e-learning
Removing the effects of the cell cycle
Abstract
Single-cell RNA sequencing can be sensitive to both biological and technical variation, which is why preparing your data carefully is an important part of the analysis. You want the results to reflect the interesting differences in expression between cells that relate to their type or state. Other sources of variation can conceal or confound this, making it harder for you to see what is going on.
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
- How can I reduce the effects of the cell cycle on my scRNA-seq data?
Learning Objectives
- Identify the cell cycle genes
- Use the cell cycle genes to regress out the effects of the cell cycle
- Create PCA plots to understand the impact of the regression
Licence: Creative Commons Attribution 4.0 International
Keywords: 10x, Single Cell
Target audience: Students
Resource type: e-learning
Version: 11
Status: Active
Prerequisites:
- Combining single cell datasets after pre-processing
- Filter, plot and explore single-cell RNA-seq data with Scanpy
- Generating a single cell matrix using Alevin
- Introduction to Galaxy Analyses
Learning objectives:
- Identify the cell cycle genes
- Use the cell cycle genes to regress out the effects of the cell cycle
- Create PCA plots to understand the impact of the regression
Date modified: 2024-06-14
Date published: 2023-01-25
Contributors: Graeme Tyson, Wendi Bacon
Activity log
