e-learning
Filter, plot, and explore single cell RNA-seq data with Seurat (R)
Abstract
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
- Is my single cell dataset a quality dataset?
- How do I pick thresholds and parameters in my analysis? What’s a “reasonable” number, and will the world collapse if I pick the wrong one?
- How do I generate and annotate cell clusters?
Learning Objectives
- Interpret quality control plots to direct parameter decisions
- Repeat analysis from matrix to clustering to labelling clusters
- Identify decision-making points
- Appraise data outputs and decisions
- Explain why single cell analysis is an iterative process (i.e. the first plots you generate are not final, but rather you go back and re-analyse your data repeatedly)
Licence: Creative Commons Attribution 4.0 International
Keywords: 10x, MIGHTS, R, Single Cell, jupyter-notebook, paper-replication, rmarkdown-notebook
Target audience: Students
Resource type: e-learning
Version: 9
Status: Active
Prerequisites:
- Combining single cell datasets after pre-processing
- Filter, plot and explore single-cell RNA-seq data with Scanpy
- Introduction to Galaxy Analyses
Learning objectives:
- Interpret quality control plots to direct parameter decisions
- Repeat analysis from matrix to clustering to labelling clusters
- Identify decision-making points
- Appraise data outputs and decisions
- Explain why single cell analysis is an iterative process (i.e. the first plots you generate are not final, but rather you go back and re-analyse your data repeatedly)
Date modified: 2024-10-28
Date published: 2023-10-02
Contributors: Helena Rasche, Mehmet Tekman, Pavankumar Videm, Saskia Hiltemann, Wendi Bacon
External resources:
Activity log
