Abstract
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.
Highlights
Microarray technology has enabled the interrogation of gene expression data in a global and parallel fashion, and has become the most popular platform in the era of systems biology [1]
Increasing efforts are focused on deciphering the multidimensional dynamic behaviours of complex biological systems, including complex regulation schemes, such as the crosstalk between multiple pathways [3,8,9], and interactions among more than 1000 genes in plant cell wall biogenesis, developmental biology, and human diseases [10,11,12,13,14]
Future studies could combine both of these strategies to simultaneously decrease the complexity of continuous time-series representations, yet minimize the information loss with the simplification-based approaches by increasing the information content of the data
Summary
Microarray technology has enabled the interrogation of gene expression data in a global and parallel fashion, and has become the most popular platform in the era of systems biology [1]. Simplification strategies reduce time-series data from continuous to discrete representations prior to analysis These strategies usually transform the raw temporal profiles into a set of symbols [29,30,33] or nominal values [31,34] that are used to categorize qualitatively the gene expression data into different states or trends, that is, in terms of phases (early or late), magnitudes (high or low), or directions (up- or down-regulation). Http://www.biomedcentral.com/1752-0509/2/58 scale or different levels of information [40,41,42], or additional time-series datasets from other sources [31,32], is another approach to address the limited sampling and to improve the computational analysis and interpretation of short time-series microarray data. Integrating gene expression data from various sources is readily achievable with public databases, such as GEO [56] and ArrayExpress [57], where the quality of the data is controlled with the MIAME score
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