Gene set enrichment analysis for multiple continuous phenotypes

BMC Bioinformatics

Xiaoming Wang²  Saumyadipta Pyne¹  Irina Dinu² 

[1] Public Health Foundation of India, Delhi, India

[2] School of Public Health, University of Alberta, Edmonton, AB T6G 1C9, Canada

关键词: Gene-set analysis;    Nonlinear combination test;    Linear combination test;    Gene expression;    DNA microarrays;   

Others  :  1087532
DOI  :  10.1186/1471-2105-15-260

 received in 2014-03-21, accepted in 2014-07-25,  发布年份 2014

【 摘 要 】

Background

Gene set analysis (GSA) methods test the association of sets of genes with phenotypes in gene expression microarray studies. While GSA methods on a single binary or categorical phenotype abounds, little attention has been paid to the case of a continuous phenotype, and there is no method to accommodate correlated multiple continuous phenotypes.

Result

We propose here an extension of the linear combination test (LCT) to its new version for multiple continuous phenotypes, incorporating correlations among gene expressions of functionally related gene sets, as well as correlations among multiple phenotypes. Further, we extend our new method to its nonlinear version, referred as nonlinear combination test (NLCT), to test potential nonlinear association of gene sets with multiple phenotypes. Simulation study and a real microarray example demonstrate the practical aspects of the proposed methods.

Conclusion

The proposed approaches are effective in controlling type I errors and powerful in testing associations between gene-sets and multiple continuous phenotypes. They are both computationally effective. Naively (univariately) analyzing a group of multiple correlated phenotypes could be dangerous. R-codes to perform LCT and NLCT for multiple continuous phenotypes are available at http://www.ualberta.ca/~yyasui/homepage.html webcite.

【 授权许可】

   
2014 Wang et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

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