Show and Tell: Start Exploring Single-cell Data
Posted by Julia Ibe, on 31 August 2026
Bioinformatics can seem intimidating, especially when you’re just starting out. But you don’t need to be an expert to begin exploring single-cell RNA-seq data. In this short tutorial, I walk through a simple four-step workflow using a publicly available dataset from the developing zebrafish heart: get the data, prepare it, visualize the cells, and ask a biological question.
What is this?
This is a beginner-friendly introduction to exploring single-cell RNA-seq data using publicly available datasets.
Where can this be found?
The tutorial uses a publicly available dataset from the Gene Expression Omnibus (GEO): GSE296176, Single-cell transcriptomic profiling of the developing zebrafish heart.
How was this made?
Using R and Seurat, we go through four simple steps: getting the data, preparing it, visualizing the cells, and asking a biological question.
Why should people care about this?
Single-cell RNA-seq is transforming the way we study biology. Today, this technology is widely used in research on development, cancer, aging, regeneration and disease, helping researchers understand what is happening at the level of individual cells. By revealing differences that can be hidden when we look at an entire tissue, single-cell transcriptomics is becoming an increasingly important tool for understanding both how healthy tissues develop and how they change in disease.
How would you explain this to an 8-year-old?
Imagine you have a big box of LEGO pieces, but you don’t know which pieces are there. Single-cell analysis helps you sort the pieces into groups and figure out what each one might be used to build.
Where can people find more about it?
The step-by-step PDF tutorial and the complete R script used in the video are available on GitHub: https://github.com/onishiibe/single-cell-tutorial/tree/main
2 thoughts on “Show and Tell: Start Exploring Single-cell Data”
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As someone who still finds the first steps into bioinformatics a bit intimidating, I really enjoyed this. There are so many biological questions I’d love to explore using publicly available datasets, and practical, approachable walkthroughs like this make that feel much more doable. Really motivating resource for getting started with single-cell data!
I found this short guide to be a really nice introduction to scRNA-seq. I was able to grasp the basic steps, and it made me realize that maybe it’s not that hard after all! It also got me thinking about what questions I could explore in my own research. Superb!