulting profiles remains a fundamental problem. Because of inadequate sampling rates, the effect of arrest-and-release methods and loss of synchronization, the measurements obtained from a series of time points may not accurately represent the underlying expression profiles. To solve this, we propose an approach that combines time-series and static (average) expression data analysis - for each gene, we determine whether its temporal expression profile can be reconciled with its static expression levels. We show that by combining synchronized and unsynchronized human cell cycle data, we can identify many cycling genes that are missed when using only time-series data. The algorithm also correctly distinguishes cycling genes from genes that specifically react to an environmental stimulus even if they share similar temporal expression profiles. Experimental validation of these results shows the utility of this analytical approach for determining the accuracy of gene expression patterns.

Original languageAmerican English
Pages (from-to)1503-1508
Number of pages6
JournalNature Biotechnology
Issue number12
StatePublished - Dec 2005

Bibliographical note

Funding Information:
We thank Tommi Jaakkola, Larry Wasserman, Naftali Kaminski and the anonymous reviewers for comments on earlier versions. Z.B.J. is partially funded by National Science Fund Foundation CAREER award 0448453 and by the Pennsylvania Department of Health.


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