PATH:一个用于分析时间过程高维基因组数据的交互式网络平台

Q4 Pharmacology, Toxicology and Pharmaceutics International Journal of Computational Biology and Drug Design Pub Date : 2020-01-01 DOI:10.1504/IJCBDD.2020.113861
Yuping Zhang, Yang Chen, Z. Ouyang
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引用次数: 0

摘要

发现时间过程基因组数据的模式可以提供对健康和疾病中生物系统动力学的见解。在这里,我们提出了一个时间过程高维数据分析平台(PATH)及其在基因组学研究中的应用。这个web应用程序提供了一个用户友好的界面,具有交互式数据可视化、降维、模式发现和基于主要趋势分析(PTA)的特征选择。此外,web应用程序支持基于联合PTA的时间过程高维数据的交互式和集成分析。通过仿真和实际算例,并与经典时程数据分析方法(如功能主成分分析)进行了比较,说明了PATH的实用性。PATH可在https://ouyanglab.shinyapps.io/PATH/免费访问。
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PATH: An interactive web platform for analysis of time-course high-dimensional genomic data
Discovering patterns in time-course genomic data can provide insights on the dynamics of biological systems in health and disease. Here, we present a Platform for Analysis of Time-course High-dimensional data (PATH) with applications in genomics research. This web application provides a user-friendly interface with interactive data visualisation, dimension reduction, pattern discovery, and feature selection based on the principal trend analysis (PTA). Furthermore, the web application enables interactive and integrative analysis of time-course high-dimensional data based on the Joint PTA. The utilities of PATH are demonstrated through simulated and real examples, and the comparison with classical time-course data analysis methods such as the functional principal component analysis. PATH is freely accessible at https://ouyanglab.shinyapps.io/PATH/.
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来源期刊
International Journal of Computational Biology and Drug Design
International Journal of Computational Biology and Drug Design Pharmacology, Toxicology and Pharmaceutics-Drug Discovery
CiteScore
1.00
自引率
0.00%
发文量
8
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