Computational Frameworks for Pathway Analysis: Evaluating Over-representation, Topology-aware Scoring, and Crosstalk Modeling

Document Type : Research Article

Authors

Department of Genetics, Faculty of Basic Sciences, Shahrekord University, Shahrekord, Iran

10.22080/jgr.2026.31513.1460

Abstract

Pathway analysis translates high-throughput molecular measurements into interpretable hypotheses about coordinated biological processes, but its conclusions depend strongly on the statistical and biological assumptions embedded in each method. This narrative review provides a practical, tool-focused update rather than a proposal of a new formal taxonomy. It revisits threshold-dependent over-representation analysis, functional class scoring, topology-aware scoring, and emerging crosstalk-aware approaches, with emphasis on when each is defensible for experimental interpretation. Threshold-dependent enrichment is fast and transparent, but relies on an arbitrary significance cutoff and treats pathway members as exchangeable counts. Functional class scoring retains ranked or continuous measurements and can partially accommodate coordinated intra-pathway behavior, yet most implementations still evaluate pathways independently and do not model inter-pathway dependence. Topology-aware methods add mechanistic structure by incorporating directionality, edge sign or weight, node centrality, or perturbation propagation; their interpretability is constrained by incomplete, static, and context-agnostic pathway maps. Because signaling pathways share components and influence one another through regulatory edges, crosstalk can produce both false pathway prioritization and biologically meaningful inter-pathway signals. We therefore compare four representative crosstalk or pathway-deregulation tools Pathifier, PathTracer, PAGI, and PathwAX/PathwAX II across input requirements, outputs, crosstalk definitions, accessibility, computational burden, strengths, and limitations. Recent multi-omics and single-cell methods are also considered because pathway inference increasingly requires direction-aware, sample-level, and reproducible integration across molecular layers. No method is universally optimal: selection should be driven by data type, biological question, availability of curated topology, tolerance for model assumptions, and intended interpretation. Transparent reporting of input gene universes, pathway databases, software versions, parameter settings, and multiple-testing procedures remains essential for reproducible pathway-level inference.

Keywords

Main Subjects

References

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Journal of Genetic Resources
Volume 12, Issue 1
Issue in Progress
2026
Pages 20-30

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