An efficient, not-only-linear correlation coefficient based on clustering.

Cell systems Pub Date : 2024-09-18 Epub Date: 2024-09-06 DOI:10.1016/j.cels.2024.08.005
Milton Pividori, Marylyn D Ritchie, Diego H Milone, Casey S Greene
{"title":"An efficient, not-only-linear correlation coefficient based on clustering.","authors":"Milton Pividori, Marylyn D Ritchie, Diego H Milone, Casey S Greene","doi":"10.1016/j.cels.2024.08.005","DOIUrl":null,"url":null,"abstract":"<p><p>Identifying meaningful patterns in data is crucial for understanding complex biological processes, particularly in transcriptomics, where genes with correlated expression often share functions or contribute to disease mechanisms. Traditional correlation coefficients, which primarily capture linear relationships, may overlook important nonlinear patterns. We introduce the clustermatch correlation coefficient (CCC), a not-only-linear coefficient that utilizes clustering to efficiently detect both linear and nonlinear associations. CCC outperforms standard methods by revealing biologically meaningful patterns that linear-only coefficients miss and is faster than state-of-the-art coefficients such as the maximal information coefficient. When applied to human gene expression data from genotype-tissue expression (GTEx), CCC identified robust linear relationships and nonlinear patterns, such as sex-specific differences, that are undetectable by standard methods. Highly ranked gene pairs were enriched for interactions in integrated networks built from protein-protein interactions, transcription factor regulation, and chemical and genetic perturbations, suggesting that CCC can detect functional relationships missed by linear-only approaches. CCC is a highly efficient, next-generation, not-only-linear correlation coefficient for genome-scale data. A record of this paper's transparent peer review process is included in the supplemental information.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"854-868.e3"},"PeriodicalIF":0.0000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cell systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.cels.2024.08.005","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/9/6 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Identifying meaningful patterns in data is crucial for understanding complex biological processes, particularly in transcriptomics, where genes with correlated expression often share functions or contribute to disease mechanisms. Traditional correlation coefficients, which primarily capture linear relationships, may overlook important nonlinear patterns. We introduce the clustermatch correlation coefficient (CCC), a not-only-linear coefficient that utilizes clustering to efficiently detect both linear and nonlinear associations. CCC outperforms standard methods by revealing biologically meaningful patterns that linear-only coefficients miss and is faster than state-of-the-art coefficients such as the maximal information coefficient. When applied to human gene expression data from genotype-tissue expression (GTEx), CCC identified robust linear relationships and nonlinear patterns, such as sex-specific differences, that are undetectable by standard methods. Highly ranked gene pairs were enriched for interactions in integrated networks built from protein-protein interactions, transcription factor regulation, and chemical and genetic perturbations, suggesting that CCC can detect functional relationships missed by linear-only approaches. CCC is a highly efficient, next-generation, not-only-linear correlation coefficient for genome-scale data. A record of this paper's transparent peer review process is included in the supplemental information.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于聚类的高效非线性相关系数
在数据中识别有意义的模式对于理解复杂的生物过程至关重要,特别是在转录组学中,具有相关表达的基因往往具有共同的功能或有助于疾病机制。传统的相关系数主要捕捉线性关系,可能会忽略重要的非线性模式。我们引入了聚类匹配相关系数(CCC),这是一种利用聚类有效检测线性和非线性关联的非线性系数。通过揭示纯线性系数所忽略的有生物意义的模式,CCC 优于标准方法,而且比最大信息系数等最先进的系数更快。当应用于基因型-组织表达(GTEx)的人类基因表达数据时,CCC 发现了标准方法无法检测到的稳健线性关系和非线性模式,如性别差异。在由蛋白质-蛋白质相互作用、转录因子调控以及化学和遗传扰动构建的整合网络中,高排序基因对富集了相互作用,这表明 CCC 可以发现纯线性方法所遗漏的功能关系。CCC 是适用于基因组规模数据的高效、新一代非线性相关系数。补充信息中包含了本文透明的同行评审过程记录。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Markov field network model of multi-modal data predicts effects of immune system perturbations on intravenous BCG vaccination in macaques. A three-node Turing gene circuit forms periodic spatial patterns in bacteria. Tracking the gene expression programs and clonal relationships that underlie mast, myeloid, and T lineage specification from stem cells. Optimized reporters for multiplexed detection of transcription factor activity. Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening.
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1