Optimal timepoint sampling in high-throughput gene expression experiments

Rosa, Bruce A. and Zhang, Ji and Major, Ian T. and Qin, Wensheng and Chen, Jin (2012) Optimal timepoint sampling in high-throughput gene expression experiments. Bioinformatics, 28 (21). pp. 2773-2781. ISSN 1367-4803

Abstract

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.
Availability: OTS is available at www.msu.edu/jinchen/OTS.
Contact: wqin@lakeheadu.ca; jinchen@msu.edu
Supplementary information: Supplementary data are available at
Bioinformatics online.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to published version due to publisher copyright policy.
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 14 Mar 2013 11:54
Last Modified: 19 Aug 2014 01:05
Uncontrolled Keywords: sampling rates
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970101 Expanding Knowledge in the Mathematical Sciences
Identification Number or DOI: 10.1093/bioinformatics/bts511
URI: http://eprints.usq.edu.au/id/eprint/22593

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