Granger causality: Comparative analysis of implementations for Gene Regulatory Networks

Abstract

Granger Causality (GC) is an effective tool for determining functional connectivity in time-series data. However, application of GC is limited by the curse of dimensionality in many applications, e.g. Gene Regularity Networks (GRN). Various methods have been proposed to overcome this limitation. To the best of our knowledge, there is no detailed comparative study of such methods. We aim to perform a detailed comparative study of a few of such methods using different statistical measures under various constraints.