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Clustered Pathway Analysis
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.ORCID iD: 0000-0002-4665-6537
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.ORCID iD: 0000-0003-2245-7557
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.ORCID iD: 0000-0002-9015-5588
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Motivation: Functional analysis of gene sets derived from experiments is typically done by pathway annotation. Although many algorithms exist for analyzing the association between a gene set and a pathway, an issue which is generally ignored is that gene sets often represent multiple pathways. In such cases an association to a pathway is weakened by the presence of genes associated with other pathways. A way to counteract this is to cluster the gene set into more homogenous parts before performing pathway analysis on each cluster.

Results: We explored whether network-based pre-clustering of a query gene set can improve pathway analysis. The methods MCL, Infomap, and MGclus were used to cluster the gene set projected onto the FunCoup network. We characterized how well these methods are able to detect individual pathways in multi-pathway gene sets, and applied each of the clustering methods in combination with four pathway analysis methods: Gene Enrichment Analysis, BinoX, NEAT, and ANUBIX. Using benchmarks constructed from the KEGG pathway database we found that clustering substantially increased the sensitivity of pathway analysis methods. For ANUBIX this came with almost no loss of specificity, while for BinoX and NEAT the specificity decreased roughly as much as the sensitivity increased. GEA had very low sensitivity both before and after clustering. The choice of clustering method only had a minor effect on the results. We conclude that clustering can improve overall pathway annotation performance, but only if the used enrichment method has a low false positive rate. 

Availability and Implementation: https://bitbucket.org/sonnhammergroup/clustering-and-pathway-enrichment/

National Category
Bioinformatics and Computational Biology
Identifiers
URN: urn:nbn:se:su:diva-195034OAI: oai:DiVA.org:su-195034DiVA, id: diva2:1582509
Available from: 2021-08-02 Created: 2021-08-02 Last updated: 2025-02-07Bibliographically approved
In thesis
1. From networks to pathway analysis
Open this publication in new window or tab >>From networks to pathway analysis
2021 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Biological mechanisms stem from complex intracellular interactions spanning across different levels of regulation. Mapping these interactions is fundamental for the understanding of all types of biological conditions, including complex diseases. Each experimental approach carries its own bias and noise. Combining heterogeneous data sources reduces noise and gives a broader sense of the interactions between genes known as functional association, where both direct and indirect interactions are captured.

FunCoup is one of the most comprehensive functional association databases, providing networks for 22 organisms in all domains of life. FunCoup uses a naïve Bayesian integration approach to combine 11 different data types and increases the coverage by transferring associations between species via orthologs. Additional insights into the mechanisms of a gene network are provided through tissue specificity filtering and directed regulatory links.

Even though FunCoup provides a comprehensive map of the intracellular machinery, gaining insights into conditions such as diseases requires a functional level analysis rather than a gene level analysis. Thus, studying genes that are involved in a condition from a functional perspective requires the usage of pathway enrichment analysis. Several approaches exist, from basic gene overlap to more elaborate analyses that use functional association networks. ANUBIX is a novel network-based analysis (NBA) method that overcomes the high false positive rate issue that previous state-of-the-art NBA approaches have. Additionally, even with accurate methods, a commonly ignored problem is that gene sets derived from experiments are often noisy or contain multiple mechanisms, mixing different pathways which weakens their association to the condition under study. To increase the sensitivity of pathway analysis, we developed a pipeline to cluster gene sets into more homogeneous parts with the aim of unraveling all the mechanisms activated in the studied condition. To facilitate the usage of these tools, we built a web server called PathBIX, a user-friendly platform that allows interactive analysis of all species in FunCoup against multiple pathway databases.

Place, publisher, year, edition, pages
Stockholm: Department of Biochemistry and Biophysics, Stockholm University, 2021. p. 77
National Category
Bioinformatics and Computational Biology
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-195048 (URN)978-91-7911-564-7 (ISBN)978-91-7911-565-4 (ISBN)
Public defence
2021-09-17, Air and Fire, SciLifeLab, Tomtebodavägen 23, Solna, 14:00 (English)
Opponent
Supervisors
Available from: 2021-08-25 Created: 2021-08-03 Last updated: 2025-02-07Bibliographically approved

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Castresana Aguirre, MiguelGuala, DimitriSonnhammer, Erik

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