Disentangling Multidimensional Spatio-Temporal Data into their Common and Aberrant Responses

With the advent of high-throughput measurement techniques, scientists and engineers are starting to grapple with massive data sets and encountering challenges with how to organize, process and extract information into meaningful structures. Multidimensional spatio-temporal biological data sets such as time series gene expression with various perturbations with different cell lines, or neural spike data sets across many experimental trials have the potential to acquire insight across multiple dimensions. For this potential to be realized, we need a suitable representation to turn data into insight. Since a wide range of experiments and the (unknown) complexity of underlying system make biological data more heterogeneous than those in other fields, we propose the method based on Robust Principal Component Analysis (RPCA), which is well suited for extracting principal components where we have corrupted observations. The proposed method provides us a new representation of these data sets which consists of its common and aberrant response. This representation might help users to acquire a new insight from data. Author Summary One of the most exciting trends and important themes in science and engineering involves the use of high-th...Continue Reading