Gene co-expression and differential co-expression analysis has been increasingly used to study co-functional and co-regulatory biological mechanisms from large scale transcriptomics data sets. Methods: In this study, we develop a nonparametric approach to identify hub genes and modules in a large coexpression network with low computational and memory cost, namely MRHCA. Results: We have applied the method to simulated transcriptomics data sets and demonstrated MRHCA can accurately identify hub genes and estimate size of co-expression modules. With applying MRHCA and differential co-expression analysis to E. coli and TCGA cancer data, we have identified significant condition specific activated genes in E. coli and distinct gene expression regulatory mechanisms between the cancer types with high copy number variation and small somatic mutations. Conclusion: Our analysis has demonstrated MRHCA can (i) deal with large association networks, (ii) rigorously assess statistical significance for hubs and module sizes, (iii) identify co-expression modules with low associations, (iv) detect small and significant modules, and (v) allow genes to be present in more than one modules, compared with existing methods.