Abstract

In the tutorial Filter, plot and explore single-cell RNA-seq data with Scanpy, we took an important step in our single-cell RNA sequencing analysis by identifying marker genes for each of the clusters in our dataset. These marker genes are crucial, as they help us distinguish between different cell types and states, giving us a clearer picture of the cellular diversity within our samples.

About This Material

This is a Hands-on Tutorial from the GTN which is usable either for individual self-study, or as a teaching material in a classroom.

Questions this will address

• What is Gene Ontology (GO) enrichment analysis, and why should I perform it on my marker genes?
• How can I use GO enrichment analysis to better understand the biological functions of the genes in my clusters?
• Can GO enrichment analysis help me confirm that my clusters represent distinct cell types or states?
• How can I visualize my GO enrichment results to make them easier to understand and interpret?

Learning Objectives

• Understand the Role of GO Enrichment in Single-Cell Analysis.
• Use marker genes from different cell clusters or conditions for GO enrichment analysis.
• Compare enrichment across experimental conditions (e.g., wild type vs. knockout) to uncover functional changes associated with genetic or environmental perturbations.
• Link GO enrichment results with previously annotated cell clusters, providing a clearer picture of the functional roles of different cell populations.