{"title":"simplifyEnrichment:一个用于聚类和可视化功能富集结果的生物导体包","authors":"Zuguang Gu , Daniel Hübschmann","doi":"10.1016/j.gpb.2022.04.008","DOIUrl":null,"url":null,"abstract":"<div><p><strong>Functional enrichment</strong> 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 <strong>simplify enrichment</strong> results by <strong>clustering</strong> them into groups either still produce redundancy between clusters or do not retain consistent term similarities within clusters. We propose a new method named <em>binary cut</em> for clustering similarity matrices of functional terms. Through comprehensive benchmarks on both simulated and real-world datasets, we demonstrated that <em>binary cut</em> could efficiently cluster functional terms into groups where terms showed consistent similarities within groups and were mutually exclusive between groups. We compared <em>binary cut</em> clustering on the similarity matrices obtained from different similarity measures and found that semantic similarity worked well with <em>binary cut</em>, while similarity matrices based on gene overlap showed less consistent patterns. We implemented the <em>binary cut</em> algorithm in the R package <em>simplifyEnrichment</em>, which additionally provides functionalities for visualizing, summarizing, and comparing the clustering. The <em>simplifyEnrichment</em> package and the documentation are available at <span>https://bioconductor.org/packages/simplifyEnrichment/</span><svg><path></path></svg>.</p></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 1","pages":"Pages 190-202"},"PeriodicalIF":11.5000,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10373083/pdf/","citationCount":"72","resultStr":"{\"title\":\"simplifyEnrichment: A Bioconductor Package for Clustering and Visualizing Functional Enrichment Results\",\"authors\":\"Zuguang Gu , Daniel Hübschmann\",\"doi\":\"10.1016/j.gpb.2022.04.008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p><strong>Functional enrichment</strong> 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 <strong>simplify enrichment</strong> results by <strong>clustering</strong> them into groups either still produce redundancy between clusters or do not retain consistent term similarities within clusters. We propose a new method named <em>binary cut</em> for clustering similarity matrices of functional terms. Through comprehensive benchmarks on both simulated and real-world datasets, we demonstrated that <em>binary cut</em> could efficiently cluster functional terms into groups where terms showed consistent similarities within groups and were mutually exclusive between groups. We compared <em>binary cut</em> clustering on the similarity matrices obtained from different similarity measures and found that semantic similarity worked well with <em>binary cut</em>, while similarity matrices based on gene overlap showed less consistent patterns. We implemented the <em>binary cut</em> algorithm in the R package <em>simplifyEnrichment</em>, which additionally provides functionalities for visualizing, summarizing, and comparing the clustering. The <em>simplifyEnrichment</em> package and the documentation are available at <span>https://bioconductor.org/packages/simplifyEnrichment/</span><svg><path></path></svg>.</p></div>\",\"PeriodicalId\":12528,\"journal\":{\"name\":\"Genomics, Proteomics & Bioinformatics\",\"volume\":\"21 1\",\"pages\":\"Pages 190-202\"},\"PeriodicalIF\":11.5000,\"publicationDate\":\"2023-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10373083/pdf/\",\"citationCount\":\"72\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Genomics, Proteomics & Bioinformatics\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1672022922000730\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GENETICS & HEREDITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Genomics, Proteomics & Bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1672022922000730","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
simplifyEnrichment: A Bioconductor Package for Clustering and Visualizing Functional Enrichment Results
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/.
期刊介绍:
Genomics, Proteomics and Bioinformatics (GPB) is the official journal of the Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation and Genetics Society of China. It aims to disseminate new developments in the field of omics and bioinformatics, publish high-quality discoveries quickly, and promote open access and online publication. GPB welcomes submissions in all areas of life science, biology, and biomedicine, with a focus on large data acquisition, analysis, and curation. Manuscripts covering omics and related bioinformatics topics are particularly encouraged. GPB is indexed/abstracted by PubMed/MEDLINE, PubMed Central, Scopus, BIOSIS Previews, Chemical Abstracts, CSCD, among others.