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Benefits and Challenges of Pre-clustered Network-Based Pathway Analysis
Miguel Castresana-Aguirre1, Dimitri Guala1, Erik L L Sonnhammer1
1Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Stockholm, Sweden.
Frontiers in Genetics
|May 27, 2022
Summary
Clustering gene sets before pathway analysis can improve sensitivity and biological insights. However, it may reduce specificity, so use this approach cautiously with sensitive enrichment methods.
Area of Science:
- Bioinformatics
- Computational Biology
- Systems Biology
Background:
- Gene set analysis typically uses pathway annotation.
- Existing methods overlook that gene sets often contain multiple pathways, diluting results.
- Pre-clustering gene sets aims to create more homogenous modules for improved analysis.
Purpose of the Study:
- To investigate if network-based pre-clustering of gene sets enhances pathway analysis.
- To evaluate different clustering algorithms (MCL, Infomap, MGclus) in conjunction with pathway analysis tools.
- To assess the impact of clustering on sensitivity and specificity in identifying biological pathways.
Main Methods:
- Gene sets were clustered using MCL, Infomap, and MGclus on the FunCoup network.
- Clustered gene sets were analyzed with pathway analysis methods: Gene Enrichment Analysis (GEA), BinoX, NEAT, and ANUBIX.
- Performance was benchmarked using the KEGG pathway database.
Main Results:
- Clustering generally increased the sensitivity of pathway analysis methods.
- Deeper biological insights were gained, but maintaining high specificity was challenging.
- ANUBIX showed a minor specificity loss post-clustering; BinoX and NEAT had unacceptable losses.
- GEA demonstrated low sensitivity both before and after clustering.
Conclusions:
- Network-based pre-clustering can improve pathway annotation performance and biological understanding.
- The benefits are contingent on the chosen pathway analysis method's false positive rate.
- Clustering is recommended only when using enrichment methods with inherently low false positive rates.
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