Discovering Propagating Signals in High-Content Multivariate Time Series via Spatio-Temporal Subsequence Clustering

Big data technologies have been applied successfully to diverse application domains in order to facilitate analytics of voluminous and heterogeneous databases at scale. Digital sensory typically provides high-content data comprising multiple data recordings with high frequency. One example of such sensory are multi-electrode arrays (MEA), which are able to measure electric cell activity with high spatial and temporal resolution. The resulting multivariate time series and their inherent subsequences can then be analyzed and compared in aspects of time, space and shape. This analytical process is frequently performed manually by domain experts in combination with data analytical methods that help to identify and track signal beginnings, signal ends and signal propagations.In this paper, we propose an unsupervised approach to discover propagating signals in high-content, multivariate time series databases. To this end, we introduce an efficient spatio-temporal subsequence clustering algorithm that detects and tracks spatial and temporal signal progagations by means of density-based clusters. We present a formal propagation model and show how to adapt the DBSCAN algorithm to our specific application setting on pharmacological data. Our empirical investigation shows that our proposal is able to detect signal propagations with high accuracy and efficiency. Our approach hence scales not only to pharmacological settings but also to other biological, medical, and chemical domains making use of high-content multi-electrode array data.