*Registration Required* and *In-Person*

Welcome to the Peer-to-Peer Teaching Summer Season 2025. These sessions are taught by our own community members to fill knowledge gaps and keep up with the accelerated pace at which bioinformatics grows as well as other data analysis methodologies.

This session dedicated to exploring gene co-expression, will be led by our colleague Giovana de Oliveira, who is a Research Fellow in the lab of Dr. Nicoli at Yale Cardiovascular Research Center.

Description:

In the era of high-throughput sequencing, exploratory data analysis tools such as DESeq2 and edgeR have become essential for identifying differentially expressed genes in omics studies. While powerful, these methods often fall short when the goal is to understand the underlying regulatory mechanisms driving complex phenotypes. Differential expression alone does not necessarily reflect functional relevance—genes may appear significantly regulated due to secondary effects or noise, rather than their active role in phenotype generation.

To move beyond gene lists and toward functional insight, network-based approaches offer a more holistic view. This workshop will focus on Weighted Gene Co-expression Network Analysis (WGCNA), a systems biology method that identifies modules of co-expressed genes and correlates them directly with traits or phenotypes of interest. WGCNA enables the discovery of core regulatory genes and hidden pathways that might be missed by classical approaches. By integrating gene expression patterns and network topology, WGCNA provides a powerful framework for prioritizing candidate genes and unraveling the molecular architecture behind complex biological processes.

Whether you're working with development, disease models, or drug response, WGCNA offers an advanced lens to explore the coordinated behavior of genes driving the phenotypes you observe.

Attendees to this session will learn:

• Understand the rationale and workflow of WGCNA to identify and interpret co-expression modules associated with phenotypic traits.
• Explore network construction using soft-thresholding power selection and topological overlap matrices (TOMs);
• Visualize module–trait associations through heatmaps and correlation plots.
• Learn how to extract hub or driver genes from key modules using intramodular connectivity and gene significance analysis.