Title: PATH: An interactive web platform for analysis of time-course high-dimensional genomic data

Authors: Yuping Zhang; Yang Chen; Zhengqing Ouyang

Addresses: Department of Statistics, University of Connecticut, Storrs, CT, 06269, USA ' Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, 01003, USA ' Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, 01003, USA

Abstract: 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/.

Keywords: dimension reduction; longitudinal data; visualisation; interactive analysis; feature selection; joint analysis.

DOI: 10.1504/IJCBDD.2020.113861

International Journal of Computational Biology and Drug Design, 2020 Vol.13 No.5/6, pp.529 - 538

Received: 27 Aug 2019
Accepted: 28 Apr 2020
Published online: 31 Mar 2021 *

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