Omada: robust clustering of transcriptomes through multiple testing.

IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES
Sokratis Kariotis, Pei Fang Tan, Haiping Lu, Christopher J Rhodes, Martin R Wilkins, Allan Lawrie, Dennis Wang
{"title":"Omada: robust clustering of transcriptomes through multiple testing.","authors":"Sokratis Kariotis, Pei Fang Tan, Haiping Lu, Christopher J Rhodes, Martin R Wilkins, Allan Lawrie, Dennis Wang","doi":"10.1093/gigascience/giae039","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Cohort studies increasingly collect biosamples for molecular profiling and are observing molecular heterogeneity. High-throughput RNA sequencing is providing large datasets capable of reflecting disease mechanisms. Clustering approaches have produced a number of tools to help dissect complex heterogeneous datasets, but selecting the appropriate method and parameters to perform exploratory clustering analysis of transcriptomic data requires deep understanding of machine learning and extensive computational experimentation. Tools that assist with such decisions without prior field knowledge are nonexistent. To address this, we have developed Omada, a suite of tools aiming to automate these processes and make robust unsupervised clustering of transcriptomic data more accessible through automated machine learning-based functions.</p><p><strong>Findings: </strong>The efficiency of each tool was tested with 7 datasets characterized by different expression signal strengths to capture a wide spectrum of RNA expression datasets. Our toolkit's decisions reflected the real number of stable partitions in datasets where the subgroups are discernible. Within datasets with less clear biological distinctions, our tools either formed stable subgroups with different expression profiles and robust clinical associations or revealed signs of problematic data such as biased measurements.</p><p><strong>Conclusions: </strong>In conclusion, Omada successfully automates the robust unsupervised clustering of transcriptomic data, making advanced analysis accessible and reliable even for those without extensive machine learning expertise. Implementation of Omada is available at http://bioconductor.org/packages/omada/.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"13 ","pages":""},"PeriodicalIF":11.8000,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11238428/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"GigaScience","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/gigascience/giae039","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Cohort studies increasingly collect biosamples for molecular profiling and are observing molecular heterogeneity. High-throughput RNA sequencing is providing large datasets capable of reflecting disease mechanisms. Clustering approaches have produced a number of tools to help dissect complex heterogeneous datasets, but selecting the appropriate method and parameters to perform exploratory clustering analysis of transcriptomic data requires deep understanding of machine learning and extensive computational experimentation. Tools that assist with such decisions without prior field knowledge are nonexistent. To address this, we have developed Omada, a suite of tools aiming to automate these processes and make robust unsupervised clustering of transcriptomic data more accessible through automated machine learning-based functions.

Findings: The efficiency of each tool was tested with 7 datasets characterized by different expression signal strengths to capture a wide spectrum of RNA expression datasets. Our toolkit's decisions reflected the real number of stable partitions in datasets where the subgroups are discernible. Within datasets with less clear biological distinctions, our tools either formed stable subgroups with different expression profiles and robust clinical associations or revealed signs of problematic data such as biased measurements.

Conclusions: In conclusion, Omada successfully automates the robust unsupervised clustering of transcriptomic data, making advanced analysis accessible and reliable even for those without extensive machine learning expertise. Implementation of Omada is available at http://bioconductor.org/packages/omada/.

Omada:通过多重测试对转录组进行稳健聚类。
背景:越来越多的队列研究收集生物样本进行分子分析,并观察到分子异质性。高通量 RNA 测序正在提供能够反映疾病机制的大型数据集。聚类方法产生了许多工具来帮助剖析复杂的异构数据集,但要选择适当的方法和参数来对转录组数据进行探索性聚类分析,需要对机器学习有深入的了解和大量的计算实验。目前还不存在无需事先了解领域知识就能协助做出此类决定的工具。为了解决这个问题,我们开发了 Omada,这是一套工具,旨在通过基于机器学习的自动功能实现这些过程的自动化,并使转录组数据的稳健无监督聚类变得更容易获得:我们用 7 个数据集测试了每个工具的效率,这些数据集的特点是表达信号强度不同,可以捕捉到广泛的 RNA 表达数据集。我们工具包的决策反映了数据集中稳定分区的实际数量,在这些数据集中,亚群是可以分辨的。在生物学区分不太明显的数据集中,我们的工具要么形成了具有不同表达谱和稳健临床关联的稳定亚组,要么揭示了有问题数据的迹象,如偏差测量:总之,Omada 成功地实现了转录组数据无监督聚类的自动化,使那些没有丰富机器学习专业知识的人也能进行可靠的高级分析。Omada的实现可在http://bioconductor.org/packages/omada/。
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
GigaScience
GigaScience MULTIDISCIPLINARY SCIENCES-
CiteScore
15.50
自引率
1.10%
发文量
119
审稿时长
1 weeks
期刊介绍: GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.
×
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