Pseudobulk Analysis with Decoupler and edgeR #5617
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added single-cell tag: "tags": ["single-cell"]
Edited Tags in Workflow file
Added edgeR explanation for performing DE in pseudobulk aggregates.
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The failing test is because of:
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Thanks a lot @dianichj! This looks good, just a couple broken boxes and formatting tweaks (see below)
Just out of curiosity, are you all set up to get a local preview of your tutorial? This can also be done online using CodeSpaces now (see tutotrial)
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| In this tutorial, we will guide you through a pseudobulk analysis workflow using the **Decoupler** and **edgeR** tools available in Galaxy ({% cite Badia-iMompel2022 %}) ({% cite Liu2015 %}). These tools facilitate functional and differential expression analysis, and their output can be integrated with other Galaxy tools to visualize results, such as creating Volcano Plots, which we will also cover in this tutorial. | ||
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| > <agenda-title>Pseudobulk Analysis Pipeline Agenda</agenda-title> |
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the agenda will be automatically generated based on your section titles, pleas replace this with
> <agenda-title></agenda-title>
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> In this tutorial, we will cover:
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> 1. TOC
> {:toc}
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{: .agenda}
| > 1. What are the output(s) of the edgeR tool? | ||
| > 2. How can we interpret our output result file? | ||
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| > <solution-title>edgeR Outputs and Interpretation</solution-title> |
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since these boxes are nested, make sure to add a > in front of this line and every line below until the end of the solution box
| # Key Takeaways and Recommendations | ||
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| ## Key Takeaways | ||
| - **Pseudobulk Analysis Advantage:** Pseudobulk analysis bridges single-cell and bulk RNA-seq approaches, combining high resolution with statistical robustness. |
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anything you add in key_points metadata will be added in a box at the end of the tutorial, so you could consider moving these there.
Thanks a lot for all your comments, Saskia! I will check it again locally after going over all your revisions and will ping you with a comment. 😊 |
Co-authored-by: Saskia Hiltemann <[email protected]>
Co-authored-by: Saskia Hiltemann <[email protected]>
Co-authored-by: Saskia Hiltemann <[email protected]>
Co-authored-by: Saskia Hiltemann <[email protected]>
Co-authored-by: Saskia Hiltemann <[email protected]>
I did not know this Saskia. Thanks for mentioning it! I was following this. I noticed the linked you mentioned here is available here but I think the link in the README is the one that a new user would check. Thanks. |
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@dadrasarmin that is a very good point, I tend to forget about the README file, I will update that! |
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Hi @MarisaJL , here is the tutorial. Thanks lots for your help <3 ! |
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| Beyond enhancing statistical validity, pseudobulk analysis enables the identification of cell-type-specific gene expression and functional changes across biological conditions. It balances the detailed resolution of single-cell data with the statistical power of bulk RNA-seq, providing insights into the functional transcriptomic landscape relevant to biological questions. Overall, for differential expression analysis in multi-sample single-cell experiments, pseudobulk approaches demonstrate superior performance compared to single-cell-specific DE methods ({% cite Squair2021 %}). | ||
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| In this tutorial, we will guide you through a pseudobulk analysis workflow using the **Decoupler** and **edgeR** tools available in Galaxy ({% cite Badia-iMompel2022 %}) ({% cite Liu2015 %}). These tools facilitate functional and differential expression analysis, and their output can be integrated with other Galaxy tools to visualize results, such as creating Volcano Plots, which we will also cover in this tutorial. |
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| In this tutorial, we will guide you through a pseudobulk analysis workflow using the **Decoupler** and **edgeR** tools available in Galaxy ({% cite Badia-iMompel2022 %}) ({% cite Liu2015 %}). These tools facilitate functional and differential expression analysis, and their output can be integrated with other Galaxy tools to visualize results, such as creating Volcano Plots, which we will also cover in this tutorial. | |
| In this tutorial, we will guide you through a pseudobulk analysis workflow using the **Decoupler** ({% cite Badia-iMompel2022 %}) and **edgeR** ({% cite Liu2015 %}) tools available in Galaxy. These tools facilitate functional and differential expression analysis, and their output can be integrated with other Galaxy tools to visualize results, such as creating Volcano Plots, which we will also cover in this tutorial. |
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why not citing the original edgeR paper from 2009? https://doi.org/10.1093/bioinformatics/btp616
| key_points: | ||
| - Pseudobulk analysis approach bridges the gap between single-cell and bulk RNA-seq data | ||
| - Decoupler tool generates a pseudobulk count matrix, enabling downstream differential expression and functional analyses | ||
| - edgeR is a robust tool for differential expression in pseudobulk datasets |
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please add pbmc and combining sc datasets tutorials as requirements here
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| > <hands-on-title> Decoupler Pseudobulk </hands-on-title> | ||
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| > 1. {% tool [Decoupler pseudo-bulk](toolshed.g2.bx.psu.edu/repos/ebi-gxa/decoupler_pseudobulk/decoupler_pseudobulk/1.4.0+galaxy5) %} tool with the following parameters: |
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can you instead use the latest version (+galaxy8) of the tool?
