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Evaluation of gene association methods for coexpression network construction and biological knowledge discovery
Sapna Kumari1, Jeff Nie, Huann-Sheng Chen
1Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA.
Plos One
|December 11, 2012
Summary
Evaluating gene association methods for coexpression networks reveals Spearman, Hoeffding, and Kendall excel at pathway gene identification. Theil-sen, Rank Theil-Sen, Spearman, and Weighted Rank methods are best for identifying coordinated transcription factors.
Area of Science:
- Bioinformatics
- Systems Biology
- Computational Biology
Background:
- Gene coexpression network construction is vital for biological discovery.
- Methods for gene association in network construction require thorough evaluation.
- Different association methods yield structurally distinct networks and information.
Purpose of the Study:
- To compare the true knowledge discovery rates of eight gene association methods.
- To evaluate method performance in associating pathway genes and constructing regulatory gene networks.
- To examine method behavior with varying microarray data properties and biological processes.
Main Methods:
- Comparison of eight gene association methods: Spearman, Weighted Rank Correlation, Kendall, Hoeffding's D, Theil-Sen, Rank Theil-Sen, Distance Covariance, and Pearson.
- Assessment of true knowledge discovery rates for pathway and regulatory gene associations.
- Analysis of method performance across different microarray data properties and biological contexts.
Main Results:
- Spearman, Hoeffding, and Kendall methods are effective for identifying coexpressed pathway genes.
- Theil-sen, Rank Theil-Sen, Spearman, and Weighted Rank methods perform well in identifying coordinated transcription factors.
- Pearson and Distance Covariance methods showed generally lower efficiency and distinct behaviors compared to others.
Conclusions:
- Method selection for gene association is critical and depends on the specific biological question.
- Spearman, Hoeffding, and Kendall are recommended for pathway gene coexpression analysis.
- Theil-sen, Rank Theil-Sen, Spearman, and Weighted Rank are suitable for identifying coordinated transcription factors.
- Pre-analysis is necessary to identify the optimal gene association method for specific data and biological processes.
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