Abstract
BackgroundConstructing 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 ResultsIn 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.ConclusionsWe 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.
Highlights
The use of gene expression data to construct coexpression networks and perform network decomposition [1,2,3] and network analysis [4,5,6] has proven very useful in biological study
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
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
Summary
The use of gene expression data to construct coexpression networks and perform network decomposition [1,2,3] and network analysis [4,5,6] has proven very useful in biological study. Which methods are more efficient in performing coexpression analysis and constructing coexpression networks has not yet been reported Such an evaluation is challenging because (1) there is inadequate gene expression data from a specific tissue or cell type over a development stage, or under a specific treatment or condition; (2) genes explicitly involved in a developmental or a biological process are often unclear in higher plants and animals; and (3) we have limited prior knowledge (e.g. positive and negative genes) for comparing the efficiency of different gene association methods in discovering true functionally associated genes. Since different methods lead to structurally different coexpression networks and provide different information, selecting the optimal gene association method is critical
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