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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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A mixture model to detect edges in sparse co-expression graphs with an application for comparing breast cancer
1Department of Statistics, University of Connecticut, Storrs, CT, United States of America.
Plos One
|February 11, 2021
Summary
We developed a novel method to reconstruct gene networks from gene expression data. This approach accurately identifies gene interactions and reveals distinct molecular characteristics across cancer subtypes.
Area of Science:
- Genomics
- Bioinformatics
- Systems Biology
Background:
- Gene co-expression analysis is crucial for understanding gene function and regulatory networks.
- Existing methods often make assumptions about network structure, limiting their applicability.
- Accurate reconstruction of gene networks is essential for identifying disease-specific pathways.
Purpose of the Study:
- To develop a robust statistical method for inferring gene network structure from co-expression data.
- To evaluate the performance of the proposed method against existing approaches.
- To apply the method to identify cancer subtype-specific gene networks and pathways.
Main Methods:
- Utilized normalized Pearson's correlation coefficients to represent gene pair co-expression.
- Developed a three-component mixture model (L2 N mixture model) to distinguish true edges from null edges.
- Implemented the method in an R package named 'edgefinder' for practical application.
Main Results:
- The L2 N mixture model demonstrated superior performance in edge detection power and false discovery rate control compared to other methods.
- Applied to a large dataset, the method successfully inferred gene networks for different cancer subtypes.
- Identified thirteen pathways enriched in cancer subtypes but not in normal tissue, including those related to autoimmune diseases and graft rejection.
- Discovered unique network characteristics for breast cancer subtypes, such as a highly connected cluster in Luminal A enriched in human diseases and a distinct cluster in Her2 enriched in drug metabolism pathways.
Conclusions:
- The developed method provides a powerful and assumption-free approach for gene network reconstruction.
- The inferred subtype-specific networks offer novel insights into cancer biology and potential therapeutic targets.
- The findings highlight the utility of network analysis in uncovering disease mechanisms and patient stratification.
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