PSF toolkit: an R package for pathway curation and topology-aware analysis

Siras Hakobyan; Ani Stepanyan; Lilit Nersisyan; Hans Binder; Arsen Arakelyan

doi:10.3389/fgene.2023.1264656

PSF toolkit: an R package for pathway curation and topology-aware analysis

Front Genet. 2023 Aug 23:14:1264656. doi: 10.3389/fgene.2023.1264656. eCollection 2023.

Authors

Siras Hakobyan^{1

2}, Ani Stepanyan³, Lilit Nersisyan², Hans Binder^{3

4}, Arsen Arakelyan^{1

5}

Affiliations

¹ Bioinformatics Group, Institute of Molecular Biology, Armenian National Academy of Sciences, Yerevan, Armenia.
² Armenian Bioinformatics Institute (ABI), Yerevan, Armenia.
³ Armenian Bioinformatics Institute, Yerevan, Armenia.
⁴ Interdisciplinary Centre for Bioinformatics, University of Leipzig, Leipzig, Germany.
⁵ Russian-Armenian University, Yerevan, Armenia.

Abstract

Most high throughput genomic data analysis pipelines currently rely on over-representation or gene set enrichment analysis (ORA/GSEA) approaches for functional analysis. In contrast, topology-based pathway analysis methods, which offer a more biologically informed perspective by incorporating interaction and topology information, have remained underutilized and inaccessible due to various limiting factors. These methods heavily rely on the quality of pathway topologies and often utilize predefined topologies from databases without assessing their correctness. To address these issues and make topology-aware pathway analysis more accessible and flexible, we introduce the PSF (Pathway Signal Flow) toolkit R package. Our toolkit integrates pathway curation and topology-based analysis, providing interactive and command-line tools that facilitate pathway importation, correction, and modification from diverse sources. This enables users to perform topology-based pathway signal flow analysis in both interactive and command-line modes. To showcase the toolkit's usability, we curated 36 KEGG signaling pathways and conducted several use-case studies, comparing our method with ORA and the topology-based signaling pathway impact analysis (SPIA) method. The results demonstrate that the algorithm can effectively identify ORA enriched pathways while providing more detailed branch-level information. Moreover, in contrast to the SPIA method, it offers the advantage of being cut-off free and less susceptible to the variability caused by selection thresholds. By combining pathway curation and topology-based analysis, the PSF toolkit enhances the quality, flexibility, and accessibility of topology-aware pathway analysis. Researchers can now easily import pathways from various sources, correct and modify them as needed, and perform detailed topology-based pathway signal flow analysis. In summary, our PSF toolkit offers an integrated solution that addresses the limitations of current topology-based pathway analysis methods. By providing interactive and command-line tools for pathway curation and topology-based analysis, we empower researchers to conduct comprehensive pathway analyses across a wide range of applications.

Keywords: biological networks; gene expression; pathway analysis; single cell omics; systems biology.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Science Committee of the MESCS of Armenia (21AG-1F021 and 21SC-BRFFR-1F020), ANSEF (NS-molbio-2522), and the EIF. Computational resources provided under the state targeted project “Creating a Cloud Computing Environment for Solving Scientific and Applied Problems” (1-8/20TB).