Abstract
BackgroundDAPfinder and DAPview are novel BRB-ArrayTools plug-ins to construct gene coexpression networks and identify significant differences in pairwise gene-gene coexpression between two phenotypes.ResultsEach significant difference in gene-gene association represents a Differentially Associated Pair (DAP). Our tools include several choices of filtering methods, gene-gene association metrics, statistical testing methods and multiple comparison adjustments. Network results are easily displayed in Cytoscape. Analyses of glioma experiments and microarray simulations demonstrate the utility of these tools.ConclusionsDAPfinder is a new friendly-user tool for reconstruction and comparison of biological networks.
Highlights
DAPfinder and DAPview are novel Brasil. 5Biometric Research Branch (BRB)-ArrayTools plug-ins to construct gene coexpression networks and identify significant differences in pairwise gene-gene coexpression between two phenotypes
* Correspondence: huyeny@niaid.nih.gov; anemorgun@hotmail.com 1Bioinformatics and Computational Biosciences Branch (BCBB), Office of Cyber Infrastructure and Computational Biology (OCICB), National Institute of Allergy and Infectious Disease (NIAID), National Institutes if Health (NIH), Bethesda, Maryland, USA 6“Ghost Lab”, T-Cell Tolerance and Memory Section (TCTMS), Laboratory of Cellular and Molecular Immunology (LCMI), National Institute of Allergy and Infectious Disease (NIAID), National Institutes if Health (NIH), Bethesda, Maryland, USA Full list of author information is available at the end of the article (CLR) [5], maximum relevancy (MR) [6,7] and other methods often provide helpful models of coexpression and coregulation, but the networks are based on data from a single phenotype and are not compared using statistical tests
The tool can be used with expression data from RNA-Seq reads or it can analyze complex quantitative biological data like comparative genomic hybridization (CGH), metabolome, microbiome and proteome data
Summary
Evaluation of DAPfinder with Simulated Microarray Data The efficacy of the DAPfinder procedures was evaluated using simulated microarray data with known gene-gene correlations to ensure its statistical methods can detect known differences in gene-gene association with high levels of statistical power and low levels of false positives. Results from the simulations show that sensitivity (i.e. statistical power) and specificity (i.e. control over false positives) increase as sample sizes (n) or differences in correlation (delta = Δr = ri - rj) increase (Figure 2) when all other experimental conditions are held constant (Additional file 1, supplementary information). Correlations that change direction between glioma types typically show strong positive or negative correlations consistent with regulation in ODG, while having zero correlation in GBM This suggests that evolution of the tumor may lead to the loss of regulatory relationships in the de-differentiating tissue. This analysis does not allow for definitive biological conclusions, it finds both previously established genes essential for tumorgenesis as wells as points to a new previously unexplored area of transcriptional regulation of gliomas These results support the idea that estimating the structure and changes in the co-expression gene networks can be a useful approach for understanding the disease process
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