Top-down analysis of gene-regulatory networks using global coordination method

<div style="direction: ltr;">Cellular activity is governed by an underlying gene regulatory network. However, due to the large complexity of the regulatory network, inferring them is extremely challenging. A novel computational measure was recently developed to cut across the network of interactions among genes and consider their transcriptional interrelationships in a top-down manner. The new measure, called global coordination level (GCL), is based on the average multivariate dependency between the expression levels of random subsets of genes in single-cell RNA-seq datasets. Yet, there are several fundamental features of this top-down method that need addressing. Here, we systematically analyse the performance and limitations of the GCL using real and simulated gene expression data. We compare the results to other bottom-up methods, such as co-expression matrices and reconstructed networks. We find that the GCL has significant advantage in detecting the gene-to-gene coordination level, especially where the number of available samples is low compared to the number of genes, which is a typical scenario in current available data. The systematic analysis of the GCL provides a useful foundation for future application of this method.</div>