Document Type
Article
Publication Date
2013
College/Unit
Davis College of Agriculture, Natural Resources and Design
Department/Program/Center
Division of Animal and Nutritional Sciences
Abstract
Background
Constructing coexpression networks and performing network analysis using large-scale gene expression data sets is an effective way to uncover new biological knowledge; however, the methods used for gene association in constructing these coexpression networks have not been thoroughly evaluated. Since different methods lead to structurally different coexpression networks and provide different information, selecting the optimal gene association method is critical.
Methods and Results
In this study, we compared eight gene association methods – Spearman rank correlation, Weighted Rank Correlation, Kendall, Hoeffding's D measure, Theil-Sen, Rank Theil-Sen, Distance Covariance, and Pearson – and focused on their true knowledge discovery rates in associating pathway genes and construction coordination networks of regulatory genes. We also examined the behaviors of different methods to microarray data with different properties, and whether the biological processes affect the efficiency of different methods.
Conclusions
We found that the Spearman, Hoeffding and Kendall methods are effective in identifying coexpressed pathway genes, whereas the Theil-sen, Rank Theil-Sen, Spearman, and Weighted Rank methods perform well in identifying coordinated transcription factors that control the same biological processes and traits. Surprisingly, the widely used Pearson method is generally less efficient, and so is the Distance Covariance method that can find gene pairs of multiple relationships. Some analyses we did clearly show Pearson and Distance Covariance methods have distinct behaviors as compared to all other six methods. The efficiencies of different methods vary with the data properties to some degree and are largely contingent upon the biological processes, which necessitates the pre-analysis to identify the best performing method for gene association and coexpression network construction.
Digital Commons Citation
Kumari, Sapna; Nie, Jeff; Chen, Huan-Sheng; Ma, Hao; Stewart, Ron; Li, Xiang; Lu, Meng-Zhu; Taylor, William M.; and Wei, Hairong, "Evaluation of Gene Association Methods for Coexpression Network Construction and Biological Knowledge Discovery" (2013). Faculty & Staff Scholarship. 2652.
https://researchrepository.wvu.edu/faculty_publications/2652
Source Citation
Kumari S, Nie J, Chen H-S, Ma H, Stewart R, Li X, et al. (2012) Evaluation of Gene Association Methods for Coexpression Network Construction and Biological Knowledge Discovery. PLoS ONE 7(11): e50411. https://doi.org/10.1371/journal.pone.0050411
Comments
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.