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Will check if it is working =D
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| > <hands-on-title> Use Manipulate AnnData Tools to extract observations </hands-on-title> | ||
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| > 1. Use the {% tool [Manipulate AnnData](toolshed.g2.bx.psu.edu/repos/iuc/anndata_manipulate/anndata_manipulate/0.10.9+galaxy0) %} tool with the following parameters: |
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In the latest version, this function does not exist anymore. We moved this function to Scanpy Filter tool. Please adjust this hands-on step accordingly and also update the workflow.
| > - *"Value"*: `T cell` (the name of the cluster of interest for subset analysis) | ||
| {: .hands_on} | ||
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| After using the {% tool [Manipulate AnnData](toolshed.g2.bx.psu.edu/repos/iuc/anndata_manipulate/anndata_manipulate/0.10.9+galaxy0) %} tool to subset the cell type of interest, go back to the top of this tutorial to the hands-on **Pseudobulk with Decoupler** step, and you may perform once again the same steps in this smaller AnnData object that now should only include your T cells. Results from this analysis will correspond to differential expression between conditions only for T cells. |
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Also, replace this with Scanpy Filter
| {: .hands_on} | ||
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| After using the {% tool [Manipulate AnnData](toolshed.g2.bx.psu.edu/repos/iuc/anndata_manipulate/anndata_manipulate/0.10.9+galaxy0) %} tool to subset the cell type of interest, go back to the top of this tutorial to the hands-on **Pseudobulk with Decoupler** step, and you may perform once again the same steps in this smaller AnnData object that now should only include your T cells. Results from this analysis will correspond to differential expression between conditions only for T cells. | ||
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please include the results (plots and some questions with number of genes etc.) of the T cell subsampled data so that the users can compare results.
| layout: tutorial_hands_on | ||
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| title: Pseudobulk Analysis with Decoupler and EdgeR | ||
| zenodo_link: https://zenodo.org/records/13929549 |
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please include answer_histories:
Co-authored-by: Pavankumar Videm <[email protected]>
Co-authored-by: Pavankumar Videm <[email protected]>
Co-authored-by: Pavankumar Videm <[email protected]>
Co-authored-by: Pavankumar Videm <[email protected]>
Co-authored-by: Pavankumar Videm <[email protected]>
🚀 Pseudobulk Analysis with Decoupler and edgeR
Summary
This tutorial introduces pseudobulk analysis using Decoupler and edgeR in Galaxy 🌌. It covers data preparation, generating pseudobulk expression matrices, and performing differential expression analysis, including a Volcano Plot for final visualization of results 🌋📊.
🔗 Zenodo Link
https://zenodo.org/records/13929549
🎯 Objectives
✨ Key Points
📋 Pending Items
💡 Feel free to review and share your feedback—your input is much appreciated! 🙌