ICN: extracting interconnected communities in gene co-expression networks

Motivation: The analysis of gene co-expression network (GCN) is critical in examining the gene-gene interactions and learning the underlying complex yet highly organized gene regulatory mechanisms. Numerous clustering methods have been developed to detect communities of co-expressed genes in the large network. The assumed independent community structure, however, can be oversimplified and may not adequately characterize the complex biological processes. Results: We develop a new computational package to extract interconnected communities from gene co-expression network. We consider a pair of communities be interconnected if a subset of genes from one community is correlated with a subset of genes from another community. The interconnected community structure is more flexible and provides a better fit to the empirical co-expression matrix. To overcome the computational challenges, we develop efficient algorithms by leveraging advanced graph norm shrinkage approach. We validate and show the advantage of our method by extensive simulation studies. We then apply our interconnected community detection method to an RNA-seq data from The Cancer Genome Atlas (TCGA) Acute Myeloid Leukemia (AML) study and identify essential interacting biological pathways related to the immune evasion mechanism of tumor cells.

Automated summary

The study introduces a new computational package for extracting interconnected communities from gene co-expression networks, providing a more flexible approach to understanding gene regulatory mechanisms. By developing efficient algorithms and leveraging advanced graph norm shrinkage, the method demonstrates significant advantages in simulation studies. The approach is validated by applying it to RNA-seq data from a cancer study, identifying key biological pathways related to tumor cells' immune evasion mechanisms.