Multiscale Embedded Gene Co-expression Network Analysis

Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.Author Summary: We developed a novel co-expression network analysis framework named Multiscale Embedded Gene co-Expression Network Analysis (MEGENA) that can effectively and efficiently construct and analyze large scale planar filtered co-expression networks. Two key components of MEGENA are the parallelization of embedded network construction and the identification of multi-scale clustering structures. MEGENA was applied to the breast cancer (BRCA) and the lung adenocarcinoma (LUAD) data from The Cancer Genome Atlas (TCGA) and showed much improved performance over well-established co-expression network approaches such as un-weighted and weighted gene co-expression network analyses. MEGENA revealed not only biologically meaningful multi-scale clustering structures of gene co-expression in both BRCA and LUAD, but also novel key regulators of important cancer biological processes like lineage-specific differentiations in LUAD. MEGENA is complementary to the established co-expression network analysis approaches by its capability of sparsifying densely connected co-expression networks and identifying multiscale modular structures.