Heatmaps for analyzing gene expression data
Heatmaps are very handy tools for the analysis and visualization of large multi-dimensional datasets. They are often used with high-throughput gene expression data as they can help to locate hidden groups among analyzed genes or association between experimental conditions and gene expression patterns. In this crash course you will learn the important tricks how to apply this tool successfully in your projects. Besides you will get an opportunity to practice creating heatmaps with online services, or if you are at a more advanced level, with R statistical environment.
You will learn how to prepare data, what are the alternative calculation methods, and available software for creating clustering heatmaps from high throughput gene expression data. You will understand how to interpret these plots and how to include them into your research plans.
Course duration: Crash course format, 1 week, expected workload 10 to 15 hours.
Suggested background knowledge: You are expected to know what high throughput gene expression experiments are, their basic goals and arrangements. You do not need special software to accomplish this course other than a web browser.
- Module 1 – Theoretical background
- Heatmap analysis: an overview
- An overview of related concepts
- Module 2 – Methodological overview
- Matrix coloring
- Statistical aspects
- Software for heatmaps
- Module 3 – Practice
- The heatmap tool at Gene Expression Omnibus
- Extra practice: How to create heatmaps with R
Special requirement: As an extra practice, you will get a demonstration how to create different heatmaps using the R statistical environment. To test this extra part, you will have to use the R software for the practical parts of this course. R is a free software programming language and a software environment for statistical computing and graphics. You have to be able to install this free software on your computer.