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Title: Comparison of threshold selection methods for microarray gene co-expression matrices

Journal Article · · BMC Research Notes
 [1];  [2];  [3];  [4];  [2]
  1. Univ. of Tennessee, Knoxville, TN (United States). Genome Science and Technology Program; DOE/OSTI
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Systems Genetics Group. Biosciences Division
  3. Univ. of Tennessee, Knoxville, TN (United States). Dept. of Electrical Engineering and Computer Science
  4. Univ. of Tennessee, Knoxville, TN (United States). Dept. of Animal Science
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Background: Network and clustering analyses of microarray co-expression correlation data often require application of a threshold to discard small correlations, thus reducing computational demands and decreasing the number of uninformative correlations. This study investigated threshold selection in the context of combinatorial network analysis of transcriptome data. Findings: Six conceptually diverse methods - based on number of maximal cliques, correlation of control spots with expressed genes, top 1% of correlations, spectral graph clustering, Bonferroni correction of p-values, and statistical power - were used to estimate a correlation threshold for three time-series microarray datasets. The validity of thresholds was tested by comparison to thresholds derived from Gene Ontology information. Stability and reliability of the best methods were evaluated with block bootstrapping. Two threshold methods, number of maximal cliques and spectral graph, used information in the correlation matrix structure and performed well in terms of stability. Comparison to Gene Ontology found thresholds from number of maximal cliques extracted from a co-expression matrix were the most biologically valid. Approaches to improve both methods were suggested. Conclusion: Threshold selection approaches based on network structure of gene relationships gave thresholds with greater relevance to curated biological relationships than approaches based on statistical pair-wise relationships.

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division
Grant/Contract Number:
AC02-05CH11231
OSTI ID:
1629482
Journal Information:
BMC Research Notes, Journal Name: BMC Research Notes Journal Issue: 1 Vol. 2; ISSN 1756-0500
Publisher:
BioMed CentralCopyright Statement
Country of Publication:
United States
Language:
English

References (16)

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The Impact of Bootstrap Methods on Time Series Analysis journal May 2003
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A General Framework for Weighted Gene Co-Expression Network Analysis journal January 2005
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Coexpression Analysis of Human Genes Across Many Microarray Data Sets dataset January 2019

Cited By (13)

Gene Coexpression Network Comparison via Persistent Homology journal September 2018
Prospecting for Genes involved in transcriptional regulation of plant defenses, a bioinformatics approach journal January 2011
Discovery of metabolite biomarkers: flux analysis and reaction-reaction network approach journal October 2013
Using graph theory to analyze biological networks journal April 2011
A Null Model for Pearson Coexpression Networks journal June 2015
Differentially Expressed Genes in Major Depression Reside on the Periphery of Resilient Gene Coexpression Networks journal January 2011
Multi-Level Integration of Environmentally Perturbed Internal Phenotypes Reveals Key Points of Connectivity between Them journal June 2017
Scalable Combinatorial Tools for Health Disparities Research journal October 2014
A new method bridging graph theory and residue co-evolutionary networks for specificity determinant positions detection journal October 2018
Threshold selection for covariance estimation journal April 2019
Gene Coexpression Network Comparison via Persistent Homology journal September 2018
Using graph theory to analyze biological networks journal April 2011
Microarray meta-analysis to explore abiotic stress-specific gene expression patterns in Arabidopsis journal May 2017

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Related Subjects

59 BASIC BIOLOGICAL SCIENCES
Biological Relationship
Maximal Clique
Spectral Cluster
Spectral Graph
Threshold Estimate