Applying single-cell RNA-seq analysis in Coding Environments

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Comment: What is a Learning Pathway?
A graphic depicting a winding path from a start symbol to a trophy, with tutorials along the way
We recommend you follow the tutorials in the order presented on this page. They have been selected to fit together and build up your knowledge step by step. If a lesson has both slides and a tutorial, we recommend you start with the slides, then proceed with the tutorial.

Gone is the pre-annotated, high quality tutorial data - now you have real, messy data to deal with. You have decisions to make and parameters to decide. This learning pathway challenges you to replicate a published analysis as if this were your own dataset. You will perform this analysis in coding environments hosted on Galaxy, instead of Galaxy’s button-based tool interface.

The data is messy. The decisions are tough. The interpretation is meaningful. Come here to advance your single cell skills! Note that you get two options: performing the analysis predominantly in R or in Python.

For support throughout these tutorials, join our Galaxy single cell chat group on Matrix to ask questions!

Want to try scRNA-seq analysis in a coding environment? Follow this learning path!

Module 1: Coding environments in Galaxy

Let’s start with the basics of running a coding environments in Galaxy.

Lesson Slides Hands-on Recordings
JupyterLab in Galaxy
Use Jupyter notebooks in Galaxy
RStudio in Galaxy

Module 2: Preparing the dataset

These tutorials take you from raw scRNA sequencing reads to a matrix ready for downstream analysis. Galaxy coding environments don’t have the same level of computational power as the easy-to-use Galaxy tools, so in practice, dataset preparation is usually performed in the Galaxy user interface to process the dataset into something smaller, which can then be analysed in the coding environment. Nevertheless, the whole process can be performed in a coding environment.

Lesson Slides Hands-on Recordings
Generating a single cell matrix using Alevin and combining datasets (bash + R)

Module 3: Generating cluster plots

These tutorials take you from the pre-processed matrix to cluster plots and gene expression values. You can pick whether to follow the Python (Scanpy) or R (Seurat) tutorial.

Lesson Slides Hands-on Recordings
Filter, plot and explore single-cell RNA-seq data with Scanpy (Python)
Filter, plot, and explore single cell RNA-seq data with Seurat (R)

Module 4: Inferring trajectories

This 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 can choose whether to follow the Python (Scanpy) or R (Monocle) tutorial.

Lesson Slides Hands-on Recordings
Inferring single cell trajectories with Scanpy (Python)
Inferring single cell trajectories with Monocle3 (R)

The End!

And now you’re done! You will find more features, tips and tricks in our general Galaxy Single-cell Training page.


Editorial Board

This material is reviewed by our Editorial Board:

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