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SEMgsa: topology-based pathway enrichment analysis with structural equation models.

Mario Grassi1, Barbara Tarantino2

  • 1Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.

BMC Bioinformatics
|August 17, 2022
PubMed
Summary

SEMgsa, a novel topology-based algorithm, enhances pathway enrichment analysis for gene expression data. It outperforms existing methods by integrating pathway topology and perturbation statistics, offering high statistical power and sensitivity for disease-specific pathways.

Keywords:
Pathway enrichment analysisPathway topologyPowerPrioritizationSEMSEMgsaSensitivityType I error

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Pathway enrichment analysis is crucial for interpreting high-throughput experimental data.
  • Topology-informed methods offer superior performance over simple pathway membership approaches.
  • Existing tools often lack a balance between statistical power and user-friendliness.

Purpose of the Study:

  • To introduce SEMgsa, a novel topology-based algorithm for pathway enrichment analysis.
  • To develop a user-friendly framework that integrates statistical power and biological insights.
  • To evaluate SEMgsa's performance against existing methods using real-world and simulated data.

Main Methods:

  • SEMgsa utilizes structural equation models (SEM) and node-specific group effect estimates.
  • It statistically controls for biological relationships among genes within pathways.
  • Performance was assessed using COVID-19 RNA-seq and frontotemporal dementia DNA methylation datasets, alongside simulated data.

Main Results:

  • SEMgsa demonstrated high sensitivity and statistical power in identifying disease-specific pathways.
  • The algorithm outperformed existing software tools in both real-world and simulated datasets.
  • Simulation results confirmed SEMgsa's superior performance in terms of type I error and statistical power.

Conclusions:

  • SEMgsa is a powerful and novel method for gene expression enrichment analysis.
  • It effectively leverages topological information and pathway perturbation statistics.
  • The SEMgsa algorithm is available as the R package SEMgraph.