Meaningful local signalling in sinoatrial node identified by random matrix theory and PCA

Abstract The sinoatrial node (SAN) is the pacemaker of heart. Recently calcium signals, believed to be crucially important in rhythm generation, have been imaged intact SAN and shown heterogeneous various regions with a lot analysis relying on visual inspection rather than mathematical tools. Here we apply methods random matrix theory (RMT) developed for financial data biological sets including β -cell collectives electroencephalograms (EEG) analyse correlations signals using eigenvalues eigenvectors correlation matrix. We use principal component locate signalling modules corresponding localization properties high eigenvalues. find that top eigenvector captures global behaviour i.e. action potential (AP) induced transient. In some cases, second highest eigenvalue yields region whose predict AP. Furthermore, new analytic methods, study relationship between covariance coefficients distance, even inside central zone, there are non-trivial long range correlations, indicating intercellular interactions most cases. Lastly, perform an nearest-neighbour distances it coincides universal Wigner surmise under all available experimental conditions, while number variance, which sensitive conditions. Thus RMT application allows remove noise effects AP-induced transient thereby isolate local meaningful signalling.