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Comparison of threshold selection methods for microarray gene co-expression matrices.
Bhavesh R Borate1, Elissa J Chesler, Michael A Langston
1Department of Animal Science, University of Tennessee, Knoxville, Tennessee, USA. asaxton@utk.edu.
BMC Research Notes
|December 4, 2009
Summary
Selecting correlation thresholds for gene co-expression networks is crucial. Network structure-based methods, like maximal cliques, offer more biologically relevant thresholds than simple statistical approaches for transcriptome analysis.
Area of Science:
- Bioinformatics
- Computational Biology
- Systems Biology
Background:
- Network and clustering analyses of gene co-expression data require thresholding to reduce computational load and remove noise.
- Investigating optimal threshold selection is critical for accurate combinatorial network analysis of transcriptome data.
Purpose of the Study:
- To evaluate diverse methods for selecting correlation thresholds in gene co-expression network analysis.
- To compare the biological validity and stability of different thresholding approaches.
Main Methods:
- Six distinct methods were employed to estimate correlation thresholds: maximal cliques, control spot correlations, top 1% correlations, spectral graph clustering, Bonferroni correction, and statistical power.
- Threshold validity was assessed against Gene Ontology (GO) information.
- Method stability and reliability were evaluated using block bootstrapping.
Main Results:
- Maximal clique and spectral graph methods, utilizing correlation matrix structure, demonstrated good stability.
- Thresholds derived from maximal cliques in co-expression matrices showed the highest biological validity when compared to GO data.
- Improvements for both leading methods were proposed.
Conclusions:
- Network structure-based thresholding methods provide greater biological relevance than statistical pair-wise relationship methods for gene networks.
- Maximal clique analysis is a promising approach for biologically meaningful threshold selection in transcriptome-wide co-expression studies.
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