Bioinformatics Advance Access originally published online on May 6, 2004
Bioinformatics 2004 20(16):2493-2503; doi:10.1093/bioinformatics/bth283
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Bioinformatics vol. 20 issue 16 © Oxford University Press 2004; all rights reserved.
Review |
Analyzing time series gene expression data
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15217, USA
Received on December 15, 2003; revised on March 22, 2004; accepted on April 19, 2004
Advance Access Publication May 6, 2004
Motivation: Time series expression experiments are an increasingly popular method for studying a wide range of biological systems. However, when analyzing these experiments researchers face many new computational challenges. Algorithms that are specifically designed for time series experiments are required so that we can take advantage of their unique features (such as the ability to infer causality from the temporal response pattern) and address the unique problems they raise (e.g. handling the different non-uniform sampling rates).
Results: We present a comprehensive review of the current research in time series expression data analysis. We divide the computational challenges into four analysis levels: experimental design, data analysis, pattern recognition and networks. For each of these levels, we discuss computational and biological problems at that level and point out some of the methods that have been proposed to deal with these issues. Many open problems in all these levels are discussed. This review is intended to serve as both, a point of reference for experimental biologists looking for practical solutions for analyzing their data, and a starting point for computer scientists interested in working on the computational problems related to time series expression analysis.
Contact: zivbj{at}cs.cmu.edu
* To whom correspondence should be addressed.
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