Developing a Visual Analytics Tool for Large-Scale Proteomics Time-Series Data

Abstract

High-resolution mass spectrometry can now track all temporal changes in the phosphoproteomes of cells. The resulting time-series datasets pose a challenge ripe for the visual analytics community: how to effectively visualise - in a single graph-time-profiles for many thousands of proteins and protein complexes. To address this challenge we recently proposed a novel graph layout strategy Minardo that uses 'tracks' instead of nodes to communicate cell signalling pathways, displaying all events simultaneously, ordered in clockwise progression. Here, we summarize the key visual concepts used in Minardo to address the complexity of cell signalling data. We also discuss ongoing work on Minardo to allow interactive and collaborative approaches to managing large proteomics time-series datasets.