simplifyEnrichment:A Bioconductor Package for Clustering and Visualizing Functional Enrichment Results

作     者：Zuguang Gu Daniel Hübschmann 

作者机构：Molecular Precision Oncology ProgramNational Center for Tumor Diseases(NCT)HeidelbergD-69120 HeidelbergGermany Heidelberg Institute of Stem Cell Technology and Experimental Medicine(HI-STEM)D-69120 HeidelbergGermany German Cancer Consortium(DKTK)D-69120 HeidelbergGermany Department of Pediatric ImmunologyHematology and OncologyUniversity Hospital HeidelbergD-69120 HeidelbergGermany 

出 版 物：《Genomics, Proteomics & Bioinformatics》 (基因组蛋白质组与生物信息学报（英文版）)

年 卷 期：2023年第21卷第1期

页      面：190-202页

核心收录：

学科分类：0710[理学-生物学] 0711[理学-系统科学] 07[理学] 

基　　金：This work was supported by the National Center for Tumor Diseases(NCT)Molecular Precision Oncology Program and the NCT Donations against Cancer Program Germany. 

主　　题：Functional enrichment Simplify enrichment Clustering R/Bioconductor Software Visualization 

摘      要：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://***/packages/simplifyEnrichment/.