Seattle Eastside Cancer Research

WGCNA - Weighted Gene Co-expression Analysis is a multi step process that starts with data cleaning of gene expression data (RNA sequencing), correlation analysis of gene to gene, building a network, and then clustering the network using hierarchical clustering. The output are gene networks. I'll show how to find gene expression data, then use PyWGCNA to find cancer genes. We'll discuss how the output can be enriched with LLMs to build up a rich report that researchers (or you) can use to help identify drug targets, and/or understand pathways that are involved in cancer or other diseases.

I'll cover how to do a correlation analysis and go over some basics like setting up VSCode and loading libraries. So if you're a beginner to data science, we'll build this up step by step. Correlation analysis is a core data science skill.