simplifyEnrichment:一个用于聚类和可视化功能富集结果的生物导体包

IF 11.5 2区 生物学 Q1 GENETICS & HEREDITY
Zuguang Gu , Daniel Hübschmann
{"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 ,&nbsp;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 ,&nbsp;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}
引用次数: 72

摘要

功能富集分析或基因集富集分析是一种基本的生物信息学方法,用于评估感兴趣基因列表的生物学重要性。然而,它可能会产生一长串重要术语,其中包含难以总结的高度冗余信息。当前通过将富集结果聚类为组来简化富集结果的工具要么仍然在聚类之间产生冗余,要么在聚类内不保持一致的术语相似性。提出了一种新的函数项相似度矩阵聚类方法——二元割。通过在模拟数据集和真实世界数据集上进行全面的基准测试,我们证明了二进制切割可以有效地将功能术语聚类到组中,其中术语在组内表现出一致的相似性,并且在组之间相互排斥。我们比较了从不同相似性度量中获得的相似性矩阵的二元切割聚类,发现语义相似性与二元切割效果良好,而基于基因重叠的相似性基质显示出不太一致的模式。我们在R包simplifyEnrichment中实现了二进制切割算法,该算法还提供了可视化、总结和比较聚类的功能。simplefyEnrichment软件包和文档可在https://bioconductor.org/packages/simplifyEnrichment/.
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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

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

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/.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Genomics, Proteomics & Bioinformatics
Genomics, Proteomics & Bioinformatics Biochemistry, Genetics and Molecular Biology-Biochemistry
CiteScore
14.30
自引率
4.20%
发文量
844
审稿时长
61 days
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信