simplifyEnrichment

• Verfasst von: Gu, Zuguang [VerfasserIn]
• Verfasst von: Hübschmann, Daniel [VerfasserIn]
• Titel: simplifyEnrichment
• Titelzusatz: a bioconductor package for clustering and visualizing functional enrichment results
• Verf.angabe: Zuguang Gu, Daniel Hübschmann
• Jahr: 2023
• Umfang: 13 S.
• Illustrationen: Illustrationen
• Fussnoten: Gesehen am 13.10.2023 ; Online verfügbar: 6 Juni 2022
• Titel Quelle: Enthalten in: Genomics, proteomics & bioinformatics
• Ort Quelle: Amsterdam [u.a.] : Elsevier, 2003
• Jahr Quelle: 2023
• Band/Heft Quelle: 21(2023), 1, Seite 190-202
• ISSN Quelle: 2210-3244
• DOI: doi:10.1016/j.gpb.2022.04.008
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: algorithms
• Sach-SW: cluster analysis
• Sach-SW: clustering
• Sach-SW: computational biology
• Sach-SW: functional enrichment
• Sach-SW: R/bioconductor
• Sach-SW: semantics
• Sach-SW: simplify enrichment
• Sach-SW: software
• Sach-SW: visualization
• K10plus-PPN: 186379817X

Abstract

Functional enrichment analysis or gene set enrichment analysis is a basic bioinformatics method that evaluates the biological importance of a list of genes of interest. However, it may produce a long list of significant terms with highly redundant information that is difficult to summarize. Current tools to simplify enrichment results by clustering them into groups either still produce redundancy between clusters or do not retain consistent term similarities within clusters. We propose a new method named binary cut for clustering similarity matrices of functional terms. Through comprehensive benchmarks on both simulated and real-world datasets, we demonstrated that binary cut could efficiently cluster functional terms into groups where terms showed consistent similarities within groups and were mutually exclusive between groups. We compared binary cut clustering on the similarity matrices obtained from different similarity measures and found that semantic similarity worked well with binary cut, while similarity matrices based on gene overlap showed less consistent patterns. We implemented the binary cut algorithm in the R package simplifyEnrichment, which additionally provides functionalities for visualizing, summarizing, and comparing the clustering. The simplifyEnrichment package and the documentation are available at https://bioconductor.org/packages/simplifyEnrichment/.