Interactive Network Visualization of Gene Expression Time-Series Data

Abstract

Visualization models have shown to be remarkably important in the interpretation of datasets across many fields of study. In the field of Biology, data visualization is used to better understand processes that range from phylogenetic trees to multiple layers of molecular networks. The latter is especially challenging due to the large quantities of varying elements and complex relationships, often with no perceptible structure. Although various tools have been proposed to improve the visualization of molecular networks, many challenges still persist. In this paper, we propose a tool that uses interactive visualization models to represent the dynamic behaviors of molecular networks. The tool employs various methods to explore and organize the data, including clustering, force-directed layouts, and a timeline for navigating through time-series data. To further analyze temporal attributes, the timeline can be distorted through a force-directed layout to spatially position time points according to their similarity. Additionally, gene expression can be annotated through an integrated biological database. The visualization model was validated with the use of time-series gene expression RNA-Seq data from the HIV-1 infection.