MoNETA: MultiOmics Network Embedding for SubType Analysis.

IF 4 Q1 GENETICS & HEREDITY
NAR Genomics and Bioinformatics Pub Date : 2024-10-16 eCollection Date: 2024-09-01 DOI:10.1093/nargab/lqae141
Giovanni Scala, Luigi Ferraro, Aurora Brandi, Yan Guo, Barbara Majello, Michele Ceccarelli
{"title":"MoNETA: MultiOmics Network Embedding for SubType Analysis.","authors":"Giovanni Scala, Luigi Ferraro, Aurora Brandi, Yan Guo, Barbara Majello, Michele Ceccarelli","doi":"10.1093/nargab/lqae141","DOIUrl":null,"url":null,"abstract":"<p><p>Cells are complex systems whose behavior emerges from a huge number of reactions taking place within and among different molecular districts. The availability of bulk and single-cell omics data fueled the creation of multi-omics systems biology models capturing the dynamics within and between omics layers. Powerful modeling strategies are needed to cope with the increased amount of data to be interrogated and the relative research questions. Here, we present MultiOmics Network Embedding for SubType Analysis (MoNETA) for fast and scalable identification of relevant multi-omics relationships between biological entities at the bulk and single-cells level. We apply MoNETA to show how glioma subtypes previously described naturally emerge with our approach. We also show how MoNETA can be used to identify cell types in five multi-omic single-cell datasets.</p>","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"6 4","pages":"lqae141"},"PeriodicalIF":4.0000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11482636/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"NAR Genomics and Bioinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/nargab/lqae141","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/9/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0

Abstract

Cells are complex systems whose behavior emerges from a huge number of reactions taking place within and among different molecular districts. The availability of bulk and single-cell omics data fueled the creation of multi-omics systems biology models capturing the dynamics within and between omics layers. Powerful modeling strategies are needed to cope with the increased amount of data to be interrogated and the relative research questions. Here, we present MultiOmics Network Embedding for SubType Analysis (MoNETA) for fast and scalable identification of relevant multi-omics relationships between biological entities at the bulk and single-cells level. We apply MoNETA to show how glioma subtypes previously described naturally emerge with our approach. We also show how MoNETA can be used to identify cell types in five multi-omic single-cell datasets.

MoNETA:用于子类型分析的多声学网络嵌入。
细胞是一个复杂的系统,其行为源于不同分子区内部和之间发生的大量反应。大量和单细胞组学数据的可用性推动了捕捉组学层内部和之间动态的多组学系统生物学模型的建立。我们需要强大的建模策略来应对日益增多的待查询数据量和相关研究问题。在这里,我们提出了用于子类型分析的多组学网络嵌入(MoNETA),用于快速、可扩展地识别大块和单细胞水平生物实体之间的相关多组学关系。我们应用 MoNETA 展示了之前描述的胶质瘤亚型是如何通过我们的方法自然出现的。我们还展示了如何利用 MoNETA 在五个多组学单细胞数据集中识别细胞类型。
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 求助全文
来源期刊
CiteScore
8.00
自引率
2.20%
发文量
95
审稿时长
15 weeks
×
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