GCNPCC: Inferring gene co-expression networks from scRNA-seq data using partial correlation coefficients
We propose a method, GCNPCC, based on partial correlation coefficient to reconstruct gene co-expression networks by measuring correlation relationships among genes through statistical inference. Besides the gene interaction network, GCNPCC can also identify correlation relationships for gene modules that are composed of functionally similar genes, and infer the functional roles each gene played in the modules. We applied it to several real data sets and compared it with several state-of-the-art methods. Results demonstrate that our method is superior to other existing methods in terms of both accuracy and specificity.