Optimal timepoint sampling in high-throughput gene expression experiments

Bruce A. Rosa, Ji Zhang, Ian T. Major, Wensheng Qin, Jin Chen

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Motivation: Determining the best sampling rates (which maximize information yield and minimize cost) for time-series high-throughput gene expression experiments is a challenging optimization problem. Although existing approaches provide insight into the design of optimal sampling rates, our ability to utilize existing differential gene expression data to discover optimal timepoints is compelling. Results: We present a new data-integrative model, Optimal Timepoint Selection (OTS), to address the sampling rate problem. Three experiments were run on two different datasets in order to test the performance of OTS, including iterative-online and a top-up sampling approaches. In all of the experiments, OTS outperformed the best existing timepoint selection approaches, suggesting that it can optimize the distribution of a limited number of timepoints, potentially leading to better biological insights about the resulting gene expression patterns.

Original languageEnglish
Pages (from-to)2773-2781
Number of pages9
Issue number21
StatePublished - Nov 2012

Bibliographical note

Funding Information:
Funding: This project has been funded by the U.S. Department of Energy (Chemical Sciences, Geosciences and Biosciences Division, grant no. DE-FG02-91ER20021 to J.C. and Natural Sciences and Engineering Research Council of Canada Research Development Fund, Canada to W.Q.

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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