Short time-series microarray analysis: Methods and challenges
1 Department of Chemical Engineering and Material Science, Michigan State University, East Lansing, MI 48824, USA
2 Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
3 Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
4 Biomedical Engineering Department, Boston University, Boston, 02215, USA
BMC Systems Biology 2008, 2:58 doi:10.1186/1752-0509-2-58Published: 7 July 2008
The detection and analysis of steady-state gene expression has become routine. Time-series microarrays are of growing interest to systems biologists for deciphering the dynamic nature and complex regulation of biosystems. Most temporal microarray data only contain a limited number of time points, giving rise to short-time-series data, which imposes challenges for traditional methods of extracting meaningful information. To obtain useful information from the wealth of short-time series data requires addressing the problems that arise due to limited sampling. Current efforts have shown promise in improving the analysis of short time-series microarray data, although challenges remain. This commentary addresses recent advances in methods for short-time series analysis including simplification-based approaches and the integration of multi-source information. Nevertheless, further studies and development of computational methods are needed to provide practical solutions to fully exploit the potential of this data.