Applying single-cell RNA-seq analysis
Licence: Creative Commons Attribution 4.0 International
Keywords: single-cell
Status: Active
Module 1: Preparing the dataset
•• intermediate 2 materialsThis tutorial takes you from the large files containing raw scRNA sequencing reads to a smaller, combined cell matrix.
Time estimation: 3 hours
Learning objectives:
- Generate a cellxgene matrix for droplet-based single cell sequencing data
- Interpret quality control (QC) plots to make informed decisions on cell thresholds
- Find relevant information in GTF files for the particulars of their study, and include this in data matrix metadata
- Combine data matrices from different samples in the same experiment
- Label the metadata for downstream processing
Module 2: Generating cluster plots
•• intermediate 2 materialsThese tutorials take you from the pre-processed matrix to cluster plots and gene expression values. You can pick whether to follow the Scanpy or Seurat tutorials - they will accomplish the same thing and generate the same results, so follow whichever you prefer!
Time estimation: 6 hours
Learning objectives:
- Interpret quality control plots to direct parameter decisions
- Repeat analysis from matrix to clustering
- Identify decision-making points
- Appraise data outputs and decisions
- Explain why single cell analysis is an iterative (i.e. the first plots you generate are not final, but rather you go back and re-analyse your data repeatedly) process
- 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)
Module 3: Inferring trajectories
•• intermediate 2 materialsThis isn’t strictly necessary, but if you want to infer trajectories - pseudotime relationships between cells - you can try out these tutorials with the same dataset. Again, you get two options for inferring trajectories, and you can choose either.
Time estimation: 5 hours
Learning objectives:
- Execute multiple plotting methods designed to identify lineage relationships between cells
- Interpret these plots
- Identify which operations to perform on an AnnData object to obtain the files needed for Monocle
- Follow the Monocle3 workflow and choose the right parameter values
- Compare the outputs from Scanpy and Monocle
- Interpet trajectory analysis results
Module 4: Moving into coding environments
•• intermediate 2 materialsDid you know Galaxy can host coding environments? They don’t have the same level of computational power as the easy-to-use Galaxy tools, but you can unlock the full freedom in your data analysis. You can install your favourite single-cell tool suite that is not available on Galaxy, export your data into these coding environments and run your analysis there. If you want your favourite tool suite as a Galaxy tool, you can always request here. Let’s start with the basics of running these environments in Galaxy.
Time estimation: 4 hours 30 minutes
Learning objectives:
- Launch JupyterLab in Galaxy
- Start a notebook
- Import libraries
- Use get() to import datasets from your history to the notebook
- Use put() to export datasets from the notebook to your history
- Save your notebook into your history
- Learn about the Jupyter Interactive Environment
- Launch RStudio in Galaxy
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