Functional network community detection can disaggregate and filter multiple underlying pathways in enrichment...

Area of Science:

• Bioinformatics
• Systems Biology
• Computational Biology

Background:

• Pathway enrichment analysis is crucial for interpreting gene lists from expression studies.
• Standard methods can yield numerous false positives when multiple biological pathways are involved, hindering biological insight.
• Complex diseases often involve multiple pathways, posing challenges for traditional enrichment analyses.

Purpose of the Study:

• To develop and evaluate a novel approach combining community detection with pathway enrichment analysis.
• To reduce false positive pathway identifications in gene expression datasets.
• To improve the interpretation of complex gene sets, particularly those related to disease subtypes.

Main Methods:

• Developed a method integrating community detection algorithms with pathway enrichment analysis.
• Utilized simulation studies to assess the performance of the new approach compared to standard methods.
• Applied the approach to gene expression data from high-grade serous ovarian cancer subtypes.

Main Results:

• The combined approach significantly reduced false positive pathway identifications in simulation studies.
• A substantial decrease in false positives was achieved with only a minor reduction in statistical power.
• Results did not support the hypothesis of multiple underlying communities for high-grade serous ovarian cancer subtypes.

Conclusions:

• Integrating community detection prior to pathway enrichment analysis enhances the interpretability of complex gene sets.
• This strategy effectively mitigates false positive findings, leading to more reliable biological insights.
• The method offers a valuable tool for researchers analyzing gene expression data from complex biological systems.