Co-expression means two or more genes are active at the same time, often indicating they may be working together. When diseases like COVID-19 strike, the normal coordination between these genes can break down. GSNCASCR, developed at the National Heart, Lung, and Blood Institute, helps scientists spot these breakdowns more accurately than older tools, using a combination of two advanced algorithms to reconstruct gene networks and rank the importance of individual genes.

Overview of the GSNCASCR package

The package receives scRNA-seq expression matrix of case and control, and a collection of pathways as input data. It constructs two gene co-expression networks for each pathway and tests the network difference between case and control. The software allows for examining each pathway by visualizing the gene co-expression networks (case, control, and difference) and ranking the genes according to their network importance.

In testing with both simulated data and real-world datasets, GSNCASCR outperformed existing methods, especially in identifying key immune pathways affected in severe COVID-19. For example, it highlighted the IFITM3 gene as a hub in interferon gamma signaling—a result supported by genetic studies linking this gene to COVID-19 severity.

The GSNCASCR software is free and easy to use for researchers with basic R programming skills. It comes with demos and supports pathway analysis across multiple cell types, making it useful for studying a wide range of diseases and cell-specific responses.

This tool represents a valuable advance for scientists using RNA sequencing to study how gene regulation shifts in health and disease.

Availability – https://github.com/shouguog/GSNCASCR