Interpolation based consensus clustering for gene expression time series

BMC Bioinformatics

Tai-Yu Chiu¹  Ting-Chieh Hsu¹  Chia-Cheng Yen¹  Jia-Shung Wang¹ 

[1] Department of Computer Science, National Tsing Hua University, No. 101, Section 2, Kuang-Fu Road, HsinChu 30013, Taiwan

关键词: Interpolation;    Affinity propagation;    Consensus clustering;    Gene expression;    Microarray data analyses;   

Others  :  1177483
DOI  :  10.1186/s12859-015-0541-0

 received in 2013-09-18, accepted in 2015-02-27,  发布年份 2015

【 摘 要 】

Background

Unsupervised analyses such as clustering are the essential tools required to interpret time-series expression data from microarrays. Several clustering algorithms have been developed to analyze gene expression data. Early methods such as k-means, hierarchical clustering, and self-organizing maps are popular for their simplicity. However, because of noise and uncertainty of measurement, these common algorithms have low accuracy. Moreover, because gene expression is a temporal process, the relationship between successive time points should be considered in the analyses. In addition, biological processes are generally continuous; therefore, the datasets collected from time series experiments are often found to have an insufficient number of data points and, as a result, compensation for missing data can also be an issue.

Results

An affinity propagation-based clustering algorithm for time-series gene expression data is proposed. The algorithm explores the relationship between genes using a sliding-window mechanism to extract a large number of features. In addition, the time-course datasets are resampled with spline interpolation to predict the unobserved values. Finally, a consensus process is applied to enhance the robustness of the method. Some real gene expression datasets were analyzed to demonstrate the accuracy and efficiency of the algorithm.

Conclusion

The proposed algorithm has benefitted from the use of cubic B-splines interpolation, sliding-window, affinity propagation, gene relativity graph, and a consensus process, and, as a result, provides both appropriate and effective clustering of time-series gene expression data. The proposed method was tested with gene expression data from the Yeast galactose dataset, the Yeast cell-cycle dataset (Y5), and the Yeast sporulation dataset, and the results illustrated the relationships between the expressed genes, which may give some insights into the biological processes involved.

【 授权许可】

   
2015 Chiu et al.; licensee BioMed Central.

【 图 表 】

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