e-learning
Inferring single cell trajectories with Monocle3 (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
- How do I perform a trajectory analysis using Monocle3 in R?
- What should I do when Galaxy's Monocle tools are not enough?
- How do I assign cell types to the clusters?
- How do I infer lineage relationships between clusters, without a time series?
- How do I perform batch correction and differential expression analysis in Monocle?
Learning Objectives
- Identify which operations are necessary to transform an AnnData object into the files needed for Monocle
- Describe the Monocle3 functions in R
- Recognise steps that can be performed in R, but not with current Galaxy tools
- Repeat the Monocle3 workflow and choose appropriate parameter values
- Compare the outputs from Scanpy, Monocle in Galaxy and Monocle in R
- Describe differential expression analysis methods
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: 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 which operations are necessary to transform an AnnData object into the files needed for Monocle
- Describe the Monocle3 functions in R
- Recognise steps that can be performed in R, but not with current Galaxy tools
- Repeat the Monocle3 workflow and choose appropriate parameter values
- Compare the outputs from Scanpy, Monocle in Galaxy and Monocle in R
- Describe differential expression analysis methods
Date modified: 2024-10-28
Date published: 2023-04-12
Contributors: Helena Rasche, Pavankumar Videm, Wendi Bacon
External resources:
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