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A statistical network pre-processing method to improve relevance and significance of gene lists in microarray gene
Giuseppe Agapito1,2, Marianna Milano3,4, Mario Cannataro3,4
1Department of Law, Economics and Sociology Sciences, University Magna Græcia, 88100, Catanzaro, Italy. agapito@unicz.it.
BMC Bioinformatics
|September 27, 2022
Summary
This study introduces a network pre-processing method to improve pathway enrichment analysis (PEA) for differential expressed genes (DEGs) and single nucleotide polymorphisms (SNPs). The approach enhances statistical significance and reduces the number of identified pathways for better biological context.
Area of Science:
- Genomics
- Bioinformatics
- Systems Biology
Background:
- Microarrays enable large-scale studies of differential expressed genes (DEGs) and single nucleotide polymorphisms (SNPs).
- DEGs and SNPs lack biological context, limiting their interpretation.
- Pathway enrichment analysis (PEA) links genetic variations to biological pathways and functions.
Purpose of the Study:
- To enhance the relevance and statistical significance of PEA.
- To incorporate biological network topology into PEA for DEGs and SNPs.
- To improve the interpretation of microarray data by providing biological context.
Main Methods:
- Developed a statistical network pre-processing method.
- Mapped DEGs and SNPs onto a biological network.
- Integrated pathway topology information into PEA.
Main Results:
- Improved statistical significance of PEA (lower p-values for pathways).
- Reduced the number of enriched pathways, focusing on relevant ones.
- Network analysis identified fewer, more relevant DEGs.
Conclusions:
- The proposed method enhances PEA by selecting relevant DEGs.
- Network analysis improves the statistical significance of enriched pathways.
- This approach provides a more focused and biologically relevant interpretation of microarray data.
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