Improving the sensitivity of sample clustering by leveraging gene co-expression networks in variable selection

BMC Bioinformatics

Yin Liu³  Peng Qiu²  F Anthony San Lucas³  Zixing Wang¹ 

[1]Department of Neurobiology and Anatomy, University of Texas Health Science Center at Houston, Houston, Texas, USA

[2]Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA

[3]University of Texas Graduate School of Biomedical Sciences, Houston, Texas, USA

关键词: Gene module discovery;    Sample clustering;    Gene co-expression network;    Variable selection;   

Others  :  818546
DOI  :  10.1186/1471-2105-15-153

 received in 2013-12-16, accepted in 2014-05-14,  发布年份 2014

【 摘 要 】

Background

Many variable selection techniques have been proposed for the clustering of gene expression data. While these methods tend to filter out irrelevant genes and identify informative genes that contribute to a clustering solution, they are based on criteria that do not consider the potential interactive influence among individual genes. Motivated by ensemble clustering, there is a strong interest in leveraging the structure of gene networks for gene selection, so that the relationship information between genes can be effectively utilized, while the selected genes are expected to preserve all the possible clustering structures in the data.

Results

We present a new filter method that uses the gene connectivity in the gene co-expression network as the evaluation criteria for variable selection. The gene connectivity measures the importance of the genes in term of their expression similarity with others in the co-expression network. The hard threshold and soft threshold transformations are employed to construct the gene co-expression networks. Both simulation studies and real data analysis have shown that the network based on soft thresholding is more effective in selecting relevant variables and provides better clustering results compared to the hard thresholding transformation and two other canonical filter methods for variable selection. Furthermore, a new module analysis approach is proposed to reveal the higher order organization of the gene space, where the genes of a module share significant topological similarity and are associated with a consensus partition of the sample space. We demonstrate that the identified modules can lead to biologically meaningful sample partitions that might be missed by other methods.

Conclusions

By leveraging the structure of gene co-expression network, first we propose a variable selection method that selects individual genes with top connectivity. Both simulation studies and real data application have demonstrated that our method has better performance in terms of the reliability of the selected genes and sample clustering results. In addition, we propose a module recovery method that can help discover novel sample partitions that might be hidden when performing clustering analyses using all available genes. The source code of our program is available at http://nba.uth.tmc.edu/homepage/liu/netVar/ webcite.

【 授权许可】

   
2014 Wang et al.; licensee BioMed Central Ltd.

【 图 表 】

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