Open AccessPATH: An interactive web platform for analysis of time-course high-dimensional genomic data
Author(s) -
Yuping Zhang,
Yang Chen,
Zhengqing Ouyang
Publication year - 2020
Publication title -
international journal of computational biology and drug design
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.107
H-Index - 13
eISSN - 1756-0764
pISSN - 1756-0756
DOI - 10.1504/ijcbdd.2020.10036399
Subject(s) - computer science , principal component analysis , path (computing) , dimensionality reduction , path analysis (statistics) , data mining , dimension (graph theory) , visualization , data visualization , interface (matter) , feature (linguistics) , course (navigation) , data science , machine learning , artificial intelligence , engineering , mathematics , linguistics , philosophy , bubble , maximum bubble pressure method , parallel computing , pure mathematics , programming language , aerospace engineering
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/.